paper_id string | title string | paper_url string | pdf_url string | authors list | abstract large_string | track string | primary_area string | doi string | volume string | issue string | pages string | abstract_source string | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
10.1609/aaai.v31i1.10916 | The Option-Critic Architecture | https://ojs.aaai.org/index.php/AAAI/article/view/10916 | https://ojs.aaai.org/index.php/AAAI/article/download/10916/10775 | [
"Pierre-Luc Bacon",
"Jean Harb",
"Doina Precup"
] | Temporal abstraction is key to scaling up learning and planning in reinforcement learning. While planning with temporally extended actions is well understood, creating such abstractions autonomously from data has remained challenging.We tackle this problem in the framework of options [Sutton,Precup and Singh, 1999; Pre... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10916 | 31 | 1 | null | official | 1609.05140 | title_snapshot |
10.1609/aaai.v31i1.10917 | Resource Constrained Structured Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/10917 | https://ojs.aaai.org/index.php/AAAI/article/download/10917/10776 | [
"Tolga Bolukbasi",
"Kai-Wei Chang",
"Joseph Wang",
"Venkatesh Saligrama"
] | We study the problem of structured prediction under test-time budget constraints. We propose a novel approach based on selectively acquiring computationally costly features during test-time in order to reduce the computational cost of pre- diction with minimal performance degradation. We formulate a novel empirical ris... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10917 | 31 | 1 | null | official | 1602.08761 | title_snapshot |
10.1609/aaai.v31i1.10914 | Scalable Algorithm for Higher-Order Co-Clustering via Random Sampling | https://ojs.aaai.org/index.php/AAAI/article/view/10914 | https://ojs.aaai.org/index.php/AAAI/article/download/10914/10773 | [
"Daisuke Hatano",
"Takuro Fukunaga",
"Takanori Maehara",
"Ken-ichi Kawarabayashi"
] | We propose a scalable and efficient algorithm for coclustering a higher-order tensor. Viewing tensors with hypergraphs, we propose formulating the co-clustering of a tensor as a problem of partitioning the corresponding hypergraph. Our algorithm is based on the random sampling technique, which has been successfully app... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10914 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10915 | Approximate Conditional Gradient Descent on Multi-Class Classification | https://ojs.aaai.org/index.php/AAAI/article/view/10915 | https://ojs.aaai.org/index.php/AAAI/article/download/10915/10774 | [
"Zhuanghua Liu",
"Ivor Tsang"
] | Conditional gradient descent, aka the Frank-Wolfe algorithm,regains popularity in recent years. The key advantage of Frank-Wolfe is that at each step the expensive projection is replaced with a much more efficient linear optimization step. Similar to gradient descent, the loss function of Frank-Wolfe scales with the da... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10915 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10912 | Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features | https://ojs.aaai.org/index.php/AAAI/article/view/10912 | https://ojs.aaai.org/index.php/AAAI/article/download/10912/10771 | [
"Zihao Chen",
"Luo Luo",
"Zhihua Zhang"
] | Recently, there has been an increasing interest in designing distributed convex optimization algorithms under the setting where the data matrix is partitioned on features. Algorithms under this setting sometimes have many advantages over those under the setting where data is partitioned on samples, especially when the ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10912 | 31 | 1 | null | official | 1612.00599 | title_snapshot |
10.1609/aaai.v31i1.10913 | When and Why Are Deep Networks Better Than Shallow Ones? | https://ojs.aaai.org/index.php/AAAI/article/view/10913 | https://ojs.aaai.org/index.php/AAAI/article/download/10913/10772 | [
"Hrushikesh Mhaskar",
"Qianli Liao",
"Tomaso Poggio"
] | While the universal approximation property holds both for hierarchical and shallow networks, deep networks can approximate the class of compositional functions as well as shallow networks but with exponentially lower number of training parameters and sample complexity. Compositional functions are obtained as a hierarch... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10913 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10911 | Policy Search with High-Dimensional Context Variables | https://ojs.aaai.org/index.php/AAAI/article/view/10911 | https://ojs.aaai.org/index.php/AAAI/article/download/10911/10770 | [
"Voot Tangkaratt",
"Herke Van Hoof",
"Simone Parisi",
"Gerhard Neumann",
"Jan Peters",
"Masashi Sugiyama"
] | Direct contextual policy search methods learn to improve policy parameters and simultaneously generalize these parameters to different context or task variables. However, learning from high-dimensional context variables, such as camera images, is still a prominent problem in many real-world tasks. A naive application o... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10911 | 31 | 1 | null | official | 1611.03231 | title_snapshot |
10.1609/aaai.v31i1.10910 | Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction | https://ojs.aaai.org/index.php/AAAI/article/view/10910 | https://ojs.aaai.org/index.php/AAAI/article/download/10910/10769 | [
"Qi Meng",
"Wei Chen",
"Jingcheng Yu",
"Taifeng Wang",
"Zhi-Ming Ma",
"Tie-Yan Liu"
] | Regularized empirical risk minimization (R-ERM) is an important branch of machine learning, since it constrains the capacity of the hypothesis space and guarantees the generalization ability of the learning algorithm. Two classic proximal optimization algorithms, i.e., proximal stochastic gradient descent (ProxSGD) and... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10910 | 31 | 1 | null | official | 1609.08435 | title_snapshot |
10.1609/aaai.v31i1.10918 | Dynamic Action Repetition for Deep Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10918 | https://ojs.aaai.org/index.php/AAAI/article/download/10918/10777 | [
"Aravind Lakshminarayanan",
"Sahil Sharma",
"Balaraman Ravindran"
] | One of the long standing goals of Artificial Intelligence (AI) is to build cognitive agents which can perform complex tasks from raw sensory inputs without explicit supervision. Recent progress in combining Reinforcement Learning objective functions and Deep Learning architectures has achieved promising results for suc... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10918 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10919 | Generalization Error Bounds for Optimization Algorithms via Stability | https://ojs.aaai.org/index.php/AAAI/article/view/10919 | https://ojs.aaai.org/index.php/AAAI/article/download/10919/10778 | [
"Qi Meng",
"Yue Wang",
"Wei Chen",
"Taifeng Wang",
"Zhi-Ming Ma",
"Tie-Yan Liu"
] | Many machine learning tasks can be formulated as Regularized Empirical Risk Minimization (R-ERM), and solved by optimization algorithms such as gradient descent (GD), stochastic gradient descent (SGD), and stochastic variance reduction (SVRG). Conventional analysis on these optimization algorithms focuses on their conv... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10919 | 31 | 1 | null | official | 1609.08397 | title_snapshot |
10.1609/aaai.v31i1.10903 | Modeling Skewed Class Distributions by Reshaping the Concept Space | https://ojs.aaai.org/index.php/AAAI/article/view/10903 | https://ojs.aaai.org/index.php/AAAI/article/download/10903/10762 | [
"Kyle Feuz",
"Diane Cook"
] | We introduce an approach to learning from imbalanced class distributions that does not change the underlying data distribution. The ICC algorithm decomposes majority classes into smaller sub-classes that create a more balanced class distribution. In this paper, we explain how ICC can not only addressthe class imbalance... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10903 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10905 | A General Clustering Agreement Index: For Comparing Disjoint and Overlapping Clusters | https://ojs.aaai.org/index.php/AAAI/article/view/10905 | https://ojs.aaai.org/index.php/AAAI/article/download/10905/10764 | [
"Reihaneh Rabbany",
"Osmar Zaïane"
