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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.v35i10.17067	Adaptive Knowledge Driven Regularization for Deep Neural Networks	https://ojs.aaai.org/index.php/AAAI/article/view/17067	https://ojs.aaai.org/index.php/AAAI/article/download/17067/16874	[ "Zhaojing Luo", "Shaofeng Cai", "Can Cui", "Beng Chin Ooi", "Yang Yang" ]	In many real-world applications, the amount of data available for training is often limited, and thus inductive bias and auxiliary knowledge are much needed for regularizing model training. One popular regularization method is to impose prior distribution assumptions on model parameters, and many recent works also atte...	main	Machine Learning	10.1609/aaai.v35i10.17067	35	10	8810-8818	official	null	null
10.1609/aaai.v35i10.17068	Multi-Domain Multi-Task Rehearsal for Lifelong Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17068	https://ojs.aaai.org/index.php/AAAI/article/download/17068/16875	[ "Fan Lyu", "Shuai Wang", "Wei Feng", "Zihan Ye", "Fuyuan Hu", "Song Wang" ]	Rehearsal, seeking to remind the model by storing old knowledge in lifelong learning, is one of the most effective ways to mitigate catastrophic forgetting, i.e., biased forgetting of previous knowledge when moving to new tasks. However, the old tasks of the most previous rehearsal-based methods suffer from the unpredi...	main	Machine Learning	10.1609/aaai.v35i10.17068	35	10	8819-8827	official	2012.07236	title_snapshot
10.1609/aaai.v35i10.17069	On the Adequacy of Untuned Warmup for Adaptive Optimization	https://ojs.aaai.org/index.php/AAAI/article/view/17069	https://ojs.aaai.org/index.php/AAAI/article/download/17069/16876	[ "Jerry Ma", "Denis Yarats" ]	Adaptive optimization algorithms such as Adam (Kingma and Ba, 2014) are widely used in deep learning. The stability of such algorithms is often improved with a warmup schedule for the learning rate. Motivated by the difficulty of choosing and tuning warmup schedules, recent work proposes automatic variance rectificatio...	main	Machine Learning	10.1609/aaai.v35i10.17069	35	10	8828-8836	official	1910.04209	title_snapshot
10.1609/aaai.v35i10.17070	Learning Representations for Incomplete Time Series Clustering	https://ojs.aaai.org/index.php/AAAI/article/view/17070	https://ojs.aaai.org/index.php/AAAI/article/download/17070/16877	[ "Qianli Ma", "Chuxin Chen", "Sen Li", "Garrison W. Cottrell" ]	Time-series clustering is an essential unsupervised technique for data analysis, applied to many real-world fields, such as medical analysis and DNA microarray. Existing clustering methods are usually based on the assumption that the data is complete. However, time series in real-world applications often contain missin...	main	Machine Learning	10.1609/aaai.v35i10.17070	35	10	8837-8846	official	null	null
10.1609/aaai.v35i10.17071	Joint-Label Learning by Dual Augmentation for Time Series Classification	https://ojs.aaai.org/index.php/AAAI/article/view/17071	https://ojs.aaai.org/index.php/AAAI/article/download/17071/16878	[ "Qianli Ma", "Zhenjing Zheng", "Jiawei Zheng", "Sen Li", "Wanqing Zhuang", "Garrison W. Cottrell" ]	Recently, deep neural networks (DNNs) have achieved excellent performance on time series classification. However, DNNs require large amounts of labeled data for supervised training. Although data augmentation can alleviate this problem, the standard approach assigns the same label to all augmented samples from the same...	main	Machine Learning	10.1609/aaai.v35i10.17071	35	10	8847-8855	official	null	null
10.1609/aaai.v35i10.17072	Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport	https://ojs.aaai.org/index.php/AAAI/article/view/17072	https://ojs.aaai.org/index.php/AAAI/article/download/17072/16879	[ "Tengfei Ma", "Jie Chen" ]	Hierarchical abstractions are a methodology for solving large-scale graph problems in various disciplines. Coarsening is one such approach: it generates a pyramid of graphs whereby the one in the next level is a structural summary of the prior one. With a long history in scientific computing, many coarsening strategies...	main	Machine Learning	10.1609/aaai.v35i10.17072	35	10	8856-8864	official	1912.11176	title_snapshot
10.1609/aaai.v35i10.17073	Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems	https://ojs.aaai.org/index.php/AAAI/article/view/17073	https://ojs.aaai.org/index.php/AAAI/article/download/17073/16880	[ "Yuzhe Ma", "Jon A Sharp", "Ruizhe Wang", "Earlence Fernandes", "Xiaojin Zhu" ]	Kalman Filter (KF) is widely used in various domains to perform sequential learning or variable estimation. In the context of autonomous vehicles, KF constitutes the core component of many Advanced Driver Assistance Systems (ADAS), such as Forward Collision Warning (FCW). It tracks the states (distance, velocity etc.) ...	main	Machine Learning	10.1609/aaai.v35i10.17073	35	10	8865-8873	official	2012.08704	title_snapshot
10.1609/aaai.v35i10.17074	Exact Reduction of Huge Action Spaces in General Reinforcement Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17074	https://ojs.aaai.org/index.php/AAAI/article/download/17074/16881	[ "Sultan J. Majeed", "Marcus Hutter" ]	The reinforcement learning (RL) framework formalizes the notion of learning with interactions. Many real-world problems have large state-spaces and/or action-spaces such as in Go, StarCraft, protein folding, and robotics or are non-Markovian, which cause significant challenges to RL algorithms. In this work we address ...	main	Machine Learning	10.1609/aaai.v35i10.17074	35	10	8874-8883	official	2012.10200	title_snapshot
10.1609/aaai.v35i10.17075	Composite Adversarial Attacks	https://ojs.aaai.org/index.php/AAAI/article/view/17075	https://ojs.aaai.org/index.php/AAAI/article/download/17075/16882	[ "Xiaofeng Mao", "Yuefeng Chen", "Shuhui Wang", "Hang Su", "Yuan He", "Hui Xue" ]	Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness. In practice, attack algorithms are artificially selected and tuned by human experts to break a ML system. However, manual selection of attackers tends to be sub-optimal, leading to ...	main	Machine Learning	10.1609/aaai.v35i10.17075	35	10	8884-8892	official	2012.05434	title_snapshot
10.1609/aaai.v35i10.17076	Deep Mutual Information Maximin for Cross-Modal Clustering	https://ojs.aaai.org/index.php/AAAI/article/view/17076	https://ojs.aaai.org/index.php/AAAI/article/download/17076/16883	[ "Yiqiao Mao", "Xiaoqiang Yan", "Qiang Guo", "Yangdong Ye" ]	Cross-modal clustering (CMC) aims to enhance the clustering performance by exploring complementary information from multiple modalities. However, the performances of existing CMC algorithms are still unsatisfactory due to the conflict of heterogeneous modalities and the high-dimensional non-linear property of individua...	main	Machine Learning	10.1609/aaai.v35i10.17076	35	10	8893-8901	official	null	null
10.1609/aaai.v35i10.17077	Searching for Machine Learning Pipelines Using a Context-Free Grammar	https://ojs.aaai.org/index.php/AAAI/article/view/17077	https://ojs.aaai.org/index.php/AAAI/article/download/17077/16884	[ "Radu Marinescu", "Akihiro Kishimoto", "Parikshit Ram", "Ambrish Rawat", "Martin Wistuba", "Paulito P. Palmes", "Adi Botea" ]	AutoML automatically selects, composes and parameterizes machine learning algorithms into a workflow or pipeline of operations that aims at maximizing performance on a given dataset. Although current methods for AutoML achieved impressive results they mostly concentrate on optimizing fixed linear workflows. In this pap...	main	Machine Learning	10.1609/aaai.v35i10.17077	35	10	8902-8911	official	null	null
