AAAI
Collection
Accepted papers for AAAI (AAAI Conference on Artificial Intelligence), one dataset per year. • 10 items • Updated
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.v31i1.10890 | Deep MIML Network | https://ojs.aaai.org/index.php/AAAI/article/view/10890 | https://ojs.aaai.org/index.php/AAAI/article/download/10890/10749 | [
"Ji Feng",
"Zhi-Hua Zhou"
] | In many real world applications, the concerned objects are with multiple labels, and can be represented as a bag of instances. Multi-instance Multi-label (MIML) learning provides a framework for handling such task and has exhibited excellent performance in various domains. In a MIML setting, the feature representation ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10890 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10897 | Thompson Sampling for Stochastic Bandits with Graph Feedback | https://ojs.aaai.org/index.php/AAAI/article/view/10897 | https://ojs.aaai.org/index.php/AAAI/article/download/10897/10756 | [
"Aristide Tossou",
"Christos Dimitrakakis",
"Devdatt Dubhashi"
] | We present a simple set of algorithms based on Thompson Sampling for stochastic bandit problems with graph feedback. Thompson Sampling is generally applicable, without the need to construct complicated upper confidence bounds. As we show in this paper, it has excellent performance in problems with graph feedback, even ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10897 | 31 | 1 | null | official | 1701.04238 | title_snapshot |
10.1609/aaai.v31i1.10898 | Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption | https://ojs.aaai.org/index.php/AAAI/article/view/10898 | https://ojs.aaai.org/index.php/AAAI/article/download/10898/10757 | [
"Tameem Adel",
"Han Zhao",
"Alexander Wong"
] | Domain adaptation addresses learning tasks where training is performed on data from one domain whereas testing is performed on data belonging to a different but related domain. Assumptions about the relationship between the source and target domains should lead to tractable solutions on the one hand, and be realistic o... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10898 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10895 | Optimal Neighborhood Kernel Clustering with Multiple Kernels | https://ojs.aaai.org/index.php/AAAI/article/view/10895 | https://ojs.aaai.org/index.php/AAAI/article/download/10895/10754 | [
"Xinwang Liu",
"Sihang Zhou",
"Yueqing Wang",
"Miaomiao Li",
"Yong Dou",
"En Zhu",
"Jianping Yin"
] | Multiple kernel $k$-means (MKKM) aims to improve clustering performance by learning an optimal kernel, which is usually assumed to be a linear combination of a group of pre-specified base kernels. However, we observe that this assumption could: i) cause limited kernel representation capability; and ii) not sufficiently... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10895 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10896 | Achieving Privacy in the Adversarial Multi-Armed Bandit | https://ojs.aaai.org/index.php/AAAI/article/view/10896 | https://ojs.aaai.org/index.php/AAAI/article/download/10896/10755 | [
"Aristide Tossou",
"Christos Dimitrakakis"
] | In this paper, we improve the previously best known regret bound to achieve ε-differential privacy in oblivious adversarial bandits from O(T2/3 /ε) to O(√T lnT/ε). This is achieved by combining a Laplace Mechanism with EXP3. We show that though EXP3 is already differentially private, it leaks a linear amount of informa... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10896 | 31 | 1 | null | official | 1701.04222 | title_snapshot |
10.1609/aaai.v31i1.10893 | Multiple Kernel k-Means with Incomplete Kernels | https://ojs.aaai.org/index.php/AAAI/article/view/10893 | https://ojs.aaai.org/index.php/AAAI/article/download/10893/10752 | [
"Xinwang Liu",
"Miaomiao Li",
"Lei Wang",
"Yong Dou",
"Jianping Yin",
"En Zhu"
] | Multiple kernel clustering (MKC) algorithms optimally combine a group of pre-specified base kernels to improve clustering performance. However, existing MKC algorithms cannot efficiently address the situation where some rows and columns of base kernels are absent. This paper proposes a simple while effective algorithm ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10893 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10891 | Learning Residual Alternating Automata | https://ojs.aaai.org/index.php/AAAI/article/view/10891 | https://ojs.aaai.org/index.php/AAAI/article/download/10891/10750 | [
"Sebastian Berndt",
"Maciej Liśkiewicz",
"Matthias Lutter",
"Rüdiger Reischuk"
] | Residuality plays an essential role for learning finite automata. While residual deterministic and non-deterministic automata have been understood quite well, fundamental questions concerning alternating automata (AFA) remain open. Recently, Angluin, Eisenstat, and Fisman (2015) have initiated a systematic study of res... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10891 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10894 | Robust Loss Functions under Label Noise for Deep Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/10894 | https://ojs.aaai.org/index.php/AAAI/article/download/10894/10753 | [
"Aritra Ghosh",
"Himanshu Kumar",
"P. S. Sastry"
] | In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particularly relevant. The current techniques proposed for learning deep networks under label noise focus on modifying the network architecture and... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10894 | 31 | 1 | null | official | 1712.09482 | title_snapshot |
10.1609/aaai.v31i1.10892 | Heavy-Tailed Analogues of the Covariance Matrix for ICA | https://ojs.aaai.org/index.php/AAAI/article/view/10892 | https://ojs.aaai.org/index.php/AAAI/article/download/10892/10751 | [
"Joseph Anderson",
"Navin Goyal",
"Anupama Nandi",
"Luis Rademacher"
] | Independent Component Analysis (ICA) is the problem of learning a square matrix A, given samples of X = AS, where S is a random vector with independent coordinates. Most existing algorithms are provably efficient only when each Si has finite and moderately valued fourth moment. However, there are practical applications... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10892 | 31 | 1 | null | official | 1702.06976 | title_snapshot |
10.1609/aaai.v31i1.10899 | Continuous Conditional Dependency Network for Structured Regression | https://ojs.aaai.org/index.php/AAAI/article/view/10899 | https://ojs.aaai.org/index.php/AAAI/article/download/10899/10758 | [
"Chao Han",
"Mohamed Ghalwash",
"Zoran Obradovic"
] | Structured regression on graphs aims to predict response variables from multiple nodes by discovering and exploiting the dependency structure among response variables. This problem is challenging since dependencies among response variables are always unknown, and the associated prior knowledge is non-symmetric. In prev... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10899 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10886 | Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm | https://ojs.aaai.org/index.php/AAAI/article/view/10886 | https://ojs.aaai.org/index.php/AAAI/article/download/10886/10745 | [
"Xiawei Guo",
"Quanming Yao",
"James Kwok"
] | Most tensor problems are NP-hard, and low-rank tensor completion is much more difficult than low-rank matrix completion. In this paper, we propose a time and space-efficient low-rank tensor completion algorithm by using the scaled latent nuclear norm for regularization and the Frank-Wolfe (FW) algorithm for optimizatio... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10886 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10884 | The Bernstein Mechanism: Function Release under Differential Privacy | https://ojs.aaai.org/index.php/AAAI/article/view/10884 | https://ojs.aaai.org/index.php/AAAI/article/download/10884/10743 | [
"Francesco Aldà",
"Benjamin Rubinstein"
] | We address the problem of general function release under differential privacy, by developing a functional mechanism that applies under the weak assumptions of oracle access to target function evaluation and sensitivity. These conditions permit treatment of functions described explicitly or implicitly as algorithmic bla... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10884 | 31 | 1 | null | official | 1507.04499 | title_snapshot |
10.1609/aaai.v31i1.10887 | Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems | https://ojs.aaai.org/index.php/AAAI/article/view/10887 | https://ojs.aaai.org/index.php/AAAI/article/download/10887/10746 | [
"Carlo D'Eramo",
"Alessandro Nuara",
"Matteo Pirotta",
"Marcello Restelli"