] | A clustering agreement index quantifies the similarity between two given clusterings. It is most commonly used to compare the results obtained from different clustering algorithms against the ground-truth clustering in the benchmark datasets. In this paper, we present a general Clustering Agreement Index (CAI) for comp... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10905 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10906 | Ordinal Constrained Binary Code Learning for Nearest Neighbor Search | https://ojs.aaai.org/index.php/AAAI/article/view/10906 | https://ojs.aaai.org/index.php/AAAI/article/download/10906/10765 | [
"Hong Liu",
"Rongrong Ji",
"Yongjian Wu",
"Feiyue Huang"
] | Recent years have witnessed extensive attention in binary code learning, a.k.a. hashing, for nearest neighbor search problems. It has been seen that high-dimensional data points can quantize into binary codes to give an efficient similarity approximation via Hamming distance. Among the existing schemes, ranking-based h... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10906 | 31 | 1 | null | official | 1611.06362 | title_snapshot |
10.1609/aaai.v31i1.10904 | Transfer Learning for Deep Learning on Graph-Structured Data | https://ojs.aaai.org/index.php/AAAI/article/view/10904 | https://ojs.aaai.org/index.php/AAAI/article/download/10904/10763 | [
"Jaekoo Lee",
"Hyunjae Kim",
"Jongsun Lee",
"Sungroh Yoon"
] | Graphs provide a powerful means for representing complex interactions between entities. Recently, new deep learning approaches have emerged for representing and modeling graph-structured data while the conventional deep learning methods, such as convolutional neural networks and recurrent neural networks, have mainly f... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10904 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10901 | Cross-Domain Kernel Induction for Transfer Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10901 | https://ojs.aaai.org/index.php/AAAI/article/download/10901/10760 | [
"Wei-Cheng Chang",
"Yuexin Wu",
"Hanxiao Liu",
"Yiming Yang"
] | The key question in transfer learning (TL) research is how to make model induction transferable across different domains. Common methods so far require source and target domains to have a shared/homogeneous feature space, or the projection of features from heterogeneous domains onto a shared space. This paper proposes ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10901 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10902 | Alternating Back-Propagation for Generator Network | https://ojs.aaai.org/index.php/AAAI/article/view/10902 | https://ojs.aaai.org/index.php/AAAI/article/download/10902/10761 | [
"Tian Han",
"Yang Lu",
"Song-Chun Zhu",
"Ying Nian Wu"
] | This paper proposes an alternating back-propagation algorithm for learning the generator network model. The model is a non-linear generalization of factor analysis. In this model, the mapping from the continuous latent factors to the observed signal is parametrized by a convolutional neural network. The alternating bac... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10902 | 31 | 1 | null | official | 1606.08571 | title_snapshot |
10.1609/aaai.v31i1.10900 | A Two-Stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/10900 | https://ojs.aaai.org/index.php/AAAI/article/download/10900/10759 | [
"Zhe Li",
"Tianbao Yang",
"Lijun Zhang",
"Rong Jin"
] | This paper aims to provide a sharp excess risk guarantee for learning a sparse linear model without any assumptions about the strong convexity of the expected loss and the sparsity of the optimal solution in hindsight. Given a target level ε for the excess risk, an interesting question to ask is how many examples and h... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10900 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10909 | Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours | https://ojs.aaai.org/index.php/AAAI/article/view/10909 | https://ojs.aaai.org/index.php/AAAI/article/download/10909/10768 | [
"Feiping Nie",
"Guohao Cai",
"Xuelong Li"
] | Due to the efficiency of learning relationships and complex structures hidden in data, graph-oriented methods have been widely investigated and achieve promising performance in multi-view learning. Generally, these learning algorithms construct informative graph for each view or fuse different views to one graph, on wh... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10909 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10907 | Label Efficient Learning by Exploiting Multi-Class Output Codes | https://ojs.aaai.org/index.php/AAAI/article/view/10907 | https://ojs.aaai.org/index.php/AAAI/article/download/10907/10766 | [
"Maria Balcan",
"Travis Dick",
"Yishay Mansour"
] | We present a new perspective on the popular multi-class algorithmic techniques of one-vs-all and error correcting output codes. Rather than studying the behavior of these techniques for supervised learning, we establish a connection between the success of these methods and the existence of label-efficient learning proc... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10907 | 31 | 1 | null | official | 1511.03225 | title_snapshot |
10.1609/aaai.v31i1.10908 | Addressing Imbalance in Multi-Label Classification Using Structured Hellinger Forests | https://ojs.aaai.org/index.php/AAAI/article/view/10908 | https://ojs.aaai.org/index.php/AAAI/article/download/10908/10767 | [
"Zachary Daniels",
"Dimitris Metaxas"
] | The multi-label classification problem involves finding a model that maps a set of input features to more than one output label. Class imbalance is a serious issue in multi-label classification. We introduce an extension of structured forests, a type of random forest used for structured prediction, called Sparse Obliqu... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10908 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10790 | Beyond RPCA: Flattening Complex Noise in the Frequency Domain | https://ojs.aaai.org/index.php/AAAI/article/view/10790 | https://ojs.aaai.org/index.php/AAAI/article/download/10790/10649 | [
"Yunhe Wang",
"Chang Xu",
"Chao Xu",
"Dacheng Tao"
] | Discovering robust low-rank data representations is important in many real-world problems. Traditional robust principal component analysis (RPCA) assumes that the observed data are corrupted by some sparse noise (e.g., Laplacian noise) and utilizes the l1-norm to separate out the noisy compo- nent. Nevertheless, as wel... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10790 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10791 | Spectral Clustering with Brainstorming Process for Multi-View Data | https://ojs.aaai.org/index.php/AAAI/article/view/10791 | https://ojs.aaai.org/index.php/AAAI/article/download/10791/10650 | [
"Jeong-Woo Son",
"Junkey Jeon",
"Alex Lee",
"Sun-Joong Kim"
] | Clustering tasks often requires multiple views rather than a singleview to correctly reflect diverse characteristics of the cluster boundaries. The cluster boundaries estimated using a single view are incorrect in general, and those incorrect estimation should be compensated by helps of other views. If each viewis inde... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10791 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10798 | Two-Dimensional PCA with F-Norm Minimization | https://ojs.aaai.org/index.php/AAAI/article/view/10798 | https://ojs.aaai.org/index.php/AAAI/article/download/10798/10657 | [
"Qianqian Wang",
"Quanxue Gao"
] | Two-dimensional principle component analysis (2DPCA) has been widely used for face image representation and recognition. But it is sensitive to the presence of outliers. To alleviate this problem, we propose a novel robust 2DPCA, namely 2DPCA with F-norm minimization (F-2DPCA), which is intuitive and directly derived f... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10798 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10799 | Classification with Minimax Distance Measures | https://ojs.aaai.org/index.php/AAAI/article/view/10799 | https://ojs.aaai.org/index.php/AAAI/article/download/10799/10658 | [
"Morteza Haghir Chehreghani"
] | Minimax distance measures provide an effective way to capture the unknown underlying patterns and classes of the data in a non-parametric way. We develop a general-purpose framework to employ Minimax distances with any classification method that performs on numerical data. For this purpose, we establish a two-step stra... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10799 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10796 | Transfer Reinforcement Learning with Shared Dynamics | https://ojs.aaai.org/index.php/AAAI/article/view/10796 | https://ojs.aaai.org/index.php/AAAI/article/download/10796/10655 | [