10.1609/aaai.v35i10.17078	Scalable Graph Networks for Particle Simulations	https://ojs.aaai.org/index.php/AAAI/article/view/17078	https://ojs.aaai.org/index.php/AAAI/article/download/17078/16885	[ "Karolis Martinkus", "Aurelien Lucchi", "Nathanaël Perraudin" ]	Learning system dynamics directly from observations is a promising direction in machine learning due to its potential to significantly enhance our ability to understand physical systems. However, the dynamics of many real-world systems are challenging to learn due to the presence of nonlinear potentials and a number of...	main	Machine Learning	10.1609/aaai.v35i10.17078	35	10	8912-8920	official	2010.06948	title_snapshot
10.1609/aaai.v35i10.17079	Infinite Gaussian Mixture Modeling with an Improved Estimation of the Number of Clusters	https://ojs.aaai.org/index.php/AAAI/article/view/17079	https://ojs.aaai.org/index.php/AAAI/article/download/17079/16886	[ "Avi Matza", "Yuval Bistritz" ]	Infinite Gaussian mixture modeling (IGMM) is a modeling method that determines all the parameters of a Gaussian mixture model (GMM), including its order. It has been well documented that it is a consistent estimator for probability density functions in the sense that, given enough training data from sufficiently regula...	main	Machine Learning	10.1609/aaai.v35i10.17079	35	10	8921-8929	official	null	null
10.1609/aaai.v35i10.17080	Exacerbating Algorithmic Bias through Fairness Attacks	https://ojs.aaai.org/index.php/AAAI/article/view/17080	https://ojs.aaai.org/index.php/AAAI/article/download/17080/16887	[ "Ninareh Mehrabi", "Muhammad Naveed", "Fred Morstatter", "Aram Galstyan" ]	Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms. Despite this interest, the robustness of those fairness measures with respect to an intentional adversarial attack has not been pr...	main	Machine Learning	10.1609/aaai.v35i10.17080	35	10	8930-8938	official	2012.08723	title_snapshot
10.1609/aaai.v35i10.17081	Physarum Powered Differentiable Linear Programming Layers and Applications	https://ojs.aaai.org/index.php/AAAI/article/view/17081	https://ojs.aaai.org/index.php/AAAI/article/download/17081/16888	[ "Zihang Meng", "Sathya N. Ravi", "Vikas Singh" ]	Consider a learning algorithm, which involves an internal call to an optimization routine such as a generalized eigenvalue problem, a cone programming problem or even sorting. Integrating such a method as layers within a trainable deep network in a numerically stable way is not simple – for instance, only recently, str...	main	Machine Learning	10.1609/aaai.v35i10.17081	35	10	8939-8949	official	2004.14539	title_snapshot
10.1609/aaai.v35i10.17082	Lenient Regret for Multi-Armed Bandits	https://ojs.aaai.org/index.php/AAAI/article/view/17082	https://ojs.aaai.org/index.php/AAAI/article/download/17082/16889	[ "Nadav Merlis", "Shie Mannor" ]	We consider the Multi-Armed Bandit (MAB) problem, where an agent sequentially chooses actions and observes rewards for the actions it took. While the majority of algorithms try to minimize the regret, i.e., the cumulative difference between the reward of the best action and the agent's action, this criterion might lead...	main	Machine Learning	10.1609/aaai.v35i10.17082	35	10	8950-8957	official	2008.03959	title_snapshot
10.1609/aaai.v35i10.17043	Class-Attentive Diffusion Network for Semi-Supervised Classification	https://ojs.aaai.org/index.php/AAAI/article/view/17043	https://ojs.aaai.org/index.php/AAAI/article/download/17043/16850	[ "Jongin Lim", "Daeho Um", "Hyung Jin Chang", "Dae Ung Jo", "Jin Young Choi" ]	Recently, graph neural networks for semi-supervised classification have been widely studied. However, existing methods only use the information of limited neighbors and do not deal with the inter-class connections in graphs. In this paper, we propose Adaptive aggregation with Class-Attentive Diffusion (AdaCAD), a new a...	main	Machine Learning	10.1609/aaai.v35i10.17043	35	10	8601-8609	official	2006.10222	title_snapshot
10.1609/aaai.v35i10.17044	Auto-Encoding Transformations in Reparameterized Lie Groups for Unsupervised Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17044	https://ojs.aaai.org/index.php/AAAI/article/download/17044/16851	[ "Feng Lin", "Haohang Xu", "Houqiang Li", "Hongkai Xiong", "Guo-Jun Qi" ]	Unsupervised training of deep representations has demonstrated remarkable potentials in mitigating the prohibitive expenses on annotating labeled data recently. Among them is predicting transformations as a pretext task to self-train representations, which has shown great potentials for unsupervised learning. However, ...	main	Machine Learning	10.1609/aaai.v35i10.17044	35	10	8610-8617	official	null	null
10.1609/aaai.v35i10.17045	Multi-Proxy Wasserstein Classifier for Image Classification	https://ojs.aaai.org/index.php/AAAI/article/view/17045	https://ojs.aaai.org/index.php/AAAI/article/download/17045/16852	[ "Benlin Liu", "Yongming Rao", "Jiwen Lu", "Jie Zhou", "Cho-Jui Hsieh" ]	Most widely-used convolutional neural networks (CNNs) end up with a global average pooling layer and a fully-connected layer. In this pipeline, a certain class is represented by one template vector preserved in the feature banks of fully-connected layer. Yet, a class may have multiple properties useful for recognition ...	main	Machine Learning	10.1609/aaai.v35i10.17045	35	10	8618-8626	official	null	null
10.1609/aaai.v35i10.17046	TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17046	https://ojs.aaai.org/index.php/AAAI/article/download/17046/16853	[ "Bingyan Liu", "Yifeng Cai", "Yao Guo", "Xiangqun Chen" ]	The increasing of pre-trained models has significantly facilitated the performance on limited data tasks with transfer learning. However, progress on transfer learning mainly focuses on optimizing the weights of pre-trained models, which ignores the structure mismatch between the model and the target task. This paper a...	main	Machine Learning	10.1609/aaai.v35i10.17046	35	10	8627-8634	official	2103.01542	title_snapshot
10.1609/aaai.v35i10.17047	Learning a Few-shot Embedding Model with Contrastive Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17047	https://ojs.aaai.org/index.php/AAAI/article/download/17047/16854	[ "Chen Liu", "Yanwei Fu", "Chengming Xu", "Siqian Yang", "Jilin Li", "Chengjie Wang", "Li Zhang" ]	Few-shot learning (FSL) aims to recognize target classes by adapting the prior knowledge learned from source classes. Such knowledge usually resides in a deep embedding model for a general matching purpose of the support and query image pairs. The objective of this paper is to repurpose the contrastive learning for suc...	main	Machine Learning	10.1609/aaai.v35i10.17047	35	10	8635-8643	official	null	null
10.1609/aaai.v35i10.17048	Unchain the Search Space with Hierarchical Differentiable Architecture Search	https://ojs.aaai.org/index.php/AAAI/article/view/17048	https://ojs.aaai.org/index.php/AAAI/article/download/17048/16855	[ "Guanting Liu", "Yujie Zhong", "Sheng Guo", "Matthew R. Scott", "Weilin Huang" ]	Differentiable architecture search (DAS) has made great progress in searching for high-performance architectures with reduced computational cost. However, DAS-based methods mainly focus on searching for a repeatable cell structure, which is then stacked sequentially in multiple stages to form the networks. This configu...	main	Machine Learning	10.1609/aaai.v35i10.17048	35	10	8644-8652	official	2101.04028	title_snapshot
10.1609/aaai.v35i10.17049	Overcoming Catastrophic Forgetting in Graph Neural Networks	https://ojs.aaai.org/index.php/AAAI/article/view/17049	https://ojs.aaai.org/index.php/AAAI/article/download/17049/16856	[ "Huihui Liu", "Yiding Yang", "Xinchao Wang" ]	Catastrophic forgetting refers to the tendency that a neural network ``forgets'' the previous learned knowledge upon learning new tasks. Prior methods have been focused on overcoming this problem on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, but have largely overlook...	main	Machine Learning	10.1609/aaai.v35i10.17049	35	10	8653-8661	official	2012.06002	title_snapshot