] | This paper is about the estimation of the maximum expected value of an infinite set of random variables.This estimation problem is relevant in many fields, like the Reinforcement Learning (RL) one.In RL it is well known that, in some stochastic environments, a bias in the estimation error can increase step-by-step the ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10887 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10885 | Variable Kernel Density Estimation in High-Dimensional Feature Spaces | https://ojs.aaai.org/index.php/AAAI/article/view/10885 | https://ojs.aaai.org/index.php/AAAI/article/download/10885/10744 | [
"Christiaan Van der Walt",
"Etienne Barnard"
] | Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high-dimensional feature spaces. We derive a variable kernel bandwidth estimato... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10885 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10882 | Binary Embedding with Additive Homogeneous Kernels | https://ojs.aaai.org/index.php/AAAI/article/view/10882 | https://ojs.aaai.org/index.php/AAAI/article/download/10882/10741 | [
"Saehoon Kim",
"Seungjin Choi"
] | Binary embedding transforms vectors in Euclidean space into the vertices of Hamming space such that Hamming distance between binary codes reflects a particular distance metric. In machine learning, the similarity metrics induced by Mercer kernels are frequently used, leading to the development of binary embedding with ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10882 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10883 | Learning Bayesian Networks with Incomplete Data by Augmentation | https://ojs.aaai.org/index.php/AAAI/article/view/10883 | https://ojs.aaai.org/index.php/AAAI/article/download/10883/10742 | [
"Tameem Adel",
"Cassio De Campos"
] | We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. As expected, the exact algorithm does not s... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10883 | 31 | 1 | null | official | 1608.07734 | title_snapshot |
10.1609/aaai.v31i1.10880 | A General Efficient Hyperparameter-Free Algorithm for Convolutional Sparse Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10880 | https://ojs.aaai.org/index.php/AAAI/article/download/10880/10739 | [
"Zheng Xu",
"Junzhou Huang"
] | Structured sparse learning has become a popular and mature research field. Among all structured sparse models, we found an interesting fact that most structured sparse properties could be captured by convolution operators, most famous ones being total variation and wavelet sparsity. This finding has naturally brought u... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10880 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10881 | Infinitely Many-Armed Bandits with Budget Constraints | https://ojs.aaai.org/index.php/AAAI/article/view/10881 | https://ojs.aaai.org/index.php/AAAI/article/download/10881/10740 | [
"Haifang Li",
"Yingce Xia"
] | We study the infinitely many-armed bandit problem with budget constraints, where the number of arms can be infinite and much larger than the number of possible experiments. The player aims at maximizing his/her total expected reward under a budget constraint B for the cost of pulling arms. We introduce a weak stochasti... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10881 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10888 | CBRAP: Contextual Bandits with RAndom Projection | https://ojs.aaai.org/index.php/AAAI/article/view/10888 | https://ojs.aaai.org/index.php/AAAI/article/download/10888/10747 | [
"Xiaotian Yu",
"Michael R. Lyu",
"Irwin King"
] | Contextual bandits with linear payoffs, which are also known as linear bandits, provide a powerful alternative for solving practical problems of sequential decisions, e.g., online advertisements. In the era of big data, contextual data usually tend to be high-dimensional, which leads to new challenges for traditional l... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10888 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10889 | Solving Indefinite Kernel Support Vector Machine with Difference of Convex Functions Programming | https://ojs.aaai.org/index.php/AAAI/article/view/10889 | https://ojs.aaai.org/index.php/AAAI/article/download/10889/10748 | [
"Hai-Ming Xu",
"Hui Xue",
"Xiao-Hong Chen",
"Yun-Yun Wang"
] | Indefinite kernel support vector machine (IKSVM) has recently attracted increasing attentions in machine learning. Different from traditional SVMs, IKSVM essentially is a non-convex optimization problem. Some algorithms directly change the spectrum of the indefinite kernel matrix at the cost of losing some valuable inf... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10889 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10875 | Large Graph Hashing with Spectral Rotation | https://ojs.aaai.org/index.php/AAAI/article/view/10875 | https://ojs.aaai.org/index.php/AAAI/article/download/10875/10734 | [
"Xuelong Li",
"Di Hu",
"Feiping Nie"
] | Faced with the requirements of huge amounts of data processing nowadays, hashing techniques have attracted much attention due to their efficient storage and searching ability. Among these techniques, the ones based on spectral graph show remarkable performance as they could embed the data on a low-dimensional manifold ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10875 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10876 | Fast Compressive Phase Retrieval under Bounded Noise | https://ojs.aaai.org/index.php/AAAI/article/view/10876 | https://ojs.aaai.org/index.php/AAAI/article/download/10876/10735 | [
"Hongyang Zhang",
"Shan You",
"Zhouchen Lin",
"Chao Xu"
] | We study the problem of recovering a t-sparse real vector from m quadratic equations yi=(ai*x)^2 with noisy measurements yi's. This is known as the problem of compressive phase retrieval, and has been widely applied to X-ray diffraction imaging, microscopy, quantum mechanics, etc. The challenge is to design a a) fast a... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10876 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10873 | Adaptive Proximal Average Approximation for Composite Convex Minimization | https://ojs.aaai.org/index.php/AAAI/article/view/10873 | https://ojs.aaai.org/index.php/AAAI/article/download/10873/10732 | [
"Li Shen",
"Wei Liu",
"Junzhou Huang",
"Yu-Gang Jiang",
"Shiqian Ma"
] | We propose a fast first-order method to solve multi-term nonsmooth composite convex minimization problems by employing a recent proximal average approximation technique and a novel adaptive parameter tuning technique. Thanks to this powerful parameter tuning technique, the proximal gradient step can be performed with a... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10873 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10874 | Fast Online Incremental Learning on Mixture Streaming Data | https://ojs.aaai.org/index.php/AAAI/article/view/10874 | https://ojs.aaai.org/index.php/AAAI/article/download/10874/10733 | [
"Yi Wang",
"Xin Fan",
"Zhongxuan Luo",
"Tianzhu Wang",
"Maomao Min",
"Jiebo Luo"
] | The explosion of streaming data poses challenges to feature learning methods including linear discriminant analysis (LDA). Many existing LDA algorithms are not efficient enough to incrementally update with samples that sequentially arrive in various manners. First, we propose a new fast batch LDA (FLDA/QR) learning alg... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10874 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10871 | Multilinear Regression for Embedded Feature Selection with Application to fMRI Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/10871 | https://ojs.aaai.org/index.php/AAAI/article/download/10871/10730 | [
"Xiaonan Song",
"Haiping Lu"
] | Embedded feature selection is effective when both prediction and interpretation are needed. The Lasso and its extensions are standard methods for selecting a subset of features while optimizing a prediction function. In this paper, we are interested in embedded feature selection for multidimensional data, wherein (1) t... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10871 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10872 | Patch Reordering: A NovelWay to Achieve Rotation and Translation Invariance in Convolutional Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/10872 | https://ojs.aaai.org/index.php/AAAI/article/download/10872/10731 | [
"Xu Shen",
"Xinmei Tian",
"Shaoyan Sun",
"Dacheng Tao"
] | Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on many visual recognition tasks. However, the combination of convolution and pooling operations only shows invariance to small local location changes in meaningful objects in input. Sometimes, such networks are trained using data augme... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10872 | 31 | 1 | null | official | 1911.12682 | title_judge |
10.1609/aaai.v31i1.10870 | From Shared Subspaces to Shared Landmarks: A Robust Multi-Source Classification Approach | https://ojs.aaai.org/index.php/AAAI/article/view/10870 | https://ojs.aaai.org/index.php/AAAI/article/download/10870/10729 | [
"Sarah Erfani",
"Mahsa Baktashmotlagh",
"Masud Moshtaghi",
"Vinh Nguyen",
"Christopher Leckie",
"James Bailey",
"Kotagiri Ramamohanarao"
] | Training machine leaning algorithms on augmented data fromdifferent related sources is a challenging task. This problemarises in several applications, such as the Internet of Things(IoT), where data may be collected from devices with differentsettings. The learned model on such datasets can generalizepoorly due to dist... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10870 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10879 | Latent Discriminant Analysis with Representative Feature Discovery | https://ojs.aaai.org/index.php/AAAI/article/view/10879 | https://ojs.aaai.org/index.php/AAAI/article/download/10879/10738 | [
"Gang Chen"
] | Linear Discriminant Analysis (LDA) is a well-known method for dimension reduction and classification with focus on discriminative feature selection. However, how to discover discriminative as well as representative features in LDA model has not been explored. In this paper, we propose a latent Fisher discriminant model... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10879 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10877 | Balanced Clustering with Least Square Regression | https://ojs.aaai.org/index.php/AAAI/article/view/10877 | https://ojs.aaai.org/index.php/AAAI/article/download/10877/10736 | [
"Hanyang Liu",
"Junwei Han",
"Feiping Nie",
"Xuelong Li"
] | Clustering is a fundamental research topic in data mining. A balanced clustering result is often required in a variety of applications. Many existing clustering algorithms have good clustering performances, yet fail in producing balanced clusters. In this paper, we propose a novel and simple method for clustering, refe... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10877 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10878 | Scalable Graph Embedding for Asymmetric Proximity | https://ojs.aaai.org/index.php/AAAI/article/view/10878 | https://ojs.aaai.org/index.php/AAAI/article/download/10878/10737 | [
"Chang Zhou",
"Yuqiong Liu",
"Xiaofei Liu",
"Zhongyi Liu",
"Jun Gao"
] | Graph Embedding methods are aimed at mapping each vertex into a low dimensional vector space, which preserves certain structural relationships among the vertices in the original graph. Recently, several works have been proposed to learn embeddings based on sampled paths from the graph, e.g., DeepWalk, Line, Node2Vec. H... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10878 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10864 | TaGiTeD: Predictive Task Guided Tensor Decomposition for Representation Learning from Electronic Health Records | https://ojs.aaai.org/index.php/AAAI/article/view/10864 | https://ojs.aaai.org/index.php/AAAI/article/download/10864/10723 | [
"Kai Yang",
"Xiang Li",
"Haifeng Liu",
"Jing Mei",
"Guotong Xie",
"Junfeng Zhao",
"Bing Xie",
"Fei Wang"
] | With the better availability of healthcare data, such as Electronic Health Records (EHR), more and more data analytics methodologies are developed aiming at digging insights from them to improve the quality of care delivery. There are many challenges on analyzing EHR, such as high dimensionality and event sparsity. Mor... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10864 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10865 | Active Search for Sparse Signals with Region Sensing | https://ojs.aaai.org/index.php/AAAI/article/view/10865 | https://ojs.aaai.org/index.php/AAAI/article/download/10865/10724 | [
"Yifei Ma",
"Roman Garnett",
"Jeff Schneider"
] | Autonomous systems can be used to search for sparse signals in a large space; e.g., aerial robots can be deployed to localize threats, detect gas leaks, or respond to distress calls. Intuitively, search algorithms may increase efficiency by collecting aggregate measurements summarizing large contiguous regions. However... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10865 | 31 | 1 | null | official | 1612.00583 | title_snapshot |
10.1609/aaai.v31i1.10862 | How to Train a Compact Binary Neural Network with High Accuracy? | https://ojs.aaai.org/index.php/AAAI/article/view/10862 | https://ojs.aaai.org/index.php/AAAI/article/download/10862/10721 | [
"Wei Tang",
"Gang Hua",
"Liang Wang"
] | How to train a binary neural network (BinaryNet) with both high compression rate and high accuracy on large scale dataset? We answer this question through a careful analysis of previous work on BinaryNets, in terms of training strategies, regularization, and activation approximation. Our findings first reveal that a lo... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10862 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10863 | A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information | https://ojs.aaai.org/index.php/AAAI/article/view/10863 | https://ojs.aaai.org/index.php/AAAI/article/download/10863/10722 | [
"Hsiang-Fu Yu",
"Hsin-Yuan Huang",
"Inderjit Dhillon",
"Chih-Jen Lin"
] | In many applications such as recommender systems and multi-label learning the task is to complete a partially observed binary matrix. Such PU learning (positive-unlabeled) problems can be solved by one-class matrix factorization (MF). In practice side information such as user or item features in recommender systems are... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10863 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10860 | Bilateral k-Means Algorithm for Fast Co-Clustering | https://ojs.aaai.org/index.php/AAAI/article/view/10860 | https://ojs.aaai.org/index.php/AAAI/article/download/10860/10719 | [
"Junwei Han",
"Kun Song",
"Feiping Nie",
"Xuelong Li"
] | With the development of the information technology, the amount of data, e.g. text, image and video, has been increased rapidly. Efficiently clustering those large scale data sets is a challenge. To address this problem, this paper proposes a novel co-clustering method named bilateral k-means algorithm (BKM) for fast co... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10860 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10861 | Parameter Free Large Margin Nearest Neighbor for Distance Metric Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10861 | https://ojs.aaai.org/index.php/AAAI/article/download/10861/10720 | [
"Kun Song",
"Feiping Nie",
"Junwei Han",
"Xuelong Li"
] | We introduce a novel supervised metric learning algorithm named parameter free large margin nearest neighbor (PFLMNN) which can be seen as an improvement of the classical large margin nearest neighbor (LMNN) algorithm. The contributions of our work consist of two aspects. First, our method discards the costterm which s... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10861 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10868 | Deep Collective Inference | https://ojs.aaai.org/index.php/AAAI/article/view/10868 | https://ojs.aaai.org/index.php/AAAI/article/download/10868/10727 | [
"John Moore",
"Jennifer Neville"
] | Collective inference is widely used to improve classification in network datasets. However, despite recent advances in deep learning and the successes of recurrent neural networks (RNNs), researchers have only just recently begun to study how to apply RNNs to heterogeneous graph and network datasets. There has been rec... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10868 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10869 | Low-Rank Factorization of Determinantal Point Processes | https://ojs.aaai.org/index.php/AAAI/article/view/10869 | https://ojs.aaai.org/index.php/AAAI/article/download/10869/10728 | [
"Mike Gartrell",
"Ulrich Paquet",
"Noam Koenigstein"
] | Determinantal point processes (DPPs) have garnered attention as an elegant probabilistic model of set diversity. They are useful for a number of subset selection tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. In this work we present a new method for learning th... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10869 | 31 | 1 | null | official | 1602.05436 | title_judge |