"Romain Laroche",
"Merwan Barlier"
] | This article addresses a particular Transfer Reinforcement Learning (RL) problem: when dynamics do not change from one task to another, and only the reward function does. Our method relies on two ideas, the first one is that transition samples obtained from a task can be reused to learn on any other task: an immediate ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10796 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10797 | Unimodal Thompson Sampling for Graph-Structured Arms | https://ojs.aaai.org/index.php/AAAI/article/view/10797 | https://ojs.aaai.org/index.php/AAAI/article/download/10797/10656 | [
"Stefano Paladino",
"Francesco Trovò",
"Marcello Restelli",
"Nicola Gatti"
] | We study, to the best of our knowledge, the first Bayesian algorithm for unimodal Multi-Armed Bandit (MAB) problems with graph structure. In this setting, each arm corresponds to a node of a graph and each edge provides a relationship, unknown to the learner, between two nodes in terms of expected reward. Furthermore, ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10797 | 31 | 1 | null | official | 1611.05724 | title_snapshot |
10.1609/aaai.v31i1.10794 | Rank Ordering Constraints Elimination with Application for Kernel Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10794 | https://ojs.aaai.org/index.php/AAAI/article/download/10794/10653 | [
"Ying Xie",
"Chris Ding",
"Yihong Gong",
"Zongze Wu"
] | A number of machine learning domains,such as information retrieval, recommender systems, kernel learning, neural network-biological systems etc,deal with importance scores. Very often, there existsome prior knowledge that could help improve the performance.In many cases, these prior knowledge manifest themselves in the... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10794 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10795 | An Exact Penalty Method for Binary Optimization Based on MPEC Formulation | https://ojs.aaai.org/index.php/AAAI/article/view/10795 | https://ojs.aaai.org/index.php/AAAI/article/download/10795/10654 | [
"Ganzhao Yuan",
"Bernard Ghanem"
] | Binary optimization is a central problem in mathematical optimization and its applications are abundant. To solve this problem, we propose a new class of continuous optimization techniques, which is based on Mathematical Programming with Equilibrium Constraints (MPECs). We first reformulate the binary program as an equ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10795 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10792 | Latent Smooth Skeleton Embedding | https://ojs.aaai.org/index.php/AAAI/article/view/10792 | https://ojs.aaai.org/index.php/AAAI/article/download/10792/10651 | [
"Li Wang",
"Qi Mao",
"Ivor Tsang"
] | Learning a smooth skeleton in a low-dimensional space from noisy data becomes important in computer vision and computational biology. Existing methods assume that the manifold constructed from the data is smooth, but they lack the ability to model skeleton structures from noisy data. To overcome this issue, we propose ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10792 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10793 | Enumerate Lasso Solutions for Feature Selection | https://ojs.aaai.org/index.php/AAAI/article/view/10793 | https://ojs.aaai.org/index.php/AAAI/article/download/10793/10652 | [
"Satoshi Hara",
"Takanori Maehara"
] | We propose an algorithm for enumerating solutions to the Lasso regression problem.In ordinary Lasso regression, one global optimum is obtained and the resulting features are interpreted as task-relevant features.However, this can overlook possibly relevant features not selected by the Lasso.With the proposed method, we... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10793 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10780 | One-Step Spectral Clustering via Dynamically Learning Affinity Matrix and Subspace | https://ojs.aaai.org/index.php/AAAI/article/view/10780 | https://ojs.aaai.org/index.php/AAAI/article/download/10780/10639 | [
"Xiaofeng Zhu",
"Wei He",
"Yonggang Li",
"Yang Yang",
"Shichao Zhang",
"Rongyao Hu",
"Yonghua Zhu"
] | This paper proposes a one-step spectral clustering method by learning an intrinsic affinity matrix (i.e., the clustering result) from the low-dimensional space (i.e., intrinsic subspace) of original data. Specifically, the intrinsic affinitymatrix is learnt by: 1) the alignment of the initial affinity matrix learnt fro... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10780 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10787 | Regularization for Unsupervised Deep Neural Nets | https://ojs.aaai.org/index.php/AAAI/article/view/10787 | https://ojs.aaai.org/index.php/AAAI/article/download/10787/10646 | [
"Baiyang Wang",
"Diego Klabjan"
] | Unsupervised neural networks, such as restricted Boltzmann machines (RBMs) and deep belief networks (DBNs), are powerful tools for feature selection and pattern recognition tasks. We demonstrate that overfitting occurs in such models just as in deep feedforward neural networks, and discuss possible regularization metho... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10787 | 31 | 1 | null | official | 1608.04426 | title_snapshot |
10.1609/aaai.v31i1.10788 | Convex Co-Embedding for Matrix Completion with Predictive Side Information | https://ojs.aaai.org/index.php/AAAI/article/view/10788 | https://ojs.aaai.org/index.php/AAAI/article/download/10788/10647 | [
"Yuhong Guo"
] | Matrix completion as a common problem in many application domains has received increasing attention in the machine learning community. Previous matrix completion methods have mostly focused on exploiting the matrix low-rank property to recover missing entries. Recently, it has been noticed that side information that de... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10788 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10785 | Improving Efficiency of SVM k -Fold Cross-Validation by Alpha Seeding | https://ojs.aaai.org/index.php/AAAI/article/view/10785 | https://ojs.aaai.org/index.php/AAAI/article/download/10785/10644 | [
"Zeyi Wen",
"Bin Li",
"Ramamohanarao Kotagiri",
"Jian Chen",
"Yawen Chen",
"Rui Zhang"
] | The k-fold cross-validation is commonly used to evaluate the effectiveness of SVMs with the selected hyper-parameters. It is known that the SVM k-fold cross-validation is expensive, since it requires training k SVMs. However, little work has explored reusing the h-th SVM for training the (h+1)-th SVM for improving the ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10785 | 31 | 1 | null | official | 1611.07659 | title_snapshot |
10.1609/aaai.v31i1.10786 | Learning Invariant Deep Representation for NIR-VIS Face Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/10786 | https://ojs.aaai.org/index.php/AAAI/article/download/10786/10645 | [
"Ran He",
"Xiang Wu",
"Zhenan Sun",
"Tieniu Tan"
] | Visual versus near infrared (VIS-NIR) face recognition is still a challenging heterogeneous task due to large appearance difference between VIS and NIR modalities. This paper presents a deep convolutional network approach that uses only one network to map both NIR and VIS images to a compact Euclidean space. The low-le... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10786 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10783 | Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting | https://ojs.aaai.org/index.php/AAAI/article/view/10783 | https://ojs.aaai.org/index.php/AAAI/article/download/10783/10642 | [
"Lin Chen",
"Hamed Hassani",
"Amin Karbasi"
] | In this paper, we consider the problem of actively learning a linear classifier through query synthesis where the learner can construct artificial queries in order to estimate the true decision boundaries. This problem has recently gained a lot of interest in automated science and adversarial reverse engineering for wh... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10783 | 31 | 1 | null | official | 1603.03515 | title_snapshot |
10.1609/aaai.v31i1.10784 | Generalization Analysis for Ranking Using Integral Operator | https://ojs.aaai.org/index.php/AAAI/article/view/10784 | https://ojs.aaai.org/index.php/AAAI/article/download/10784/10643 | [
"Yong Liu",
"Shizhong Liao",
"Hailun Lin",
"Yinliang Yue",
"Weiping Wang"