10.1609/aaai.v35i10.17050	Stable Adversarial Learning under Distributional Shifts	https://ojs.aaai.org/index.php/AAAI/article/view/17050	https://ojs.aaai.org/index.php/AAAI/article/download/17050/16857	[ "Jiashuo Liu", "Zheyan Shen", "Peng Cui", "Linjun Zhou", "Kun Kuang", "Bo Li", "Yishi Lin" ]	Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts due to the greedy adoption of all the correlations found in training data. Recently, there are robust learning methods aiming at this problem by minimizing the worst-case risk over an uncertainty set. However, they e...	main	Machine Learning	10.1609/aaai.v35i10.17050	35	10	8662-8670	official	2006.04414	title_snapshot
10.1609/aaai.v35i10.17051	Hierarchical Multiple Kernel Clustering	https://ojs.aaai.org/index.php/AAAI/article/view/17051	https://ojs.aaai.org/index.php/AAAI/article/download/17051/16858	[ "Jiyuan Liu", "Xinwang Liu", "Siwei Wang", "Sihang Zhou", "Yuexiang Yang" ]	Current multiple kernel clustering algorithms compute a partition with the consensus kernel or graph learned from the pre-specified ones, while the emerging late fusion methods firstly construct multiple partitions from each kernel separately, and then obtain a consensus one with them. However, both of them directly di...	main	Machine Learning	10.1609/aaai.v35i10.17051	35	10	8671-8679	official	null	null
10.1609/aaai.v35i10.17052	Dynamically Grown Generative Adversarial Networks	https://ojs.aaai.org/index.php/AAAI/article/view/17052	https://ojs.aaai.org/index.php/AAAI/article/download/17052/16859	[ "Lanlan Liu", "Yuting Zhang", "Jia Deng", "Stefano Soatto" ]	Recent work introduced progressive network growing as a promising way to ease the training for large GANs, but the model design and architecture-growing strategy still remain under-explored and needs manual design for different image data. In this paper, we propose a method to dynamically grow a GAN during training, op...	main	Machine Learning	10.1609/aaai.v35i10.17052	35	10	8680-8687	official	2106.08505	title_snapshot
10.1609/aaai.v35i10.17053	FLAME: Differentially Private Federated Learning in the Shuffle Model	https://ojs.aaai.org/index.php/AAAI/article/view/17053	https://ojs.aaai.org/index.php/AAAI/article/download/17053/16860	[ "Ruixuan Liu", "Yang Cao", "Hong Chen", "Ruoyang Guo", "Masatoshi Yoshikawa" ]	Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train a model without collecting users' raw data. To ensure users' privacy, differentially private federated learning has been intensively studied. The existing works are mainly based on the curator model or local model of dif...	main	Machine Learning	10.1609/aaai.v35i10.17053	35	10	8688-8696	official	2009.08063	title_snapshot
10.1609/aaai.v35i10.17054	Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision	https://ojs.aaai.org/index.php/AAAI/article/view/17054	https://ojs.aaai.org/index.php/AAAI/article/download/17054/16861	[ "Xingchao Liu", "Mao Ye", "Dengyong Zhou", "Qiang Liu" ]	We consider the post-training quantization problem, which discretizes the weights of pre-trained deep neural networks without re-training the model. We propose multipoint quantization, a quantization method that approximates a full-precision weight vector using a linear combination of multiple vectors of low-bit number...	main	Machine Learning	10.1609/aaai.v35i10.17054	35	10	8697-8705	official	2002.09049	title_snapshot
10.1609/aaai.v35i10.17055	Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17055	https://ojs.aaai.org/index.php/AAAI/article/download/17055/16862	[ "Yu Liu", "Lianghua Huang", "Pan Pan", "Bin Wang", "Yinghui Xu", "Rong Jin" ]	This paper presents a simple unsupervised visual representation learning method with a pretext task of discriminating all images in a dataset using a parametric, instance-level classifier. The overall framework is a replica of a supervised classification model, where semantic classes (e.g., dog, bird, and ship) are rep...	main	Machine Learning	10.1609/aaai.v35i10.17055	35	10	8706-8714	official	2102.04848	title_snapshot
10.1609/aaai.v35i10.17056	ROSITA: Refined BERT cOmpreSsion with InTegrAted techniques	https://ojs.aaai.org/index.php/AAAI/article/view/17056	https://ojs.aaai.org/index.php/AAAI/article/download/17056/16863	[ "Yuanxin Liu", "Zheng Lin", "Fengcheng Yuan" ]	Pre-trained language models of the BERT family have defined the state-of-the-arts in a wide range of NLP tasks. However, the performance of BERT-based models is mainly driven by the enormous amount of parameters, which hinders their application to resource-limited scenarios. Faced with this problem, recent studies have...	main	Machine Learning	10.1609/aaai.v35i10.17056	35	10	8715-8722	official	2103.11367	title_snapshot
10.1609/aaai.v35i10.17057	Task Aligned Generative Meta-learning for Zero-shot Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17057	https://ojs.aaai.org/index.php/AAAI/article/download/17057/16864	[ "Zhe Liu", "Yun Li", "Lina Yao", "Xianzhi Wang", "Guodong Long" ]	Zero-shot learning (ZSL) refers to the problem of learning to classify instances from novel classes (unseen) that are absent in the training set (seen). Most ZSL methods infer the correlation between visual features and attributes to train the classifier for unseen classes. They may have a strong bias towards seen clas...	main	Machine Learning	10.1609/aaai.v35i10.17057	35	10	8723-8731	official	2103.02185	title_snapshot
10.1609/aaai.v35i10.17058	Learning from eXtreme Bandit Feedback	https://ojs.aaai.org/index.php/AAAI/article/view/17058	https://ojs.aaai.org/index.php/AAAI/article/download/17058/16865	[ "Romain Lopez", "Inderjit S. Dhillon", "Michael I. Jordan" ]	We study the problem of batch learning from bandit feedback in the setting of extremely large action spaces. Learning from extreme bandit feedback is ubiquitous in recommendation systems, in which billions of decisions are made over sets consisting of millions of choices in a single day, yielding massive observational ...	main	Machine Learning	10.1609/aaai.v35i10.17058	35	10	8732-8740	official	2009.12947	title_snapshot
10.1609/aaai.v35i10.17059	Improving Causal Discovery By Optimal Bayesian Network Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17059	https://ojs.aaai.org/index.php/AAAI/article/download/17059/16866	[ "Ni Y Lu", "Kun Zhang", "Changhe Yuan" ]	Many widely-used causal discovery methods such as Greedy Equivalent Search (GES), although with asymptotic correctness guarantees, have been reported to produce sub-optimal solutions on finite data, or when the causal faithfulness condition is violated. The constraint-based procedure with Boolean satisfiability (SAT) s...	main	Machine Learning	10.1609/aaai.v35i10.17059	35	10	8741-8748	official	null	null
10.1609/aaai.v35i10.17060	Stochastic Graphical Bandits with Adversarial Corruptions	https://ojs.aaai.org/index.php/AAAI/article/view/17060	https://ojs.aaai.org/index.php/AAAI/article/download/17060/16867	[ "Shiyin Lu", "Guanghui Wang", "Lijun Zhang" ]	We study bandits with graph-structured feedback, where a learner repeatedly selects an arm and then observes rewards of the chosen arm as well as its neighbors in the feedback graph. Existing work on graphical bandits assumes either stochastic rewards or adversarial rewards, both of which are extremes and appear rarely...	main	Machine Learning	10.1609/aaai.v35i10.17060	35	10	8749-8757	official	null	null