10.1609/aaai.v31i1.10866 | A Riemannian Network for SPD Matrix Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10866 | https://ojs.aaai.org/index.php/AAAI/article/download/10866/10725 | [
"Zhiwu Huang",
"Luc Van Gool"
] | Symmetric Positive Definite (SPD) matrix learning methods have become popular in many image and video processing tasks, thanks to their ability to learn appropriate statistical representations while respecting Riemannian geometry of underlying SPD manifolds. In this paper we build a Riemannian network architecture to o... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10866 | 31 | 1 | null | official | 1608.04233 | title_snapshot |
10.1609/aaai.v31i1.10867 | Multi-View Clustering via Deep Matrix Factorization | https://ojs.aaai.org/index.php/AAAI/article/view/10867 | https://ojs.aaai.org/index.php/AAAI/article/download/10867/10726 | [
"Handong Zhao",
"Zhengming Ding",
"Yun Fu"
] | Multi-View Clustering (MVC) has garnered more attention recently since many real-world data are comprised of different representations or views. The key is to explore complementary information to benefit the clustering problem. In this paper, we present a deep matrix factorization framework for MVC, where semi-nonnegat... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10867 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10853 | Twin Learning for Similarity and Clustering: A Unified Kernel Approach | https://ojs.aaai.org/index.php/AAAI/article/view/10853 | https://ojs.aaai.org/index.php/AAAI/article/download/10853/10712 | [
"Zhao Kang",
"Chong Peng",
"Qiang Cheng"
] | Many similarity-based clustering methods work in two separate steps including similarity matrix computation and subsequent spectral clustering. However similarity measurement is challenging because it is usually impacted by many factors, e.g., the choice of similarity metric, neighborhood size, scale of data, noise and... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10853 | 31 | 1 | null | official | 1705.00678 | title_snapshot |
10.1609/aaai.v31i1.10854 | Tsallis Regularized Optimal Transport and Ecological Inference | https://ojs.aaai.org/index.php/AAAI/article/view/10854 | https://ojs.aaai.org/index.php/AAAI/article/download/10854/10713 | [
"Boris Muzellec",
"Richard Nock",
"Giorgio Patrini",
"Frank Nielsen"
] | Optimal transport is a powerful framework for computing distances between probability distributions. We unify the two main approaches to optimal transport, namely Monge-Kantorovitch and Sinkhorn-Cuturi, into what we define as Tsallis regularized optimal transport (TROT). TROT interpolates a rich family of distortions f... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10854 | 31 | 1 | null | official | 1609.04495 | title_snapshot |
10.1609/aaai.v31i1.10851 | Column Networks for Collective Classification | https://ojs.aaai.org/index.php/AAAI/article/view/10851 | https://ojs.aaai.org/index.php/AAAI/article/download/10851/10710 | [
"Trang Pham",
"Truyen Tran",
"Dinh Phung",
"Svetha Venkatesh"
] | Relational learning deals with data that are characterized by relational structures. An important task is collective classification, which is to jointly classify networked objects. While it holds a great promise to produce a better accuracy than non-collective classifiers, collective classification is computationally c... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10851 | 31 | 1 | null | official | 1609.04508 | title_snapshot |
10.1609/aaai.v31i1.10852 | Compressed K-Means for Large-Scale Clustering | https://ojs.aaai.org/index.php/AAAI/article/view/10852 | https://ojs.aaai.org/index.php/AAAI/article/download/10852/10711 | [
"Xiaobo Shen",
"Weiwei Liu",
"Ivor Tsang",
"Fumin Shen",
"Quan-Sen Sun"
] | Large-scale clustering has been widely used in many applications, and has received much attention. Most existing clustering methods suffer from both expensive computation and memory costs when applied to large-scale datasets. In this paper, we propose a novel clustering method, dubbed compressed k-means (CKM), for fast... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10852 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10850 | Self-Correcting Models for Model-Based Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10850 | https://ojs.aaai.org/index.php/AAAI/article/download/10850/10709 | [
"Erik Talvitie"
] | When an agent cannot represent a perfectly accurate model of its environment's dynamics, model-based reinforcement learning (MBRL) can fail catastrophically. Planning involves composing the predictions of the model; when flawed predictions are composed, even minor errors can compound and render the model useless for pl... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10850 | 31 | 1 | null | official | 1612.06018 | title_snapshot |
10.1609/aaai.v31i1.10859 | Online Active Linear Regression via Thresholding | https://ojs.aaai.org/index.php/AAAI/article/view/10859 | https://ojs.aaai.org/index.php/AAAI/article/download/10859/10718 | [
"Carlos Riquelme",
"Ramesh Johari",
"Baosen Zhang"
] | We consider the problem of online active learning to collect data for regression modeling. Specifically, we consider a decision maker with a limited experimentation budget who must efficiently learn an underlying linear population model. Our main contribution is a novel threshold-based algorithm for selection of most i... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10859 | 31 | 1 | null | official | 1602.02845 | title_snapshot |
10.1609/aaai.v31i1.10857 | Query-Efficient Imitation Learning for End-to-End Simulated Driving | https://ojs.aaai.org/index.php/AAAI/article/view/10857 | https://ojs.aaai.org/index.php/AAAI/article/download/10857/10716 | [
"Jiakai Zhang",
"Kyunghyun Cho"
] | One way to approach end-to-end autonomous driving is to learn a policy that maps from a sensory input, such as an image frame from a front-facing camera, to a driving action, by imitating an expert driver, or a reference policy. This can be done by supervised learning, where a policy is tuned to minimize the difference... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10857 | 31 | 1 | null | official | 1605.06450 | title_judge |
10.1609/aaai.v31i1.10858 | Deep Hashing: A Joint Approach for Image Signature Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10858 | https://ojs.aaai.org/index.php/AAAI/article/download/10858/10717 | [
"Yadong Mu",
"Zhu Liu"
] | Similarity-based image hashing represents crucial technique for visual data storage reduction and expedited image search. Conventional hashing schemes typically feed hand-crafted features into hash functions, which separates the procedures of feature extraction and hash function learning. In this paper, we propose a no... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10858 | 31 | 1 | null | official | 1608.03658 | title_snapshot |
10.1609/aaai.v31i1.10855 | Deep Learning for Fixed Model Reuse | https://ojs.aaai.org/index.php/AAAI/article/view/10855 | https://ojs.aaai.org/index.php/AAAI/article/download/10855/10714 | [
"Yang Yang",
"De-Chuan Zhan",
"Ying Fan",
"Yuan Jiang",
"Zhi-Hua Zhou"
] | Model reuse attempts to construct a model by utilizing existing available models, mostly trained for other tasks, rather than building a model from scratch. It is helpful to reduce the time cost, data amount, and expertise required. Deep learning has achieved great success in various tasks involving images, voices and ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10855 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10856 | Learning Safe Prediction for Semi-Supervised Regression | https://ojs.aaai.org/index.php/AAAI/article/view/10856 | https://ojs.aaai.org/index.php/AAAI/article/download/10856/10715 | [
"Yu-Feng Li",
"Han-Wen Zha",
"Zhi-Hua Zhou"
] | Semi-supervised learning (SSL) concerns how to improve performance via the usage of unlabeled data. Recent studies indicate that the usage of unlabeled data might even deteriorate performance. Although some proposals have been developed to alleviate such a fundamental challenge for semi-supervised classification, the e... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10856 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10842 | Streaming Classification with Emerging New Class by Class Matrix Sketching | https://ojs.aaai.org/index.php/AAAI/article/view/10842 | https://ojs.aaai.org/index.php/AAAI/article/download/10842/10701 | [
"Xin Mu",
"Feida Zhu",
"Juan Du",
"Ee-Peng Lim",
"Zhi-Hua Zhou"