] | The study on generalization performance of ranking algorithms is one of the fundamental issues in ranking learning theory. Although several generalization bounds have been proposed based on different measures, the convergence rates of the existing bounds are usually at most O(√1/n), where n is the size of data set. In ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10784 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10781 | Parametric Dual Maximization for Non-Convex Learning Problems | https://ojs.aaai.org/index.php/AAAI/article/view/10781 | https://ojs.aaai.org/index.php/AAAI/article/download/10781/10640 | [
"Yuxun Zhou",
"Zhaoyi Kang",
"Costas Spanos"
] | We consider a class of non-convex learning problems that can be formulated as jointly optimizing regularized hinge loss and a set of auxiliary variables. Such problems encompass but are not limited to various versions of semi-supervised learning,learning with hidden structures, robust learning, etc. Existing methods ei... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10781 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10782 | Sparse Subspace Clustering by Learning Approximation ℓ0 Codes | https://ojs.aaai.org/index.php/AAAI/article/view/10782 | https://ojs.aaai.org/index.php/AAAI/article/download/10782/10641 | [
"Jun Li",
"Yu Kong",
"Yun Fu"
] | Subspace clustering has been widely applied to detect meaningful clusters in high-dimensional data spaces. A main challenge in subspace clustering is to quickly calculate a "good" affinity matrix. ℓ0, ℓ1, ℓ2 or nuclear norm regularization is used to construct the affinity matrix in many subspace clustering methods beca... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10782 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10789 | Structure Regularized Unsupervised Discriminant Feature Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/10789 | https://ojs.aaai.org/index.php/AAAI/article/download/10789/10648 | [
"Mingyu Fan",
"Xiaojun Chang",
"Dacheng Tao"
] | Feature selection is an important technique in machine learning research. An effective and robust feature selection method is desired to simultaneously identify the informative features and eliminate the noisy ones of data. In this paper, we consider the unsupervised feature selection problem which is particularly diff... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10789 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10776 | Low-Rank Tensor Completion with Total Variation for Visual Data Inpainting | https://ojs.aaai.org/index.php/AAAI/article/view/10776 | https://ojs.aaai.org/index.php/AAAI/article/download/10776/10635 | [
"Xutao Li",
"Yunming Ye",
"Xiaofei Xu"
] | With the advance of acquisition techniques, plentiful higherorder tensor data sets are built up in a great variety of fields such as computer vision, neuroscience, remote sensing and recommender systems. The real-world tensors often contain missing values, which makes tensor completion become a prerequisite to utilize ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10776 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10777 | Denoising Criterion for Variational Auto-Encoding Framework | https://ojs.aaai.org/index.php/AAAI/article/view/10777 | https://ojs.aaai.org/index.php/AAAI/article/download/10777/10636 | [
"Daniel Im Im",
"Sungjin Ahn",
"Roland Memisevic",
"Yoshua Bengio"
] | Denoising autoencoders (DAE) are trained to reconstruct their clean inputs with noise injected at the input level, while variational autoencoders (VAE) are trained with noise injected in their stochastic hidden layer, with a regularizer that encourages this noise injection. In this paper, we show that injecting noise b... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10777 | 31 | 1 | null | official | 1511.06406 | title_snapshot |
10.1609/aaai.v31i1.10774 | Exploring Commonality and Individuality for Multi-Modal Curriculum Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10774 | https://ojs.aaai.org/index.php/AAAI/article/download/10774/10633 | [
"Chen Gong"
] | Curriculum Learning (CL) mimics the cognitive process ofhumans and favors a learning algorithm to follow the logical learning sequence from simple examples to more difficult ones. Recent studies show that selecting the simplest curriculum examples from different modalities for graph-based label propagation can yield be... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10774 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10775 | Confidence-Rated Discriminative Partial Label Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10775 | https://ojs.aaai.org/index.php/AAAI/article/download/10775/10634 | [
"Cai-Zhi Tang",
"Min-Ling Zhang"
] | Partial label learning aims to induce a multi-class classifier from training examples where each of them is associated with a set of candidate labels, among which only one label is valid. The common discriminative solution to learn from partial label examples assumes one parametric model for each class label, whose pre... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10775 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10772 | A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression | https://ojs.aaai.org/index.php/AAAI/article/view/10772 | https://ojs.aaai.org/index.php/AAAI/article/download/10772/10631 | [
"Quang Minh Hoang",
"Trong Nghia Hoang",
"Kian Hsiang Low"
] | While much research effort has been dedicated to scaling up sparse Gaussian process (GP) models based on inducing variables for big data, little attention is afforded to the other less explored class of low-rank GP approximations that exploit the sparse spectral representation of a GP kernel. This paper presents such a... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10772 | 31 | 1 | null | official | 1611.06080 | title_snapshot |
10.1609/aaai.v31i1.10773 | Sparse Boltzmann Machines with Structure Learning as Applied to Text Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/10773 | https://ojs.aaai.org/index.php/AAAI/article/download/10773/10632 | [
"Zhourong Chen",
"Nevin Zhang",
"Dit-Yan Yeung",
"Peixian Chen"
] | We are interested in exploring the possibility and benefits of structure learning for deep models. As the first step, this paper investigates the matter for Restricted Boltzmann Machines (RBMs). We conduct the study with Replicated Softmax, a variant of RBMs for unsupervised text analysis. We present a method for learn... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10773 | 31 | 1 | null | official | 1609.05294 | title_snapshot |
10.1609/aaai.v31i1.10770 | Cost-Sensitive Feature Selection via F-Measure Optimization Reduction | https://ojs.aaai.org/index.php/AAAI/article/view/10770 | https://ojs.aaai.org/index.php/AAAI/article/download/10770/10629 | [
"Meng Liu",
"Chang Xu",
"Yong Luo",
"Chao Xu",
"Yonggang Wen",
"Dacheng Tao"
] | Feature selection aims to select a small subset from the high-dimensional features which can lead to better learning performance, lower computational complexity, and better model readability. The class imbalance problem has been neglected by traditional feature selection methods, therefore the selected features will be... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10770 | 31 | 1 | null | official | 1904.02301 | title_judge |
10.1609/aaai.v31i1.10771 | Infinite Kernel Learning: Generalization Bounds and Algorithms | https://ojs.aaai.org/index.php/AAAI/article/view/10771 | https://ojs.aaai.org/index.php/AAAI/article/download/10771/10630 | [
"Yong Liu",
"Shizhong Liao",
"Hailun Lin",
"Yinliang Yue",
"Weiping Wang"
] | Kernel learning is a fundamental problem both in recent research and application of kernel methods. Existing kernel learning methods commonly use some measures of generalization errors to learn the optimal kernel in a convex (or conic) combination of prescribed basic kernels. However, the generalization bounds derived ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10771 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10778 | Unbiased Multivariate Correlation Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/10778 | https://ojs.aaai.org/index.php/AAAI/article/download/10778/10637 | [
"Yisen Wang",
"Simone Romano",
"Vinh Nguyen",
"James Bailey",
"Xingjun Ma",
"Shu-Tao Xia"
] | Correlation measures are a key element of statistics and machine learning, and essential for a wide range of data analysis tasks. Most existing correlation measures are for pairwise relationships, but real-world data can also exhibit complex multivariate correlations, involving three or more variables. We argue that mu... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10778 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10779 | Structured Inference Networks for Nonlinear State Space Models | https://ojs.aaai.org/index.php/AAAI/article/view/10779 | https://ojs.aaai.org/index.php/AAAI/article/download/10779/10638 | [