10.1609/aaai.v35i10.17061	Stochastic Bandits with Graph Feedback in Non-Stationary Environments	https://ojs.aaai.org/index.php/AAAI/article/view/17061	https://ojs.aaai.org/index.php/AAAI/article/download/17061/16868	[ "Shiyin Lu", "Yao Hu", "Lijun Zhang" ]	We study a variant of stochastic bandits where the feedback model is specified by a graph. In this setting, after playing an arm, one can observe rewards of not only the played arm but also other arms that are adjacent to the played arm in the graph. Most of the existing work assumes the reward distributions are statio...	main	Machine Learning	10.1609/aaai.v35i10.17061	35	10	8758-8766	official	null	null
10.1609/aaai.v35i10.17062	Decentralized Policy Gradient Descent Ascent for Safe Multi-Agent Reinforcement Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17062	https://ojs.aaai.org/index.php/AAAI/article/download/17062/16869	[ "Songtao Lu", "Kaiqing Zhang", "Tianyi Chen", "Tamer Başar", "Lior Horesh" ]	This paper deals with distributed reinforcement learning problems with safety constraints. In particular, we consider that a team of agents cooperate in a shared environment, where each agent has its individual reward function and safety constraints that involve all agents' joint actions. As such, the agents aim to max...	main	Machine Learning	10.1609/aaai.v35i10.17062	35	10	8767-8775	official	null	null
10.1609/aaai.v35i10.17023	Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting	https://ojs.aaai.org/index.php/AAAI/article/view/17023	https://ojs.aaai.org/index.php/AAAI/article/download/17023/16830	[ "Longyuan Li", "Jihai Zhang", "Junchi Yan", "Yaohui Jin", "Yunhao Zhang", "Yanjie Duan", "Guangjian Tian" ]	Time-series is ubiquitous across applications, such as transportation, finance and healthcare. Time-series is often influenced by external factors, especially in the form of asynchronous events, making forecasting difficult. However, existing models are mainly designated for either synchronous time-series or asynchrono...	main	Machine Learning	10.1609/aaai.v35i10.17023	35	10	8420-8428	official	2102.00431	title_snapshot
10.1609/aaai.v35i10.17024	Bayesian Distributional Policy Gradients	https://ojs.aaai.org/index.php/AAAI/article/view/17024	https://ojs.aaai.org/index.php/AAAI/article/download/17024/16831	[ "Luchen Li", "A. Aldo Faisal" ]	Distributional Reinforcement Learning (RL) maintains the entire probability distribution of the reward-to-go, i.e. the return, providing more learning signals that account for the uncertainty associated with policy performance, which may be beneficial for trading off exploration and exploitation and policy learning in ...	main	Machine Learning	10.1609/aaai.v35i10.17024	35	10	8429-8437	official	2103.11265	title_snapshot
10.1609/aaai.v35i10.17025	Learning Graph Neural Networks with Approximate Gradient Descent	https://ojs.aaai.org/index.php/AAAI/article/view/17025	https://ojs.aaai.org/index.php/AAAI/article/download/17025/16832	[ "Qunwei Li", "Shaofeng Zou", "Wenliang Zhong" ]	The first provably efficient algorithm for learning graph neural networks (GNNs) with one hidden layer for node information convolution is provided in this paper. Two types of GNNs are investigated, depending on whether labels are attached to nodes or graphs. A comprehensive framework for designing and analyzing conver...	main	Machine Learning	10.1609/aaai.v35i10.17025	35	10	8438-8446	official	2012.03429	title_snapshot
10.1609/aaai.v35i10.17026	Multi-View Representation Learning with Manifold Smoothness	https://ojs.aaai.org/index.php/AAAI/article/view/17026	https://ojs.aaai.org/index.php/AAAI/article/download/17026/16833	[ "Shu Li", "Wei Wang", "Wen-Tao Li", "Pan Chen" ]	Multi-view representation learning attempts to learn a representation from multiple views and most existing methods are unsupervised. However, representation learned only from unlabeled data may not be discriminative enough for further applications (e.g., clustering and classification). For this reason, semi-supervised...	main	Machine Learning	10.1609/aaai.v35i10.17026	35	10	8447-8454	official	null	null
10.1609/aaai.v35i10.17027	Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation	https://ojs.aaai.org/index.php/AAAI/article/view/17027	https://ojs.aaai.org/index.php/AAAI/article/download/17027/16834	[ "Shuang Li", "Fangrui Lv", "Binhui Xie", "Chi Harold Liu", "Jian Liang", "Chen Qin" ]	Unsupervised domain adaptation challenges the problem of transferring knowledge from a well-labelled source domain to an unlabelled target domain. Recently, adversarial learning with bi-classifier has been proven effective in pushing cross-domain distributions close. Prior approaches typically leverage the disagreement...	main	Machine Learning	10.1609/aaai.v35i10.17027	35	10	8455-8464	official	2012.06995	title_snapshot
10.1609/aaai.v35i10.17028	Sublinear Classical and Quantum Algorithms for General Matrix Games	https://ojs.aaai.org/index.php/AAAI/article/view/17028	https://ojs.aaai.org/index.php/AAAI/article/download/17028/16835	[ "Tongyang Li", "Chunhao Wang", "Shouvanik Chakrabarti", "Xiaodi Wu" ]	We investigate sublinear classical and quantum algorithms for matrix games, a fundamental problem in optimization and machine learning, with provable guarantees. Given a matrix, sublinear algorithms for the matrix game were previously known only for two special cases: (1) the maximizing vectors live in the L1-norm unit...	main	Machine Learning	10.1609/aaai.v35i10.17028	35	10	8465-8473	official	2012.06519	title_snapshot
10.1609/aaai.v35i10.17029	A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data	https://ojs.aaai.org/index.php/AAAI/article/view/17029	https://ojs.aaai.org/index.php/AAAI/article/download/17029/16836	[ "Xianfeng Li", "Weijie Chen", "Di Xie", "Shicai Yang", "Peng Yuan", "Shiliang Pu", "Yueting Zhuang" ]	Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the data privacy and the inefficiency of data transmission, it is impractical in real scenarios. Hence, it draws our eyes to optimize the netwo...	main	Machine Learning	10.1609/aaai.v35i10.17029	35	10	8474-8481	official	2012.05400	title_snapshot
10.1609/aaai.v35i10.17030	Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints	https://ojs.aaai.org/index.php/AAAI/article/view/17030	https://ojs.aaai.org/index.php/AAAI/article/download/17030/16837	[ "Xin Li", "Xiangrui Li", "Deng Pan", "Dongxiao Zhu" ]	Convolutional neural networks (CNNs) have achieved state-of-the-art performance on various tasks in computer vision. However, recent studies demonstrate that these models are vulnerable to carefully crafted adversarial samples and suffer from a significant performance drop when predicting them. Many methods have been p...	main	Machine Learning	10.1609/aaai.v35i10.17030	35	10	8482-8490	official	2012.07688	title_snapshot
10.1609/aaai.v35i10.17031	MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements	https://ojs.aaai.org/index.php/AAAI/article/view/17031	https://ojs.aaai.org/index.php/AAAI/article/download/17031/16838	[ "Yang Li", "Yu Shen", "Jiawei Jiang", "Jinyang Gao", "Ce Zhang", "Bin Cui" ]	Hyperparameter optimization (HPO) is a fundamental problem in automatic machine learning (AutoML). However, due to the expensive evaluation cost of models (e.g., training deep learning models or training models on large datasets), vanilla Bayesian optimization (BO) is typically computationally infeasible. To alleviate ...	main	Machine Learning	10.1609/aaai.v35i10.17031	35	10	8491-8500	official	2012.03011	title_snapshot