] | Streaming classification with emerging new class is an important problem of great research challenge and practical value. In many real applications, the task often needs to handle large matrices issues such as textual data in the bag-of-words model and large-scale image analysis. However, the methodologies and approach... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10842 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10843 | Accelerated Variance Reduced Stochastic ADMM | https://ojs.aaai.org/index.php/AAAI/article/view/10843 | https://ojs.aaai.org/index.php/AAAI/article/download/10843/10702 | [
"Yuanyuan Liu",
"Fanhua Shang",
"James Cheng"
] | Recently, many variance reduced stochastic alternating direction method of multipliers (ADMM) methods (e.g. SAG-ADMM, SDCA-ADMM and SVRG-ADMM) have made exciting progress such as linear convergence rates for strongly convex problems. However, the best known convergence rate for general convex problems is O(1/T) as oppo... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10843 | 31 | 1 | null | official | 1707.03190 | title_snapshot |
10.1609/aaai.v31i1.10840 | Multi-Kernel Low-Rank Dictionary Pair Learning for Multiple Features Based Image Classification | https://ojs.aaai.org/index.php/AAAI/article/view/10840 | https://ojs.aaai.org/index.php/AAAI/article/download/10840/10699 | [
"Xiaoke Zhu",
"Xiao-Yuan Jing",
"Fei Wu",
"Di Wu",
"Li Cheng",
"Sen Li",
"Ruimin Hu"
] | Dictionary learning (DL) is an effective feature learning technique, and has led to interesting results in many classification tasks. Recently, by combining DL with multiple kernel learning (which is a crucial and effective technique for combining different feature representation information), a few multi-kernel DL met... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10840 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10841 | Lifted Inference for Convex Quadratic Programs | https://ojs.aaai.org/index.php/AAAI/article/view/10841 | https://ojs.aaai.org/index.php/AAAI/article/download/10841/10700 | [
"Martin Mladenov",
"Leonard Kleinhans",
"Kristian Kersting"
] | Symmetry is the essential element of lifted inferencethat has recently demonstrated the possibility to perform very efficient inference in highly-connected, but symmetric probabilistic models. This raises the question, whether this holds for optimization problems in general.Here we show that for a large classof optimiz... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10841 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10848 | Fast Generalized Distillation for Semi-Supervised Domain Adaptation | https://ojs.aaai.org/index.php/AAAI/article/view/10848 | https://ojs.aaai.org/index.php/AAAI/article/download/10848/10707 | [
"Shuang Ao",
"Xiang Li",
"Charles Ling"
] | Semi-supervised domain adaptation (SDA) is a typical setting when we face the problem of domain adaptation in real applications. How to effectively utilize the unlabeled data is an important issue in SDA. Previous work requires access to the source data to measure the data distribution mismatch, which is ineffective wh... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10848 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10849 | Sampling Beats Fixed Estimate Predictors for Cloning Stochastic Behavior in Multiagent Systems | https://ojs.aaai.org/index.php/AAAI/article/view/10849 | https://ojs.aaai.org/index.php/AAAI/article/download/10849/10708 | [
"Brian Hrolenok",
"Byron Boots",
"Tucker Balch"
] | Modeling stochastic multiagent behavior such as fish schooling is challenging for fixed-estimate prediction techniques because they fail to reliably reproduce the stochastic aspects of the agents’ behavior. We show how standard fixed-estimate predictors fit within a probabilistic framework, and suggest the reason they ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10849 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10846 | Multivariate Hawkes Processes for Large-Scale Inference | https://ojs.aaai.org/index.php/AAAI/article/view/10846 | https://ojs.aaai.org/index.php/AAAI/article/download/10846/10705 | [
"Rémi Lemonnier",
"Kevin Scaman",
"Argyris Kalogeratos"
] | In this paper, we present a framework for fitting multivariate Hawkes processes for large-scale problems, both in the number of events in the observed history n and the number of event types d (i.e. dimensions). The proposed Scalable Low-Rank Hawkes Process (SLRHP) framework introduces a low-rank approximation of the k... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10846 | 31 | 1 | null | official | 1602.08418 | title_snapshot |
10.1609/aaai.v31i1.10847 | Self-Paced Multi-Task Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10847 | https://ojs.aaai.org/index.php/AAAI/article/download/10847/10706 | [
"Changsheng Li",
"Junchi Yan",
"Fan Wei",
"Weishan Dong",
"Qingshan Liu",
"Hongyuan Zha"
] | Multi-task learning is a paradigm, where multiple tasks are jointly learnt. Previous multi-task learning models usually treat all tasks and instances per task equally during learning. Inspired by the fact that humans often learn from easy concepts to hard ones in the cognitive process, in this paper, we propose a novel... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10847 | 31 | 1 | null | official | 1604.01474 | title_snapshot |
10.1609/aaai.v31i1.10844 | Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions | https://ojs.aaai.org/index.php/AAAI/article/view/10844 | https://ojs.aaai.org/index.php/AAAI/article/download/10844/10703 | [
"Alejandro Molina",
"Sriraam Natarajan",
"Kristian Kersting"
] | Multivariate count data are pervasive in science in the form of histograms, contingency tables and others. Previous work on modeling this type of distributions do not allow for fast and tractable inference. In this paper we present a novel Poisson graphical model, the first based on sum product networks, called PSPN, a... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10844 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10845 | Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee | https://ojs.aaai.org/index.php/AAAI/article/view/10845 | https://ojs.aaai.org/index.php/AAAI/article/download/10845/10704 | [
"Yi Xu",
"Haiqin Yang",
"Lijun Zhang",
"Tianbao Yang"
] | In this paper, we address learning problems for high dimensional data. Previously, oblivious random projection based approaches that project high dimensional features onto a random subspace have been used in practice for tackling high-dimensionality challenge in machine learning. Recently, various non-oblivious randomi... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10845 | 31 | 1 | null | official | 1612.01663 | title_snapshot |
10.1609/aaai.v31i1.10831 | Asymmetric Discrete Graph Hashing | https://ojs.aaai.org/index.php/AAAI/article/view/10831 | https://ojs.aaai.org/index.php/AAAI/article/download/10831/10690 | [
"Xiaoshuang Shi",
"Fuyong Xing",
"Kaidi Xu",
"Manish Sapkota",
"Lin Yang"
] | Recently, many graph based hashing methods have been emerged to tackle large-scale problems. However, there exists two major bottlenecks: (1) directly learning discrete hashing codes is an NP-hardoptimization problem; (2) the complexity of both storage and computational time to build a graph with n data points is O(n2)... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10831 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10832 | Probabilistic Non-Negative Matrix Factorization and Its Robust Extensions for Topic Modeling | https://ojs.aaai.org/index.php/AAAI/article/view/10832 | https://ojs.aaai.org/index.php/AAAI/article/download/10832/10691 | [
"Minnan Luo",
"Feiping Nie",
"Xiaojun Chang",
"Yi Yang",
"Alexander Hauptmann",
"Qinghua Zheng"
] | Traditional topic model with maximum likelihood estimate inevitably suffers from the conditional independence of words given the document’s topic distribution. In this paper, we follow the generative procedure of topic model and learn the topic-word distribution and topics distribution via directly approximating the wo... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10832 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10830 | Learning Non-Linear Dynamics of Decision Boundaries for Maintaining Classification Performance | https://ojs.aaai.org/index.php/AAAI/article/view/10830 | https://ojs.aaai.org/index.php/AAAI/article/download/10830/10689 | [
"Atsutoshi Kumagai",
"Tomoharu Iwata"
] | We propose a method that involves a probabilistic model for learning future classifiers for tasks in which decision boundaries nonlinearly change over time. In certain applications, such as spam-mail classification, the decision boundary dynamically changes over time. Accordingly, the performance of the classifiers wil... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10830 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10839 | Matching Node Embeddings for Graph Similarity | https://ojs.aaai.org/index.php/AAAI/article/view/10839 | https://ojs.aaai.org/index.php/AAAI/article/download/10839/10698 | [