"Rahul Krishnan",
"Uri Shalit",
"David Sontag"
] | Gaussian state space models have been used for decades as generative models of sequential data. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption. We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space mod... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10779 | 31 | 1 | null | official | 1609.09869 | title_snapshot |
10.1609/aaai.v31i1.10769 | Learning Deep Latent Space for Multi-Label Classification | https://ojs.aaai.org/index.php/AAAI/article/view/10769 | https://ojs.aaai.org/index.php/AAAI/article/download/10769/10628 | [
"Chih-Kuan Yeh",
"Wei-Chieh Wu",
"Wei-Jen Ko",
"Yu-Chiang Frank Wang"
] | Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming a... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10769 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10768 | Universum Prescription: Regularization Using Unlabeled Data | https://ojs.aaai.org/index.php/AAAI/article/view/10768 | https://ojs.aaai.org/index.php/AAAI/article/download/10768/10627 | [
"Xiang Zhang",
"Yann LeCun"
] | This paper shows that simply prescribing "none of the above" labels to unlabeled data has a beneficial regularization effect to supervised learning. We call it universum prescription by the fact that the prescribed labels cannot be one of the supervised labels. In spite of its simplicity, universum prescription obtaine... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10768 | 31 | 1 | null | official | 1511.03719 | title_snapshot |
10.1609/aaai.v31i1.10820 | Learning Sparse Task Relations in Multi-Task Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10820 | https://ojs.aaai.org/index.php/AAAI/article/download/10820/10679 | [
"Yu Zhang",
"Qiang Yang"
] | In multi-task learning, when the number of tasks is large, pairwise task relations exhibit sparse patterns since usually a task cannot be helpful to all of the other tasks and moreover, sparse task relations can reduce the risk of overfitting compared with the dense ones. In this paper, we focus on learning sparse task... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10820 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10821 | Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration | https://ojs.aaai.org/index.php/AAAI/article/view/10821 | https://ojs.aaai.org/index.php/AAAI/article/download/10821/10680 | [
"Himabindu Lakkaraju",
"Ece Kamar",
"Rich Caruana",
"Eric Horvitz"
] | Predictive models deployed in the real world may assign incorrect labels to instances with high confidence. Such errors or unknown unknowns are rooted in model incompleteness, and typically arise because of the mismatch between training data and the cases encountered at test time. As the models are blind to such errors... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10821 | 31 | 1 | null | official | 1610.09064 | title_snapshot |
10.1609/aaai.v31i1.10828 | Cross-Domain Ranking via Latent Space Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10828 | https://ojs.aaai.org/index.php/AAAI/article/download/10828/10687 | [
"Jie Tang",
"Wendy Hall"
] | We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all t... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10828 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10829 | Accelerated Gradient Temporal Difference Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10829 | https://ojs.aaai.org/index.php/AAAI/article/download/10829/10688 | [
"Yangchen Pan",
"Adam White",
"Martha White"
] | The family of temporal difference (TD) methods span a spectrum from computationally frugal linear methods like TD(λ) to data efficient least squares methods. Least square methods make the best use of available data directly computing the TD solution and thus do not require tuning a typically highly sensitive learning r... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10829 | 31 | 1 | null | official | 1611.09328 | title_snapshot |
10.1609/aaai.v31i1.10827 | Playing FPS Games with Deep Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10827 | https://ojs.aaai.org/index.php/AAAI/article/download/10827/10686 | [
"Guillaume Lample",
"Devendra Singh Chaplot"
] | Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of these games take place in 2D environments that are fully observable to the agent. In this paper, we present the first archite... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10827 | 31 | 1 | null | official | 1609.05521 | title_snapshot |
10.1609/aaai.v31i1.10826 | Distant Domain Transfer Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10826 | https://ojs.aaai.org/index.php/AAAI/article/download/10826/10685 | [
"Ben Tan",
"Yu Zhang",
"Sinno Pan",
"Qiang Yang"
] | In this paper, we study a novel transfer learning problem termed Distant Domain Transfer Learning (DDTL). Different from existing transfer learning problems which assume that there is a close relation between the source domain and the target domain, in the DDTL problem, the target domain can be totally different from t... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10826 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10824 | Cascade Subspace Clustering | https://ojs.aaai.org/index.php/AAAI/article/view/10824 | https://ojs.aaai.org/index.php/AAAI/article/download/10824/10683 | [
"Xi Peng",
"Jiashi Feng",
"Jiwen Lu",
"Wei-Yun Yau",
"Zhang Yi"
] | In this paper, we recast the subspace clustering as a verification problem. Our idea comes from an assumption that the distribution between a given sample x and cluster centers Omega is invariant to different distance metrics on the manifold, where each distribution is defined as a probability map (i.e. soft-assignment... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10824 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10825 | Random Features for Shift-Invariant Kernels with Moment Matching | https://ojs.aaai.org/index.php/AAAI/article/view/10825 | https://ojs.aaai.org/index.php/AAAI/article/download/10825/10684 | [
"Weiwei Shen",
"Zhihui Yang",
"Jun Wang"
] | In order to grapple with the conundrum in the scalability of kernel-based learning algorithms, the method of approximating nonlinear kernels via random feature maps has attracted wide attention in large-scale learning systems. Specifically, the associated sampling procedure is one critical component that dictates the q... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10825 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10822 | Semi-Supervised Adaptive Label Distribution Learning for Facial Age Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/10822 | https://ojs.aaai.org/index.php/AAAI/article/download/10822/10681 | [
"Peng Hou",
"Xin Geng",
"Zeng-Wei Huo",
"Jia-Qi Lv"
] | Lack of sufficient training data with exact ages is still a challenge for facial age estimation. To deal with such problem, a method called Label Distribution Learning (LDL) was proposed to utilize the neighboring ages while learning a particular age. Later, an adaptive version of LDL called ALDL was proposed to genera... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10822 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10823 | Multi-View Correlated Feature Learning by Uncovering Shared Component | https://ojs.aaai.org/index.php/AAAI/article/view/10823 | https://ojs.aaai.org/index.php/AAAI/article/download/10823/10682 | [
"Xiaowei Xue",
"Feiping Nie",
"Sen Wang",
"Xiaojun Chang",
"Bela Stantic",
"Min Yao"
] | Learning multiple heterogeneous features from different data sources is challenging. One research topic is how to exploit and utilize the correlations among various features across multiple views with the aim of improving the performance of learning tasks, such as classification. In this paper, we propose a new multi-v... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10823 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10810 | OFFER: Off-Environment Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10810 | https://ojs.aaai.org/index.php/AAAI/article/download/10810/10669 | [
"Kamil Ciosek",
"Shimon Whiteson"