10.1609/aaai.v35i10.17032	Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding	https://ojs.aaai.org/index.php/AAAI/article/view/17032	https://ojs.aaai.org/index.php/AAAI/article/download/17032/16839	[ "Yangyang Li", "Lin Kong", "Fanhua Shang", "Yuanyuan Liu", "Hongying Liu", "Zhouchen Lin" ]	Recently, the study on learned iterative shrinkage thresholding algorithm (LISTA) has attracted increasing attentions. A large number of experiments as well as some theories have proved the high efficiency of LISTA for solving sparse coding problems. However, existing LISTA methods are all serial connection. To address...	main	Machine Learning	10.1609/aaai.v35i10.17032	35	10	8501-8509	official	null	null
10.1609/aaai.v35i10.17033	One-shot Graph Neural Architecture Search with Dynamic Search Space	https://ojs.aaai.org/index.php/AAAI/article/view/17033	https://ojs.aaai.org/index.php/AAAI/article/download/17033/16840	[ "Yanxi Li", "Zean Wen", "Yunhe Wang", "Chang Xu" ]	Relying on the diverse graph convolution operations that have emerged in recent years, graph neural networks (GNNs) are shown to be powerful to deal with high-dimensional non-Euclidean domains, such as social networks or citation networks. Despite the tremendous human efforts been taken to explore new graph convolution...	main	Machine Learning	10.1609/aaai.v35i10.17033	35	10	8510-8517	official	null	null
10.1609/aaai.v35i10.17034	Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network	https://ojs.aaai.org/index.php/AAAI/article/view/17034	https://ojs.aaai.org/index.php/AAAI/article/download/17034/16841	[ "Yehao Li", "Yingwei Pan", "Ting Yao", "Jingwen Chen", "Tao Mei" ]	Despite having impressive vision-language (VL) pretraining with BERT-based encoder for VL understanding, the pretraining of a universal encoder-decoder for both VL understanding and generation remains challenging. The difficulty originates from the inherently different peculiarities of the two disciplines, e.g., VL und...	main	Machine Learning	10.1609/aaai.v35i10.17034	35	10	8518-8526	official	2101.11562	title_snapshot
10.1609/aaai.v35i10.17035	Online Optimal Control with Affine Constraints	https://ojs.aaai.org/index.php/AAAI/article/view/17035	https://ojs.aaai.org/index.php/AAAI/article/download/17035/16842	[ "Yingying Li", "Subhro Das", "Na Li" ]	This paper considers online optimal control with affine constraints on the states and actions under linear dynamics with bounded random disturbances. The system dynamics and constraints are assumed to be known and time invariant but the convex stage cost functions change adversarially. To solve this problem, we propose...	main	Machine Learning	10.1609/aaai.v35i10.17035	35	10	8527-8537	official	2010.04891	title_snapshot
10.1609/aaai.v35i10.17036	TRQ: Ternary Neural Networks With Residual Quantization	https://ojs.aaai.org/index.php/AAAI/article/view/17036	https://ojs.aaai.org/index.php/AAAI/article/download/17036/16843	[ "Yue Li", "Wenrui Ding", "Chunlei Liu", "Baochang Zhang", "Guodong Guo" ]	Ternary neural networks (TNNs) are potential for network acceleration by reducing the full-precision weights in network to ternary ones, e.g., {-1,0,1}. However, existing TNNs are mostly calculated based on rule-of-thumb quantization methods by simply thresholding operations, which causes a significant accuracy loss. I...	main	Machine Learning	10.1609/aaai.v35i10.17036	35	10	8538-8546	official	null	null
10.1609/aaai.v35i10.17037	Contrastive Clustering	https://ojs.aaai.org/index.php/AAAI/article/view/17037	https://ojs.aaai.org/index.php/AAAI/article/download/17037/16844	[ "Yunfan Li", "Peng Hu", "Zitao Liu", "Dezhong Peng", "Joey Tianyi Zhou", "Xi Peng" ]	In this paper, we propose an online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, the positive and negative instance pairs are constructed through data augmentations and then projected into a feat...	main	Machine Learning	10.1609/aaai.v35i10.17037	35	10	8547-8555	official	2009.09687	title_snapshot
10.1609/aaai.v35i10.17038	Longitudinal Deep Kernel Gaussian Process Regression	https://ojs.aaai.org/index.php/AAAI/article/view/17038	https://ojs.aaai.org/index.php/AAAI/article/download/17038/16845	[ "Junjie Liang", "Yanting Wu", "Dongkuan Xu", "Vasant G Honavar" ]	Gaussian processes offer an attractive framework for predictive modeling from longitudinal data, \ie irregularly sampled, sparse observations from a set of individuals over time. However, such methods have two key shortcomings: (i) They rely on ad hoc heuristics or expensive trial and error to choose the effective kern...	main	Machine Learning	10.1609/aaai.v35i10.17038	35	10	8556-8564	official	2005.11770	title_snapshot
10.1609/aaai.v35i10.17039	Large Norms of CNN Layers Do Not Hurt Adversarial Robustness	https://ojs.aaai.org/index.php/AAAI/article/view/17039	https://ojs.aaai.org/index.php/AAAI/article/download/17039/16846	[ "Youwei Liang", "Dong Huang" ]	Since the Lipschitz properties of convolutional neural networks (CNNs) are widely considered to be related to adversarial robustness, we theoretically characterize the L-1 norm and L-infinity norm of 2D multi-channel convolutional layers and provide efficient methods to compute the exact L-1 norm and L-infinity norm. B...	main	Machine Learning	10.1609/aaai.v35i10.17039	35	10	8565-8573	official	2009.08435	title_snapshot
10.1609/aaai.v35i10.17040	Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding	https://ojs.aaai.org/index.php/AAAI/article/view/17040	https://ojs.aaai.org/index.php/AAAI/article/download/17040/16847	[ "Siyu Liao", "Chunhua Deng", "Miao Yin", "Bo Yuan" ]	Recently deep neural networks have been successfully applied in channel coding to improve the decoding performance. However, the state-of-the-art neural channel decoders cannot achieve high decoding performance and low complexity simultaneously. To overcome this challenge, in this paper we propose doubly residual neura...	main	Machine Learning	10.1609/aaai.v35i10.17040	35	10	8574-8582	official	2102.03959	title_snapshot
10.1609/aaai.v35i10.17041	From Label Smoothing to Label Relaxation	https://ojs.aaai.org/index.php/AAAI/article/view/17041	https://ojs.aaai.org/index.php/AAAI/article/download/17041/16848	[ "Julian Lienen", "Eyke Hüllermeier" ]	Regularization of (deep) learning models can be realized at the model, loss, or data level. As a technique somewhere in-between loss and data, label smoothing turns deterministic class labels into probability distributions, for example by uniformly distributing a certain part of the probability mass over all classes. A...	main	Machine Learning	10.1609/aaai.v35i10.17041	35	10	8583-8591	official	null	null
10.1609/aaai.v35i10.17042	Sample Selection for Universal Domain Adaptation	https://ojs.aaai.org/index.php/AAAI/article/view/17042	https://ojs.aaai.org/index.php/AAAI/article/download/17042/16849	[ "Omri Lifshitz", "Lior Wolf" ]	This paper studies the problem of unsupervised domain adaption in the universal scenario, in which only some of the classes are shared between the source and target domains. We present a scoring scheme that is effective in identifying the samples of the shared classes. The score is used to select samples in the target ...	main	Machine Learning	10.1609/aaai.v35i10.17042	35	10	8592-8600	official	2001.05071	title_judge
10.1609/aaai.v35i11.17223	Adaptive Verifiable Training Using Pairwise Class Similarity	https://ojs.aaai.org/index.php/AAAI/article/view/17223	https://ojs.aaai.org/index.php/AAAI/article/download/17223/17030	[ "Shiqi Wang", "Kevin Eykholt", "Taesung Lee", "Jiyong Jang", "Ian Molloy" ]	Verifiable training has shown success in creating neural networks that are provably robust to a given amount of noise. However, despite only enforcing a single robustness criterion, its performance scales poorly with dataset complexity. On CIFAR10, a non-robust LeNet model has a 21.63% error rate, while a model created...	main	Machine Learning	10.1609/aaai.v35i11.17223	35	11	10201-10209	official	2012.07887	title_snapshot