"Giannis Nikolentzos",
"Polykarpos Meladianos",
"Michalis Vazirgiannis"
] | Graph kernels have emerged as a powerful tool for graph comparison. Most existing graph kernels focus on local properties of graphs and ignore global structure. In this paper, we compare graphs based on their global properties as these are captured by the eigenvectors of their adjacency matrices. We present two algorit... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10839 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10837 | A Framework of Online Learning with Imbalanced Streaming Data | https://ojs.aaai.org/index.php/AAAI/article/view/10837 | https://ojs.aaai.org/index.php/AAAI/article/download/10837/10696 | [
"Yan Yan",
"Tianbao Yang",
"Yi Yang",
"Jianhui Chen"
] | A challenge for mining large-scale streaming data overlooked by most existing studies on online learning is the skewdistribution of examples over different classes. Many previous works have considered cost-sensitive approaches in an online setting for streaming data, where fixed costs are assigned to different classes,... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10837 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10838 | Discover Multiple Novel Labels in Multi-Instance Multi-Label Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10838 | https://ojs.aaai.org/index.php/AAAI/article/download/10838/10697 | [
"Yue Zhu",
"Kai Ming Ting",
"Zhi-Hua Zhou"
] | Multi-instance multi-label learning (MIML) is a learning paradigm where an object is represented by a bag of instances and each bag is associated with multiple labels. Ordinary MIML setting assumes a fixed target label set. In real applications, multiple novel labels may exist outside this set, but hidden in the traini... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10838 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10835 | On Learning High Dimensional Structured Single Index Models | https://ojs.aaai.org/index.php/AAAI/article/view/10835 | https://ojs.aaai.org/index.php/AAAI/article/download/10835/10694 | [
"Ravi Ganti",
"Nikhil Rao",
"Laura Balzano",
"Rebecca Willett",
"Robert Nowak"
] | Single Index Models (SIMs) are simple yet flexible semi-parametric models for machine learning, where the response variable is modeled as a monotonic function of a linear combination of features. Estimation in this context requires learning both the feature weights and the nonlinear function that relates features to ob... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10835 | 31 | 1 | null | official | 1603.03980 | title_snapshot |
10.1609/aaai.v31i1.10836 | A Nearly-Black-Box Online Algorithm for Joint Parameter and State Estimation in Temporal Models | https://ojs.aaai.org/index.php/AAAI/article/view/10836 | https://ojs.aaai.org/index.php/AAAI/article/download/10836/10695 | [
"Yusuf Erol",
"Yi Wu",
"Lei Li",
"Stuart Russell"
] | Online joint parameter and state estimation is a core problem for temporal models.Most existing methods are either restricted to a particular class of models (e.g., the Storvik filter) or computationally expensive (e.g., particle MCMC). We propose a novel nearly-black-box algorithm, the Assumed Parameter Filter (APF), ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10836 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10833 | A General Framework for Sparsity Regularized Feature Selection via Iteratively Reweighted Least Square Minimization | https://ojs.aaai.org/index.php/AAAI/article/view/10833 | https://ojs.aaai.org/index.php/AAAI/article/download/10833/10692 | [
"Hanyang Peng",
"Yong Fan"
] | A variety of feature selection methods based on sparsity regularization have been developed with different loss functions and sparse regularization functions. Capitalizing on the existing sparsity regularized feature selection methods, we propose a general sparsity feature selection (GSR-FS) algorithm that optimizes a ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10833 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10834 | Generalized Ambiguity Decompositions for Classification with Applications in Active Learning and Unsupervised Ensemble Pruning | https://ojs.aaai.org/index.php/AAAI/article/view/10834 | https://ojs.aaai.org/index.php/AAAI/article/download/10834/10693 | [
"Zhengshen Jiang",
"Hongzhi Liu",
"Bin Fu",
"Zhonghai Wu"
] | Error decomposition analysis is a key problem for ensemble learning. Two commonly used error decomposition schemes, the classic Ambiguity Decomposition and Bias-Variance-Covariance decomposition, are only suitable for regression tasks with square loss. We generalized the classic Ambiguity Decomposition from regression ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10834 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10941 | Polynomial Optimization Methods for Matrix Factorization | https://ojs.aaai.org/index.php/AAAI/article/view/10941 | https://ojs.aaai.org/index.php/AAAI/article/download/10941/10800 | [
"Po-Wei Wang",
"Chun-Liang Li",
"J. Kolter"
] | Matrix factorization is a core technique in many machine learning problems, yet also presents a nonconvex and often difficult-to-optimize problem. In this paper we present an approach based upon polynomial optimization techniques that both improves the convergence time of matrix factorization algorithms and helps them ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10941 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10942 | Scalable Multitask Policy Gradient Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10942 | https://ojs.aaai.org/index.php/AAAI/article/download/10942/10801 | [
"Salam El Bsat",
"Haitham Bou Ammar",
"Matthew Taylor"
] | Policy search reinforcement learning (RL) allows agents to learn autonomously with limited feedback. However, such methods typically require extensive experience for successful behavior due to their tabula rasa nature. Multitask RL is an approach, which aims to reduce data requirements by allowing knowledge transfer be... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10942 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10940 | Asynchronous Mini-Batch Gradient Descent with Variance Reduction for Non-Convex Optimization | https://ojs.aaai.org/index.php/AAAI/article/view/10940 | https://ojs.aaai.org/index.php/AAAI/article/download/10940/10799 | [
"Zhouyuan Huo",
"Heng Huang"
] | We provide the first theoretical analysis on the convergence rate of asynchronous mini-batch gradient descent with variance reduction (AsySVRG) for non-convex optimization. Asynchronous stochastic gradient descent (AsySGD) has been broadly used for deep learning optimization, and it is proved to converge with rate of O... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10940 | 31 | 1 | null | official | 1604.03584 | title_judge |
10.1609/aaai.v31i1.10949 | Estimating Uncertainty Online Against an Adversary | https://ojs.aaai.org/index.php/AAAI/article/view/10949 | https://ojs.aaai.org/index.php/AAAI/article/download/10949/10808 | [
"Volodymyr Kuleshov",
"Stefano Ermon"
] | Assessing uncertainty is an important step towards ensuring the safety and reliability of machine learning systems. Existing uncertainty estimation techniques may fail when their modeling assumptions are not met, e.g. when the data distribution differs from the one seen at training time. Here, we propose techniques tha... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10949 | 31 | 1 | null | official | 1607.03594 | title_snapshot |
10.1609/aaai.v31i1.10947 | Scalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10947 | https://ojs.aaai.org/index.php/AAAI/article/download/10947/10806 | [
"Apoorv Aggarwal",
"Sandip Ghoshal",
"Ankith Shetty",
"Suhit Sinha",
"Ganesh Ramakrishnan",
"Purushottam Kar",
"Prateek Jain"
] | The problem of multi-instance multi-label learning (MIML) requires a bag of instances to be assigned a set of labels most relevant to the bag as a whole. The problem finds numerous applications in machine learning, computer vision, and natural language processing settings where only partial or distant supervision is av... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10947 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10948 | Multiclass Capped ℓp-Norm SVM for Robust Classifications | https://ojs.aaai.org/index.php/AAAI/article/view/10948 | https://ojs.aaai.org/index.php/AAAI/article/download/10948/10807 | [