] | Policy gradient methods have been widely applied in reinforcement learning. For reasons of safety and cost, learning is often conducted using a simulator. However, learning in simulation does not traditionally utilise the opportunity to improve learning by adjusting certain environment variables - state features that a... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10810 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10817 | Bilinear Probabilistic Canonical Correlation Analysis via Hybrid Concatenations | https://ojs.aaai.org/index.php/AAAI/article/view/10817 | https://ojs.aaai.org/index.php/AAAI/article/download/10817/10676 | [
"Yang Zhou",
"Haiping Lu",
"Yiu-ming Cheung"
] | Canonical Correlation Analysis (CCA) is a classical technique for two-view correlation analysis, while Probabilistic CCA (PCCA) provides a generative and more general viewpoint for this task. Recently, PCCA has been extended to bilinear cases for dealing with two-view matrices in order to preserve and exploit the matri... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10817 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10815 | Unsupervised Learning with Truncated Gaussian Graphical Models | https://ojs.aaai.org/index.php/AAAI/article/view/10815 | https://ojs.aaai.org/index.php/AAAI/article/download/10815/10674 | [
"Qinliang Su",
"Xuejun Liao",
"Chunyuan Li",
"Zhe Gan",
"Lawrence Carin"
] | Gaussian graphical models (GGMs) are widely used for statistical modeling, because of ease of inference and the ubiquitous use of the normal distribution in practical approximations. However, they are also known for their limited modeling abilities, due to the Gaussian assumption. In this paper, we introduce a novel va... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10815 | 31 | 1 | null | official | 1611.04920 | title_snapshot |
10.1609/aaai.v31i1.10818 | Fredholm Multiple Kernel Learning for Semi-Supervised Domain Adaptation | https://ojs.aaai.org/index.php/AAAI/article/view/10818 | https://ojs.aaai.org/index.php/AAAI/article/download/10818/10677 | [
"Wei Wang",
"Hao Wang",
"Chen Zhang",
"Yang Gao"
] | As a fundamental constituent of machine learning, domain adaptation generalizes a learning model from a source domain to a different (but related) target domain. In this paper, we focus on semi-supervised domain adaptation and explicitly extend the applied range of unlabeled target samples into the combination of distr... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10818 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10816 | Riemannian Submanifold Tracking on Low-Rank Algebraic Variety | https://ojs.aaai.org/index.php/AAAI/article/view/10816 | https://ojs.aaai.org/index.php/AAAI/article/download/10816/10675 | [
"Qian Li",
"Zhichao Wang"
] | Matrix recovery aims to learn a low-rank structure from high dimensional data, which arises in numerous learning applications. As a popular heuristic to matrix recovery, convex relaxation involves iterative calling of singular value decomposition (SVD). Riemannian optimization based method can alleviate such expensive ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10816 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10813 | Top-k Hierarchical Classification | https://ojs.aaai.org/index.php/AAAI/article/view/10813 | https://ojs.aaai.org/index.php/AAAI/article/download/10813/10672 | [
"Sechan Oh"
] | This paper studies a top-k hierarchical classification problem. In top-k classification, one is allowed to make k predictions and no penalty is incurred if at least one of k predictions is correct. In hierarchical classification, classes form a structured hierarchy, and misclassification costs depend on the relation be... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10813 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10811 | Feature Selection Guided Auto-Encoder | https://ojs.aaai.org/index.php/AAAI/article/view/10811 | https://ojs.aaai.org/index.php/AAAI/article/download/10811/10670 | [
"Shuyang Wang",
"Zhengming Ding",
"Yun Fu"
] | Recently the auto-encoder and its variants have demonstrated their promising results in extracting effective features. Specifically, its basic idea of encouraging the output to be as similar as input, ensures the learned representation could faithfully reconstruct the input data. However, one problem arises that not al... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10811 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10814 | Unsupervised Large Graph Embedding | https://ojs.aaai.org/index.php/AAAI/article/view/10814 | https://ojs.aaai.org/index.php/AAAI/article/download/10814/10673 | [
"Feiping Nie",
"Wei Zhu",
"Xuelong Li"
] | There are many successful spectral based unsupervised dimensionality reduction methods, including Laplacian Eigenmap (LE), Locality Preserving Projection (LPP), Spectral Regression (SR), etc. LPP and SR are two different linear spectral based methods, however, we discover that LPP and SR are equivalent, if the symmetri... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10814 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10812 | Inductive Pairwise Ranking: Going Beyond the n log( n ) Barrier | https://ojs.aaai.org/index.php/AAAI/article/view/10812 | https://ojs.aaai.org/index.php/AAAI/article/download/10812/10671 | [
"U.N. Niranjan",
"Arun Rajkumar"
] | We study the problem of ranking a set of items from nonactively chosen pairwise preferences where each item has feature information with it. We propose and characterize a very broad class of preference matrices giving rise to the Feature Low Rank (FLR) model, which subsumes several models ranging from the classic Bradl... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10812 | 31 | 1 | null | official | 1702.02661 | title_snapshot |
10.1609/aaai.v31i1.10819 | MPGL: An Efficient Matching Pursuit Method for Generalized LASSO | https://ojs.aaai.org/index.php/AAAI/article/view/10819 | https://ojs.aaai.org/index.php/AAAI/article/download/10819/10678 | [
"Dong Gong",
"Mingkui Tan",
"Yanning Zhang",
"Anton Van den Hengel",
"Qinfeng Shi"
] | Unlike traditional LASSO enforcing sparsity on the variables, Generalized LASSO (GL) enforces sparsity on a linear transformation of the variables, gaining flexibility and success in many applications. However, many existing GL algorithms do not scale up to high-dimensional problems, and/or only work well for a specifi... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10819 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10806 | Nonlinear Dynamic Boltzmann Machines for Time-Series Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/10806 | https://ojs.aaai.org/index.php/AAAI/article/download/10806/10665 | [
"Sakyasingha Dasgupta",
"Takayuki Osogami"
] | The dynamic Boltzmann machine (DyBM) has been proposed as a stochastic generative model of multi-dimensional time series, with an exact, learning rule that maximizes the log-likelihood of a given time series. The DyBM, however, is defined only for binary valued data, without any nonlinear hidden units. Here, in our fir... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10806 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10807 | Tunable Sensitivity to Large Errors in Neural Network Training | https://ojs.aaai.org/index.php/AAAI/article/view/10807 | https://ojs.aaai.org/index.php/AAAI/article/download/10807/10666 | [
"Gil Keren",
"Sivan Sabato",
"Björn Schuller"
] | When humans learn a new concept, they might ignore examples that they cannot make sense of at first, and only later focus on such examples, when they are more useful for learning. We propose incorporating this idea of tunable sensitivity for hard examples in neural network learning, using a new generalization of the cr... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10807 | 31 | 1 | null | official | 1611.07743 | title_snapshot |
10.1609/aaai.v31i1.10804 | SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient | https://ojs.aaai.org/index.php/AAAI/article/view/10804 | https://ojs.aaai.org/index.php/AAAI/article/download/10804/10663 | [
"Lantao Yu",
"Weinan Zhang",
"Jun Wang",
"Yong Yu"
] | As a new way of training generative models, Generative Adversarial Net (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major r... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10804 | 31 | 1 | null | official | 1609.05473 | title_snapshot |
10.1609/aaai.v31i1.10805 | Relational Deep Learning: A Deep Latent Variable Model for Link Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/10805 | https://ojs.aaai.org/index.php/AAAI/article/download/10805/10664 | [
"Hao Wang",
"Xingjian Shi",
"Dit-Yan Yeung"