10.1609/aaai.v35i11.17224	Adaptive Algorithms for Multi-armed Bandit with Composite and Anonymous Feedback	https://ojs.aaai.org/index.php/AAAI/article/view/17224	https://ojs.aaai.org/index.php/AAAI/article/download/17224/17031	[ "Siwei Wang", "Haoyun Wang", "Longbo Huang" ]	We study the multi-armed bandit (MAB) problem with composite and anonymous feedback. In this model, the reward of pulling an arm spreads over a period of time (we call this period as reward interval) and the player receives partial rewards of the action, convoluted with rewards from pulling other arms, successively. Ex...	main	Machine Learning	10.1609/aaai.v35i11.17224	35	11	10210-10217	official	2012.07048	title_snapshot
10.1609/aaai.v35i11.17225	Harmonized Dense Knowledge Distillation Training for Multi-Exit Architectures	https://ojs.aaai.org/index.php/AAAI/article/view/17225	https://ojs.aaai.org/index.php/AAAI/article/download/17225/17032	[ "Xinglu Wang", "Yingming Li" ]	Multi-exit architectures, in which a sequence of intermediate classifiers are introduced at different depths of the feature layers, perform adaptive computation by early exiting ``easy" samples to speed up the inference. In this paper, a novel Harmonized Dense Knowledge Distillation (HDKD) training method for multi-exi...	main	Machine Learning	10.1609/aaai.v35i11.17225	35	11	10218-10226	official	null	null
10.1609/aaai.v35i11.17226	Tied Block Convolution: Leaner and Better CNNs with Shared Thinner Filters	https://ojs.aaai.org/index.php/AAAI/article/view/17226	https://ojs.aaai.org/index.php/AAAI/article/download/17226/17033	[ "Xudong Wang", "Stella X. Yu" ]	Convolution is the main building block of a convolutional neural network (CNN). We observe that an optimized CNN often has highly correlated filters as the number of channels increases with depth, reducing the expressive power of feature representations. We propose Tied Block Convolution (TBC) that shares the same thin...	main	Machine Learning	10.1609/aaai.v35i11.17226	35	11	10227-10235	official	2009.12021	title_snapshot
10.1609/aaai.v35i11.17227	Deep Recurrent Belief Propagation Network for POMDPs	https://ojs.aaai.org/index.php/AAAI/article/view/17227	https://ojs.aaai.org/index.php/AAAI/article/download/17227/17034	[ "Yuhui Wang", "Xiaoyang Tan" ]	In many real-world sequential decision-making tasks, especially in continuous control like robotic control, it is rare that the observations are perfect, that is, the sensory data could be incomplete, noisy or even dynamically polluted due to the unexpected malfunctions or intrinsic low quality of the sensors. Previous...	main	Machine Learning	10.1609/aaai.v35i11.17227	35	11	10236-10244	official	null	null
10.1609/aaai.v35i11.17228	Data-Free Knowledge Distillation with Soft Targeted Transfer Set Synthesis	https://ojs.aaai.org/index.php/AAAI/article/view/17228	https://ojs.aaai.org/index.php/AAAI/article/download/17228/17035	[ "Zi Wang" ]	Knowledge distillation (KD) has proved to be an effective approach for deep neural network compression, which learns a compact network (student) by transferring the knowledge from a pre-trained, over-parameterized network (teacher). In traditional KD, the transferred knowledge is usually obtained by feeding training sa...	main	Machine Learning	10.1609/aaai.v35i11.17228	35	11	10245-10253	official	2104.04868	title_snapshot
10.1609/aaai.v35i11.17229	Incremental Embedding Learning via Zero-Shot Translation	https://ojs.aaai.org/index.php/AAAI/article/view/17229	https://ojs.aaai.org/index.php/AAAI/article/download/17229/17036	[ "Kun Wei", "Cheng Deng", "Xu Yang", "Maosen Li" ]	Modern deep learning methods have achieved great success in machine learning and computer vision fields by learning a set of pre-defined datasets. Howerver, these methods perform unsatisfactorily when applied into real-world situations. The reason of this phenomenon is that learning new tasks leads the trained model qu...	main	Machine Learning	10.1609/aaai.v35i11.17229	35	11	10254-10262	official	2012.15497	title_snapshot
10.1609/aaai.v35i11.17230	Gene Regulatory Network Inference as Relaxed Graph Matching	https://ojs.aaai.org/index.php/AAAI/article/view/17230	https://ojs.aaai.org/index.php/AAAI/article/download/17230/17037	[ "Deborah Weighill", "Marouen Ben Guebila", "Camila Lopes-Ramos", "Kimberly Glass", "John Quackenbush", "John Platig", "Rebekka Burkholz" ]	Bipartite network inference is a ubiquitous problem across disciplines. One important example in the field molecular biology is gene regulatory network inference. Gene regulatory networks are an instrumental tool aiding in the discovery of the molecular mechanisms driving diverse diseases, including cancer. However, on...	main	Machine Learning	10.1609/aaai.v35i11.17230	35	11	10263-10272	official	null	null
10.1609/aaai.v35i11.17231	Unified Tensor Framework for Incomplete Multi-view Clustering and Missing-view Inferring	https://ojs.aaai.org/index.php/AAAI/article/view/17231	https://ojs.aaai.org/index.php/AAAI/article/download/17231/17038	[ "Jie Wen", "Zheng Zhang", "Zhao Zhang", "Lei Zhu", "Lunke Fei", "Bob Zhang", "Yong Xu" ]	In this paper, we propose a novel method, referred to as incomplete multi-view tensor spectral clustering with missing-view inferring (IMVTSC-MVI) to address the challenging multi-view clustering problem with missing views. Different from the existing methods which commonly focus on exploring the certain information of...	main	Machine Learning	10.1609/aaai.v35i11.17231	35	11	10273-10281	official	null	null
10.1609/aaai.v35i11.17203	GraphMix: Improved Training of GNNs for Semi-Supervised Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17203	https://ojs.aaai.org/index.php/AAAI/article/download/17203/17010	[ "Vikas Verma", "Meng Qu", "Kenji Kawaguchi", "Alex Lamb", "Yoshua Bengio", "Juho Kannala", "Jian Tang" ]	We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object classification, whereby we propose to train a fully-connected network jointly with the graph neural network via parameter sharing and interpolation-based regularization. Further, we provide a theoretical analysis of how G...	main	Machine Learning	10.1609/aaai.v35i11.17203	35	11	10024-10032	official	1909.11715	title_snapshot
10.1609/aaai.v35i11.17204	PID-Based Approach to Adversarial Attacks	https://ojs.aaai.org/index.php/AAAI/article/view/17204	https://ojs.aaai.org/index.php/AAAI/article/download/17204/17011	[ "Chen Wan", "Biaohua Ye", "Fangjun Huang" ]	Adversarial attack can misguide the deep neural networks (DNNs) with adding small-magnitude perturbations to normal examples, which is mainly determined by the gradient of the loss function with respect to inputs. Previously, various strategies have been proposed to enhance the performance of adversarial attacks. Howev...	main	Machine Learning	10.1609/aaai.v35i11.17204	35	11	10033-10040	official	null	null
10.1609/aaai.v35i11.17205	Nearest Neighbor Classifier Embedded Network for Active Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17205	https://ojs.aaai.org/index.php/AAAI/article/download/17205/17012	[ "Fang Wan", "Tianning Yuan", "Mengying Fu", "Xiangyang Ji", "Qingming Huang", "Qixiang Ye" ]	Deep neural networks (DNNs) have been widely applied to active learning. Despite of its effectiveness, the generalization ability of the discriminative classifier (the softmax classifier) is questionable when there is a significant distribution bias between the labeled set and the unlabeled set. In this paper, we attem...	main	Machine Learning	10.1609/aaai.v35i11.17205	35	11	10041-10048	official	null	null