"Feiping Nie",
"Xiaoqian Wang",
"Heng Huang"
] | Support vector machine (SVM) model is one of most successful machine learning methods and has been successfully applied to solve numerous real-world application. Because the SVM methods use the hinge loss or squared hinge loss functions for classifications, they usually outperform other classification approaches, e.g. ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10948 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10945 | Where to Add Actions in Human-in-the-Loop Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10945 | https://ojs.aaai.org/index.php/AAAI/article/download/10945/10804 | [
"Travis Mandel",
"Yun-En Liu",
"Emma Brunskill",
"Zoran Popović"
] | In order for reinforcement learning systems to learn quickly in vast action spaces such as the space of all possible pieces of text or the space of all images, leveraging human intuition and creativity is key. However, a human-designed action space is likely to be initially imperfect and limited; furthermore, humans ma... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10945 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10946 | Semi-Supervised Classifications via Elastic and Robust Embedding | https://ojs.aaai.org/index.php/AAAI/article/view/10946 | https://ojs.aaai.org/index.php/AAAI/article/download/10946/10805 | [
"Yun Liu",
"Yiming Guo",
"Hua Wang",
"Feiping Nie",
"Heng Huang"
] | Transductive semi-supervised learning can only predict labels for unlabeled data appearing in training data, and can not predict labels for testing data never appearing in training set. To handle this out-of-sample problem, many inductive methods make a constraint such that the predicted label matrix should be exactly ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10946 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10943 | The Multivariate Generalised von Mises Distribution: Inference and Applications | https://ojs.aaai.org/index.php/AAAI/article/view/10943 | https://ojs.aaai.org/index.php/AAAI/article/download/10943/10802 | [
"Alexandre Navarro",
"Jes Frellsen",
"Richard Turner"
] | Circular variables arise in a multitude of data-modelling contexts ranging from robotics to the social sciences, but they have been largely overlooked by the machine learning community. This paper partially redresses this imbalance by extending some standard probabilistic modelling tools to the circular domain. First w... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10943 | 31 | 1 | null | official | 1602.05003 | title_snapshot |
10.1609/aaai.v31i1.10944 | Local Centroids Structured Non-Negative Matrix Factorization | https://ojs.aaai.org/index.php/AAAI/article/view/10944 | https://ojs.aaai.org/index.php/AAAI/article/download/10944/10803 | [
"Hongchang Gao",
"Feiping Nie",
"Heng Huang"
] | Non-negative Matrix Factorization (NMF) has attracted much attention and been widely used in real-world applications. As a clustering method, it fails to handle the case where data points lie in a complicated geometry structure. Existing methods adopt single global centroid for each cluster, failing to capture the mani... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10944 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10930 | Active Search in Intensionally Specified Structured Spaces | https://ojs.aaai.org/index.php/AAAI/article/view/10930 | https://ojs.aaai.org/index.php/AAAI/article/download/10930/10789 | [
"Dino Oglic",
"Roman Garnett",
"Thomas Gaertner"
] | We consider an active search problem in intensionally specified structured spaces. The ultimate goal in this setting is to discover structures from structurally different partitions of a fixed but unknown target class. An example of such a process is that of computer-aided de novo drug design. In the past 20 years seve... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10930 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10931 | Distributed Negative Sampling for Word Embeddings | https://ojs.aaai.org/index.php/AAAI/article/view/10931 | https://ojs.aaai.org/index.php/AAAI/article/download/10931/10790 | [
"Stergios Stergiou",
"Zygimantas Straznickas",
"Rolina Wu",
"Kostas Tsioutsiouliklis"
] | Word2Vec recently popularized dense vector word representations as fixed-length features for machine learning algorithms and is in widespread use today. In this paper we investigate one of its core components, Negative Sampling, and propose efficient distributed algorithms that allow us to scale to vocabulary sizes of ... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10931 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10938 | Robust Partially-Compressed Least-Squares | https://ojs.aaai.org/index.php/AAAI/article/view/10938 | https://ojs.aaai.org/index.php/AAAI/article/download/10938/10797 | [
"Stephen Becker",
"Ban Kawas",
"Marek Petrik"
] | Randomized matrix compression techniques, such as the Johnson-Lindenstrauss transform, have emerged as an effective and practical way for solving large-scale problems efficiently. With a focus on computational efficiency, however, forsaking solutions quality and accuracy becomes the trade-off. In this paper, we investi... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10938 | 31 | 1 | null | official | 1510.04905 | title_snapshot |
10.1609/aaai.v31i1.10939 | Efficient Ordered Combinatorial Semi-Bandits for Whole-Page Recommendation | https://ojs.aaai.org/index.php/AAAI/article/view/10939 | https://ojs.aaai.org/index.php/AAAI/article/download/10939/10798 | [
"Yingfei Wang",
"Hua Ouyang",
"Chu Wang",
"Jianhui Chen",
"Tsvetan Asamov",
"Yi Chang"
] | Multi-Armed Bandit (MAB) framework has been successfully applied in many web applications. However, many complex real-world applications that involve multiple content recommendations cannot fit into the traditional MAB setting. To address this issue, we consider an ordered combinatorial semi-bandit problem where the le... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10939 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10936 | Factorization Bandits for Interactive Recommendation | https://ojs.aaai.org/index.php/AAAI/article/view/10936 | https://ojs.aaai.org/index.php/AAAI/article/download/10936/10795 | [
"Huazheng Wang",
"Qingyun Wu",
"Hongning Wang"
] | We perform online interactive recommendation via a factorization-based bandit algorithm. Low-rank matrix completion is performed over an incrementally constructed user-item preference matrix, where an upper confidence bound based item selection strategy is developed to balance the exploit/explore trade-off during onlin... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10936 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10937 | Recovering True Classifier Performance in Positive-Unlabeled Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10937 | https://ojs.aaai.org/index.php/AAAI/article/download/10937/10796 | [
"Shantanu Jain",
"Martha White",
"Predrag Radivojac"
] | A common approach in positive-unlabeled learning is to train a classification model between labeled and unlabeled data. This strategy is in fact known to give an optimal classifier under mild conditions; however, it results in biased empirical estimates of the classifier performance. In this work, we show that the typi... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10937 | 31 | 1 | null | official | 1702.00518 | title_snapshot |
10.1609/aaai.v31i1.10934 | Label-Free Supervision of Neural Networks with Physics and Domain Knowledge | https://ojs.aaai.org/index.php/AAAI/article/view/10934 | https://ojs.aaai.org/index.php/AAAI/article/download/10934/10793 | [
"Russell Stewart",
"Stefano Ermon"
] | In many machine learning applications, labeled data is scarce and obtaining more labels is expensive. We introduce a new approach to supervising neural networks by specifying constraints that should hold over the output space, rather than direct examples of input-output pairs. These constraints are derived from prior d... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10934 | 31 | 1 | null | official | 1609.05566 | title_snapshot |
10.1609/aaai.v31i1.10935 | Cleaning the Null Space: A Privacy Mechanism for Predictors | https://ojs.aaai.org/index.php/AAAI/article/view/10935 | https://ojs.aaai.org/index.php/AAAI/article/download/10935/10794 | [
"Ke Xu",
"Tongyi Cao",
"Swair Shah",
"Crystal Maung",
"Haim Schweitzer"