] | Link prediction is a fundamental task in such areas as social network analysis, information retrieval, and bioinformatics. Usually link prediction methods use the link structures or node attributes as the sources of information. Recently, the relational topic model (RTM) and its variants have been proposed as hybrid me... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10805 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10802 | PAC Identification of a Bandit Arm Relative to a Reward Quantile | https://ojs.aaai.org/index.php/AAAI/article/view/10802 | https://ojs.aaai.org/index.php/AAAI/article/download/10802/10661 | [
"Arghya Roy Chaudhuri",
"Shivaram Kalyanakrishnan"
] | We propose a PAC formulation for identifying an arm in an n-armed bandit whose mean is within a fixed tolerance of the m-th highest mean. This setup generalises a previous formulation with m = 1, and differs from yet another one which requires m such arms to be identified. The key implication of our proposed approach i... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10802 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10803 | Efficient Online Model Adaptation by Incremental Simplex Tableau | https://ojs.aaai.org/index.php/AAAI/article/view/10803 | https://ojs.aaai.org/index.php/AAAI/article/download/10803/10662 | [
"Zhixian Lei",
"Xuehan Ye",
"Yongcai Wang",
"Deying Li",
"Jia Xu"
] | Online multi-kernel learning is promising in the era of mobile computing, in which a combined classifier with multiple kernels are offline trained, and online adapts to personalized features for serving the end user precisely and smartly. The online adaptation is mainly carried out at the end-devices, which requires th... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10803 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10800 | Informative Subspace Learning for Counterfactual Inference | https://ojs.aaai.org/index.php/AAAI/article/view/10800 | https://ojs.aaai.org/index.php/AAAI/article/download/10800/10659 | [
"Yale Chang",
"Jennifer Dy"
] | Inferring causal relations from observational data is widely used for knowledge discovery in healthcare and economics. To investigate whether a treatment can affect an outcome of interest, we focus on answering counterfactual questions of this type: what would a patient’s blood pressure be had he/she received a differe... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10800 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10801 | Sparse Deep Transfer Learning for Convolutional Neural Network | https://ojs.aaai.org/index.php/AAAI/article/view/10801 | https://ojs.aaai.org/index.php/AAAI/article/download/10801/10660 | [
"Jiaming Liu",
"Yali Wang",
"Yu Qiao"
] | Extensive studies have demonstrated that the representations of convolutional neural networks (CNN), which are learned from a large-scale data set in the source domain, can be effectively transferred to a new target domain. However, compared to the source domain, the target domain often has limited data in practice. In... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10801 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10808 | Querying Partially Labelled Data to Improve a K-nn Classifier | https://ojs.aaai.org/index.php/AAAI/article/view/10808 | https://ojs.aaai.org/index.php/AAAI/article/download/10808/10667 | [
"Vu-Linh Nguyen",
"Sébastien Destercke",
"Marie-Helene Masson"
] | When learning from instances whose output labels may be partial, the problem of knowing which of these output labels should be made precise to improve the accuracy of predictions arises. This problem can be seen as the intersection of two tasks: the one of learning from partial labels and the one of active learning, wh... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10808 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10809 | Self-Paced Learning: An Implicit Regularization Perspective | https://ojs.aaai.org/index.php/AAAI/article/view/10809 | https://ojs.aaai.org/index.php/AAAI/article/download/10809/10668 | [
"Yanbo Fan",
"Ran He",
"Jian Liang",
"Baogang Hu"
] | Self-paced learning (SPL) mimics the cognitive mechanism of humans and animals that gradually learns from easy to hard samples. One key issue in SPL is to obtain better weighting strategy that is determined by the minimizer function. Existing methods usually pursue this by artificially designing the explicit form of SP... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10809 | 31 | 1 | null | official | 1606.00128 | title_snapshot |
10.1609/aaai.v31i1.11000 | Structural Correspondence Learning for Cross-Lingual Sentiment Classification with One-to-Many Mappings | https://ojs.aaai.org/index.php/AAAI/article/view/11000 | https://ojs.aaai.org/index.php/AAAI/article/download/11000/10859 | [
"Nana Li",
"Shuangfei Zhai",
"Zhongfei Zhang",
"Boying Liu"
] | Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation oracle to automatically induce task specific, cross-lingual correspondences. It transfers knowledge through identifying important features, i.e... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11000 | 31 | 1 | null | official | 1611.08737 | title_snapshot |
10.1609/aaai.v31i1.11013 | Greedy Flipping for Constrained Word Deletion | https://ojs.aaai.org/index.php/AAAI/article/view/11013 | https://ojs.aaai.org/index.php/AAAI/article/download/11013/10872 | [
"Jin-ge Yao",
"Xiaojun Wan"
] | In this paper we propose a simple yet efficient method for constrained word deletion to compress sentences, based on top-down greedy local flipping from multiple random initializations. The algorithm naturally integrates various grammatical constraints in the compression process, without using time-consuming integer li... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11013 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.11012 | Efficiently Mining High Quality Phrases from Texts | https://ojs.aaai.org/index.php/AAAI/article/view/11012 | https://ojs.aaai.org/index.php/AAAI/article/download/11012/10871 | [
"Bing Li",
"Xiaochun Yang",
"Bin Wang",
"Wei Cui"
] | Phrase mining is a key research problem for semantic analysis and text-based information retrieval. The existing approaches based on NLP, frequency, and statistics cannot extract high quality phrases and the processing is also time consuming, which are not suitable for dynamic on-line applications. In this paper, we pr... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11012 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.11015 | Using Discourse Signals for Robust Instructor Intervention Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/11015 | https://ojs.aaai.org/index.php/AAAI/article/download/11015/10874 | [
"Muthu Kumar Chandrasekaran",
"Carrie Epp",
"Min-Yen Kan",
"Diane Litman"
] | We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs). Our key finding is that using automatically obtained discourse relations improves the prediction of when instructors intervene in student discussions, when compared with a state-of-the-art... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11015 | 31 | 1 | null | official | 1612.00944 | title_snapshot |
10.1609/aaai.v31i1.11014 | Recurrent Neural Networks with Auxiliary Labels for Cross-Domain Opinion Target Extraction | https://ojs.aaai.org/index.php/AAAI/article/view/11014 | https://ojs.aaai.org/index.php/AAAI/article/download/11014/10873 | [
"Ying Ding",
"Jianfei Yu",
"Jing Jiang"
] | Opinion target extraction is a fundamental task in opinion mining. In recent years, neural network based supervised learning methods have achieved competitive performance on this task. However, as with any supervised learning method, neural network based methods for this task cannot work well when the training data com... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11014 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.11016 | Learning Latent Sentiment Scopes for Entity-Level Sentiment Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/11016 | https://ojs.aaai.org/index.php/AAAI/article/download/11016/10875 | [
"Hao Li",
"Wei Lu"
] | In this paper, we focus on the task of extracting named entities together with their associated sentiment information in a joint manner. Our key observation in such an entity-level sentiment analysis (a.k.a. targeted sentiment analysis) task is that there exists a sentiment scope within which each named entity is embed... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11016 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.11011 | Collaborative User Clustering for Short Text Streams | https://ojs.aaai.org/index.php/AAAI/article/view/11011 | https://ojs.aaai.org/index.php/AAAI/article/download/11011/10870 | [