10.1609/aaai.v35i11.17206	Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17206	https://ojs.aaai.org/index.php/AAAI/article/download/17206/17013	[ "Sheng Wan", "Shirui Pan", "Jian Yang", "Chen Gong" ]	Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. As one of the most popular graph-based SSL approaches, the recently proposed Graph Convolutional Networks (GCNs) have gained remarkable progress by combining the sound...	main	Machine Learning	10.1609/aaai.v35i11.17206	35	11	10049-10057	official	2009.07111	title_snapshot
10.1609/aaai.v35i11.17207	Approximate Multiplication of Sparse Matrices with Limited Space	https://ojs.aaai.org/index.php/AAAI/article/view/17207	https://ojs.aaai.org/index.php/AAAI/article/download/17207/17014	[ "Yuanyu Wan", "Lijun Zhang" ]	Approximate matrix multiplication with limited space has received ever-increasing attention due to the emergence of large-scale applications. Recently, based on a popular matrix sketching algorithm---frequent directions, previous work has introduced co-occuring directions (COD) to reduce the approximation error for thi...	main	Machine Learning	10.1609/aaai.v35i11.17207	35	11	10058-10066	official	2009.03527	title_snapshot
10.1609/aaai.v35i11.17208	Projection-free Online Learning in Dynamic Environments	https://ojs.aaai.org/index.php/AAAI/article/view/17208	https://ojs.aaai.org/index.php/AAAI/article/download/17208/17015	[ "Yuanyu Wan", "Bo Xue", "Lijun Zhang" ]	To efficiently solve high-dimensional problems with complicated constraints, projection-free online learning has received ever-increasing research interest. However, previous studies either focused on static regret that is not suitable for dynamic environments, or only established the dynamic regret bound under the smo...	main	Machine Learning	10.1609/aaai.v35i11.17208	35	11	10067-10075	official	null	null
10.1609/aaai.v35i11.17209	Projection-free Online Learning over Strongly Convex Sets	https://ojs.aaai.org/index.php/AAAI/article/view/17209	https://ojs.aaai.org/index.php/AAAI/article/download/17209/17016	[ "Yuanyu Wan", "Lijun Zhang" ]	To efficiently solve online problems with complicated constraints, projection-free algorithms including online frank-wolfe (OFW) and its variants have received significant interest recently. However, in the general case, existing efficient projection-free algorithms only achieved the regret bound of O(T^{3/4}), which i...	main	Machine Learning	10.1609/aaai.v35i11.17209	35	11	10076-10084	official	2010.08177	title_snapshot
10.1609/aaai.v35i11.17210	Multi-View Information-Bottleneck Representation Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17210	https://ojs.aaai.org/index.php/AAAI/article/download/17210/17017	[ "Zhibin Wan", "Changqing Zhang", "Pengfei Zhu", "Qinghua Hu" ]	In real-world applications, clustering or classification can usually be improved by fusing information from different views. Therefore, unsupervised representation learning on multi-view data becomes a compelling topic in machine learning. In this paper, we propose a novel and flexible unsupervised multi-view represent...	main	Machine Learning	10.1609/aaai.v35i11.17210	35	11	10085-10092	official	null	null
10.1609/aaai.v35i11.17211	Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks	https://ojs.aaai.org/index.php/AAAI/article/view/17211	https://ojs.aaai.org/index.php/AAAI/article/download/17211/17018	[ "Binghui Wang", "Jinyuan Jia", "Neil Zhenqiang Gong" ]	Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user’s private attribute inference in social networks, and community detection. Various methods such as pairwise Markov Random Fields (pMRF) and graph neural networks were devel...	main	Machine Learning	10.1609/aaai.v35i11.17211	35	11	10093-10101	official	2012.13085	title_snapshot
10.1609/aaai.v35i11.17212	Quantum Exploration Algorithms for Multi-Armed Bandits	https://ojs.aaai.org/index.php/AAAI/article/view/17212	https://ojs.aaai.org/index.php/AAAI/article/download/17212/17019	[ "Daochen Wang", "Xuchen You", "Tongyang Li", "Andrew M. Childs" ]	Identifying the best arm of a multi-armed bandit is a central problem in bandit optimization. We study a quantum computational version of this problem with coherent oracle access to states encoding the reward probabilities of each arm as quantum amplitudes. Specifically, we provide an algorithm to find the best arm wit...	main	Machine Learning	10.1609/aaai.v35i11.17212	35	11	10102-10110	official	2007.07049	title_snapshot
10.1609/aaai.v35i11.17213	Learning from Noisy Labels with Complementary Loss Functions	https://ojs.aaai.org/index.php/AAAI/article/view/17213	https://ojs.aaai.org/index.php/AAAI/article/download/17213/17020	[ "Deng-Bao Wang", "Yong Wen", "Lujia Pan", "Min-Ling Zhang" ]	Recent researches reveal that deep neural networks are sensitive to label noises hence leading to poor generalization performance in some tasks. Although different robust loss functions have been proposed to remedy this issue, they suffer from an underfitting problem, thus are not sufficient to learn accurate models. O...	main	Machine Learning	10.1609/aaai.v35i11.17213	35	11	10111-10119	official	null	null
10.1609/aaai.v35i11.17214	Debiasing Evaluations That Are Biased by Evaluations	https://ojs.aaai.org/index.php/AAAI/article/view/17214	https://ojs.aaai.org/index.php/AAAI/article/download/17214/17021	[ "Jingyan Wang", "Ivan Stelmakh", "Yuting Wei", "Nihar B. Shah" ]	It is common to evaluate a set of items by soliciting people to rate them. For example, universities ask students to rate the teaching quality of their instructors, and conference organizers ask authors of submissions to evaluate the quality of the reviews. However, in these applications, students often give a higher r...	main	Machine Learning	10.1609/aaai.v35i11.17214	35	11	10120-10128	official	2012.00714	title_snapshot
10.1609/aaai.v35i11.17215	Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion	https://ojs.aaai.org/index.php/AAAI/article/view/17215	https://ojs.aaai.org/index.php/AAAI/article/download/17215/17022	[ "Jinpeng Wang", "Yuting Gao", "Ke Li", "Jianguo Hu", "Xinyang Jiang", "Xiaowei Guo", "Rongrong Ji", "Xing Sun" ]	One significant factor we expect the video representation learning to capture, especially in contrast with the image representation learning, is the object motion. However, we found that in the current mainstream video datasets, some action categories are highly related with the scene where the action happens, making t...	main	Machine Learning	10.1609/aaai.v35i11.17215	35	11	10129-10137	official	2009.05757	title_snapshot
10.1609/aaai.v35i11.17216	Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation	https://ojs.aaai.org/index.php/AAAI/article/view/17216	https://ojs.aaai.org/index.php/AAAI/article/download/17216/17023	[ "Kaihong Wang", "Chenhongyi Yang", "Margrit Betke" ]	Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixel-level annotation for synthetic data. The most common approaches try to generate images or features mimicking the distribution in the target domain while preserving the semantic contents in the source d...	main	Machine Learning	10.1609/aaai.v35i11.17216	35	11	10138-10146	official	2009.08610	title_snapshot
10.1609/aaai.v35i11.17217	Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path	https://ojs.aaai.org/index.php/AAAI/article/view/17217	https://ojs.aaai.org/index.php/AAAI/article/download/17217/17024	[ "Lili Wang", "Chongyang Gao", "Chenghan Huang", "Ruibo Liu", "Weicheng Ma", "Soroush Vosoughi" ]	Networks found in the real-world are numerous and varied. A common type of network is the heterogeneous network, where the nodes (and edges) can be of different types. Accordingly, there have been efforts at learning representations of these heterogeneous networks in low-dimensional space. However, most of the existing...	main	Machine Learning	10.1609/aaai.v35i11.17217	35	11	10147-10155	official	2106.09923	title_snapshot