] | In standard machine learning and regression setting feature values are used to predict some desired information. The privacy challenge considered here is to prevent an adversary from using available feature values to predict confidential information that one wishes to keep secret. We show that this can sometimes be ach... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10935 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10932 | Importance Sampling with Unequal Support | https://ojs.aaai.org/index.php/AAAI/article/view/10932 | https://ojs.aaai.org/index.php/AAAI/article/download/10932/10791 | [
"Philip Thomas",
"Emma Brunskill"
] | Importance sampling is often used in machine learning when training and testing data come from different distributions. In this paper we propose a new variant of importance sampling that can reduce the variance of importance samplingbased estimates by orders of magnitude when the supports of the training and testing di... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10932 | 31 | 1 | null | official | 1611.03451 | title_snapshot |
10.1609/aaai.v31i1.10933 | Automatic Curriculum Graph Generation for Reinforcement Learning Agents | https://ojs.aaai.org/index.php/AAAI/article/view/10933 | https://ojs.aaai.org/index.php/AAAI/article/download/10933/10792 | [
"Maxwell Svetlik",
"Matteo Leonetti",
"Jivko Sinapov",
"Rishi Shah",
"Nick Walker",
"Peter Stone"
] | In recent years, research has shown that transfer learning methods can be leveraged to construct curricula that sequence a series of simpler tasks such that performance on a final target task is improved. A major limitation of existing approaches is that such curricula are handcrafted by humans that are typically domai... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10933 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10920 | SCOPE: Scalable Composite Optimization for Learning on Spark | https://ojs.aaai.org/index.php/AAAI/article/view/10920 | https://ojs.aaai.org/index.php/AAAI/article/download/10920/10779 | [
"Shen-Yi Zhao",
"Ru Xiang",
"Ying-Hao Shi",
"Peng Gao",
"Wu-Jun Li"
] | Many machine learning models, such as logistic regression (LR) and support vector machine (SVM), can be formulated as composite optimization problems. Recently, many distributed stochastic optimization (DSO) methods have been proposed to solve the large-scale composite optimization problems, which have shown better per... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10920 | 31 | 1 | null | official | 1602.00133 | title_snapshot |
10.1609/aaai.v31i1.10927 | Sequential Classification-Based Optimization for Direct Policy Search | https://ojs.aaai.org/index.php/AAAI/article/view/10927 | https://ojs.aaai.org/index.php/AAAI/article/download/10927/10786 | [
"Yi-Qi Hu",
"Hong Qian",
"Yang Yu"
] | Classification-based optimization is a recently developed framework for derivative-free optimization, which has shown to be effective for non-convex optimization problems with many local optima. This framework requires to sample a batch of solutions for every update of the search model. However, in reinforcement learni... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10927 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10928 | Learning Unitary Operators with Help From u(n) | https://ojs.aaai.org/index.php/AAAI/article/view/10928 | https://ojs.aaai.org/index.php/AAAI/article/download/10928/10787 | [
"Stephanie Hyland",
"Gunnar Rätsch"
] | A major challenge in the training of recurrent neural networks is the so-called vanishing or exploding gradient problem. The use of a norm-preserving transition operator can address this issue, but parametrization is challenging. In this work we focus on unitary operators and describe a parametrization using the Lie al... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10928 | 31 | 1 | null | official | 1607.04903 | title_snapshot |
10.1609/aaai.v31i1.10926 | Scalable Feature Selection via Distributed Diversity Maximization | https://ojs.aaai.org/index.php/AAAI/article/view/10926 | https://ojs.aaai.org/index.php/AAAI/article/download/10926/10785 | [
"Sepehr Zadeh",
"Mehrdad Ghadiri",
"Vahab Mirrokni",
"Morteza Zadimoghaddam"
] | Feature selection is a fundamental problem in machine learning and data mining. The majority of feature selection algorithms are designed for running on a single machine (centralized setting) and they are less applicable to very large datasets. Although there are some distributed methods to tackle this problem, most of... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10926 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10925 | Non-Negative Inductive Matrix Completion for Discrete Dyadic Data | https://ojs.aaai.org/index.php/AAAI/article/view/10925 | https://ojs.aaai.org/index.php/AAAI/article/download/10925/10784 | [
"Piyush Rai"
] | We present a non-negative inductive latent factor model for binary- and count-valued matrices containing dyadic data, with side information along the rows and/or the columns of the matrix. The side information is incorporated by conditioning the row and column latent factors on the available side information via a regr... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10925 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10923 | Selecting Sequences of Items via Submodular Maximization | https://ojs.aaai.org/index.php/AAAI/article/view/10923 | https://ojs.aaai.org/index.php/AAAI/article/download/10923/10782 | [
"Sebastian Tschiatschek",
"Adish Singla",
"Andreas Krause"
] | Motivated by many real world applications such as recommendations in online shopping or entertainment, we consider the problem of selecting sequences of items. In this paper we introduce a novel class of utility functions over sequences of items, strictly generalizing the commonly used class of submodular set functions... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10923 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10924 | Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning | https://ojs.aaai.org/index.php/AAAI/article/view/10924 | https://ojs.aaai.org/index.php/AAAI/article/download/10924/10783 | [
"Quanshi Zhang",
"Ruiming Cao",
"Ying Nian Wu",
"Song-Chun Zhu"
] | This paper proposes a learning strategy that embeds object-part concepts into a pre-trained convolutional neural network (CNN), in an attempt to 1) explore explicit semantics hidden in CNN units and 2) gradually transform the pre-trained CNN into a semantically interpretable graphical model for hierarchical object unde... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10924 | 31 | 1 | null | official | 1611.04246 | title_snapshot |
10.1609/aaai.v31i1.10921 | Lock-Free Optimization for Non-Convex Problems | https://ojs.aaai.org/index.php/AAAI/article/view/10921 | https://ojs.aaai.org/index.php/AAAI/article/download/10921/10780 | [
"Shen-Yi Zhao",
"Gong-Duo Zhang",
"Wu-Jun Li"
] | Stochastic gradient descent (SGD) and its variants have attracted much attention in machine learning due to their efficiency and effectiveness for optimization. To handle large-scale problems, researchers have recently proposed several lock-free strategy based parallel SGD (LF-PSGD) methods for multi-core systems. Howe... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10921 | 31 | 1 | null | official | 1612.03441 | title_snapshot |
10.1609/aaai.v31i1.10922 | Weighted Bandits or: How Bandits Learn Distorted Values That Are Not Expected | https://ojs.aaai.org/index.php/AAAI/article/view/10922 | https://ojs.aaai.org/index.php/AAAI/article/download/10922/10781 | [
"Aditya Gopalan",
"Prashanth L. A.",
"Michael Fu",
"Steve Marcus"
] | Motivated by models of human decision making proposed to explain commonly observed deviations from conventional expected value preferences, we formulate two stochastic multi-armed bandit problems with distorted probabilities on the cost distributions: the classic K-armed bandit and the linearly parameterized bandit. In... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10922 | 31 | 1 | null | official | null | null |
10.1609/aaai.v31i1.10929 | Coactive Critiquing: Elicitation of Preferences and Features | https://ojs.aaai.org/index.php/AAAI/article/view/10929 | https://ojs.aaai.org/index.php/AAAI/article/download/10929/10788 | [
"Stefano Teso",
"Paolo Dragone",
"Andrea Passerini"
] | When faced with complex choices, users refine their own preference criteria as they explore the catalogue of options. In this paper we propose an approach to preference elicitation suited for this scenario. We extend Coactive Learning, which iteratively collects manipulative feedback, to optionally query example critiq... | main | Machine Learning Methods | 10.1609/aaai.v31i1.10929 | 31 | 1 | null | official | 1612.01941 | title_snapshot |
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