"Shangsong Liang",
"Zhaochun Ren",
"Emine Yilmaz",
"Evangelos Kanoulas"
] | In this paper, we study the problem of user clustering in the context of their published short text streams. Clustering users by short text streams is more challenging than in the case of long documents associated with them as it is difficult to track users' dynamic interests in streaming sparse data. To obtain better ... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11011 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.11010 | Automatic Emphatic Information Extraction from Aligned Acoustic Data and Its Application on Sentence Compression | https://ojs.aaai.org/index.php/AAAI/article/view/11010 | https://ojs.aaai.org/index.php/AAAI/article/download/11010/10869 | [
"Yanju Chen",
"Rong Pan"
] | We introduce a novel method to extract and utilize the semantic information from acoustic data. By automatic Speech-To-Text alignment techniques, we are able to detect word-based acoustic durations that can prosodically emphasize specific words in an utterance. We model and analyze the sentence-based emphatic patterns ... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11010 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10999 | Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10999 | https://ojs.aaai.org/index.php/AAAI/article/download/10999/10858 | [
"Zhou Zhao",
"Hanqing Lu",
"Vincent Zheng",
"Deng Cai",
"Xiaofei He",
"Yueting Zhuang"
] | Nowadays the community-based question answering (CQA) sites become the popular Internet-based web service, which have accumulated millions of questions and their posted answers over time. Thus, question answering becomes an essential problem in CQA sites, which ranks the high-quality answers to the given question. Curr... | main | NLP and Text Mining | 10.1609/aaai.v31i1.10999 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.11009 | Efficient Dependency-Guided Named Entity Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/11009 | https://ojs.aaai.org/index.php/AAAI/article/download/11009/10868 | [
"Zhanming Jie",
"Aldrian Muis",
"Wei Lu"
] | Named entity recognition (NER), which focuses on the extraction of semantically meaningful named entities and their semantic classes from text, serves as an indispensable component for several down-stream natural language processing (NLP) tasks such as relation extraction and event extraction. Dependency trees, on the ... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11009 | 31 | 1 | null | official | 1810.08436 | title_snapshot |
10.1609/aaai.v31i1.11007 | Salience Estimation via Variational Auto-Encoders for Multi-Document Summarization | https://ojs.aaai.org/index.php/AAAI/article/view/11007 | https://ojs.aaai.org/index.php/AAAI/article/download/11007/10866 | [
"Piji Li",
"Zihao Wang",
"Wai Lam",
"Zhaochun Ren",
"Lidong Bing"
] | We propose a new unsupervised sentence salience framework for Multi-Document Summarization (MDS), which can be divided into two components: latent semantic modeling and salience estimation. For latent semantic modeling, a neural generative model called Variational Auto-Encoders (VAEs) is employed to describe the observ... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11007 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.11008 | Unsupervised Sentiment Analysis with Signed Social Networks | https://ojs.aaai.org/index.php/AAAI/article/view/11008 | https://ojs.aaai.org/index.php/AAAI/article/download/11008/10867 | [
"Kewei Cheng",
"Jundong Li",
"Jiliang Tang",
"Huan Liu"
] | Huge volumes of opinion-rich data is user-generated in social media at an unprecedented rate, easing the analysis of individual and public sentiments. Sentiment analysis has shown to be useful in probing and understanding emotions, expressions and attitudes in the text. However, the distinct characteristics of social m... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11008 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.11005 | Improving Event Causality Recognition with Multiple Background Knowledge Sources Using Multi-Column Convolutional Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/11005 | https://ojs.aaai.org/index.php/AAAI/article/download/11005/10864 | [
"Canasai Kruengkrai",
"Kentaro Torisawa",
"Chikara Hashimoto",
"Julien Kloetzer",
"Jong-Hoon Oh",
"Masahiro Tanaka"
] | We propose a method for recognizing such event causalities as "smoke cigarettes" → "die of lung cancer" using background knowledge taken from web texts as well as original sentences from which candidates for the causalities were extracted. We retrieve texts related to our event causality candidates from four billion we... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11005 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.11006 | Attentive Interactive Neural Networks for Answer Selection in Community Question Answering | https://ojs.aaai.org/index.php/AAAI/article/view/11006 | https://ojs.aaai.org/index.php/AAAI/article/download/11006/10865 | [
"Xiaodong Zhang",
"Sujian Li",
"Lei Sha",
"Houfeng Wang"
] | Answer selection plays a key role in community question answering (CQA). Previous research on answer selection usually ignores the problems of redundancy and noise prevalent in CQA. In this paper, we propose to treat different text segments differently and design a novel attentive interactive neural network (AI-NN) to ... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11006 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.11003 | Bootstrapping Distantly Supervised IE Using Joint Learning and Small Well-Structured Corpora | https://ojs.aaai.org/index.php/AAAI/article/view/11003 | https://ojs.aaai.org/index.php/AAAI/article/download/11003/10862 | [
"Lidong Bing",
"Bhuwan Dhingra",
"Kathryn Mazaitis",
"Jong Hyuk Park",
"William W. Cohen"
] | We propose a framework to improve the performance of distantly-supervised relation extraction, by jointly learning to solve two related tasks: concept-instance extraction and relation extraction. We further extend this framework to make a novel use of document structure: in some small, well-structured corpora, sections... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11003 | 31 | 1 | null | official | 1606.03398 | title_snapshot |
10.1609/aaai.v31i1.11001 | What Happens Next? Future Subevent Prediction Using Contextual Hierarchical LSTM | https://ojs.aaai.org/index.php/AAAI/article/view/11001 | https://ojs.aaai.org/index.php/AAAI/article/download/11001/10860 | [
"Linmei Hu",
"Juanzi Li",
"Liqiang Nie",
"Xiao-Li Li",
"Chao Shao"
] | Events are typically composed of a sequence of subevents. Predicting a future subevent of an event is of great importance for many real-world applications. Most previous work on event prediction relied on hand-crafted features and can only predict events that already exist in the training data. In this paper, we develo... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11001 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.11004 | Distant Supervision via Prototype-Based Global Representation Learning | https://ojs.aaai.org/index.php/AAAI/article/view/11004 | https://ojs.aaai.org/index.php/AAAI/article/download/11004/10863 | [
"Xianpei Han",
"Le Sun"
] | Distant supervision (DS) is a promising technique for relation extraction. Currently, most DS approaches build relation extraction models in local instance feature space, often suffer from the multi-instance problem and the missing label problem. In this paper, we propose a new DS method — prototype-based global repres... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11004 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.11002 | Word Embedding Based Correlation Model for Question/Answer Matching | https://ojs.aaai.org/index.php/AAAI/article/view/11002 | https://ojs.aaai.org/index.php/AAAI/article/download/11002/10861 | [
"Yikang Shen",
"Wenge Rong",
"Nan Jiang",
"Baolin Peng",
"Jie Tang",
"Zhang Xiong"
] | The large scale of Q&A archives accumulated in community based question answering (CQA) servivces are important information and knowledge resource on the web. Question and answer matching task has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing ... | main | NLP and Text Mining | 10.1609/aaai.v31i1.11002 | 31 | 1 | null | official | 1511.04646 | title_snapshot |
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