10.1609/aaai.v35i11.17218	Adversarial Linear Contextual Bandits with Graph-Structured Side Observations	https://ojs.aaai.org/index.php/AAAI/article/view/17218	https://ojs.aaai.org/index.php/AAAI/article/download/17218/17025	[ "Lingda Wang", "Bingcong Li", "Huozhi Zhou", "Georgios B. Giannakis", "Lav R. Varshney", "Zhizhen Zhao" ]	This paper studies the adversarial graphical contextual bandits, a variant of adversarial multi-armed bandits that leverage two categories of the most common side information: contexts and side observations. In this setting, a learning agent repeatedly chooses from a set of K actions after being presented with a d-dime...	main	Machine Learning	10.1609/aaai.v35i11.17218	35	11	10156-10164	official	2012.05756	title_snapshot
10.1609/aaai.v35i11.17219	Addressing Class Imbalance in Federated Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17219	https://ojs.aaai.org/index.php/AAAI/article/download/17219/17026	[ "Lixu Wang", "Shichao Xu", "Xiao Wang", "Qi Zhu" ]	Federated learning (FL) is a promising approach for training decentralized data located on local client devices while improving efficiency and privacy. However, the distribution and quantity of the training data on the clients' side may lead to significant challenges such as class imbalance and non-IID (non-independent...	main	Machine Learning	10.1609/aaai.v35i11.17219	35	11	10165-10173	official	2008.06217	title_snapshot
10.1609/aaai.v35i11.17220	Contrastive Transformation for Self-supervised Correspondence Learning	https://ojs.aaai.org/index.php/AAAI/article/view/17220	https://ojs.aaai.org/index.php/AAAI/article/download/17220/17027	[ "Ning Wang", "Wengang Zhou", "Houqiang Li" ]	In this paper, we focus on the self-supervised learning of visual correspondence using unlabeled videos in the wild. Our method simultaneously considers intra- and inter-video representation associations for reliable correspondence estimation. The intra-video learning transforms the image contents across frames within ...	main	Machine Learning	10.1609/aaai.v35i11.17220	35	11	10174-10182	official	2012.05057	title_snapshot
10.1609/aaai.v35i11.17221	Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model	https://ojs.aaai.org/index.php/AAAI/article/view/17221	https://ojs.aaai.org/index.php/AAAI/article/download/17221/17028	[ "Qizhou Wang", "Bo Han", "Tongliang Liu", "Gang Niu", "Jian Yang", "Chen Gong" ]	The drastic increase of data quantity often brings the severe decrease of data quality, such as incorrect label annotations. It poses a great challenge for robustly training Deep Neural Networks (DNNs). Existing learning methods with label noise either employ ad-hoc heuristics or restrict to specific noise assumptions....	main	Machine Learning	10.1609/aaai.v35i11.17221	35	11	10183-10191	official	2101.05467	title_snapshot
10.1609/aaai.v35i11.17222	Learning with Group Noise	https://ojs.aaai.org/index.php/AAAI/article/view/17222	https://ojs.aaai.org/index.php/AAAI/article/download/17222/17029	[ "Qizhou Wang", "Jiangchao Yao", "Chen Gong", "Tongliang Liu", "Mingming Gong", "Hongxia Yang", "Bo Han" ]	Machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels. However, the group noise, which is para...	main	Machine Learning	10.1609/aaai.v35i11.17222	35	11	10192-10200	official	2103.09468	title_snapshot
10.1609/aaai.v35i11.17183	Gradient Descent Averaging and Primal-dual Averaging for Strongly Convex Optimization	https://ojs.aaai.org/index.php/AAAI/article/view/17183	https://ojs.aaai.org/index.php/AAAI/article/download/17183/16990	[ "Wei Tao", "Wei Li", "Zhisong Pan", "Qing Tao" ]	Averaging scheme has attracted extensive attention in deep learning as well as traditional machine learning. It achieves theoretically optimal convergence and also improves the empirical model performance. However, there is still a lack of sufficient convergence analysis for strongly convex optimization. Typically, the...	main	Machine Learning	10.1609/aaai.v35i11.17183	35	11	9843-9850	official	2012.14558	title_snapshot
10.1609/aaai.v35i11.17184	Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm	https://ojs.aaai.org/index.php/AAAI/article/view/17184	https://ojs.aaai.org/index.php/AAAI/article/download/17184/16991	[ "Akira Terauchi", "Naoki Mori" ]	Data augmentation is one of the most effective ways to stabilize learning by improving the generalization of machine-learning models. In recent years, automatic data augmentation methods, such as AutoAugment or Fast AutoAugment have been attracting attention; and these methods improved the results of image classificati...	main	Machine Learning	10.1609/aaai.v35i11.17184	35	11	9851-9858	official	null	null
10.1609/aaai.v35i11.17185	Semi-Supervised Knowledge Amalgamation for Sequence Classification	https://ojs.aaai.org/index.php/AAAI/article/view/17185	https://ojs.aaai.org/index.php/AAAI/article/download/17185/16992	[ "Jidapa Thadajarassiri", "Thomas Hartvigsen", "Xiangnan Kong", "Elke A Rundensteiner" ]	Sequence classification is essential for domains from medical diagnosis to online advertising. In these settings, data are typically proprietary, and annotations are expensive to acquire. Often times, so few annotations are available that training a robust model from scratch is impractical. Recently, knowledge amalgama...	main	Machine Learning	10.1609/aaai.v35i11.17185	35	11	9859-9867	official	null	null
10.1609/aaai.v35i11.17186	Online Non-Monotone DR-Submodular Maximization	https://ojs.aaai.org/index.php/AAAI/article/view/17186	https://ojs.aaai.org/index.php/AAAI/article/download/17186/16993	[ "Nguyễn Kim Thắng", "Abhinav Srivastav" ]	In this paper, we study fundamental problems of maximizing DR-submodular continuous functions that have real-world applications in the domain of machine learning, economics, operations research and communication systems. It captures a subclass of non-convex optimization that provides both theoretical and practical guar...	main	Machine Learning	10.1609/aaai.v35i11.17186	35	11	9868-9876	official	1909.11426	title_snapshot
10.1609/aaai.v35i11.17187	Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain	https://ojs.aaai.org/index.php/AAAI/article/view/17187	https://ojs.aaai.org/index.php/AAAI/article/download/17187/16994	[ "Jinyu Tian", "Jiantao Zhou", "Yuanman Li", "Jia Duan" ]	Deep neural networks (DNNs) have been shown to be vulnerable against adversarial examples (AEs), which are maliciously designed to cause dramatic model output errors. In this work, we reveal that normal examples (NEs) are insensitive to the fluctuations occurring at the highly-curved region of the decision boundary, wh...	main	Machine Learning	10.1609/aaai.v35i11.17187	35	11	9877-9885	official	2103.04302	title_snapshot
10.1609/aaai.v35i11.17188	Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration	https://ojs.aaai.org/index.php/AAAI/article/view/17188	https://ojs.aaai.org/index.php/AAAI/article/download/17188/16995	[ "Christian Tomani", "Florian Buettner" ]	To facilitate a wide-spread acceptance of AI systems guiding decision making in real-world applications, trustworthiness of deployed models is key. That is, it is crucial for predictive models to be uncertainty-aware and yield well-calibrated (and thus trustworthy) predictions for both in-domain samples as well as unde...	main	Machine Learning	10.1609/aaai.v35i11.17188	35	11	9886-9896	official	2012.10923	title_snapshot
10.1609/aaai.v35i11.17189	Meta Learning for Causal Direction	https://ojs.aaai.org/index.php/AAAI/article/view/17189	https://ojs.aaai.org/index.php/AAAI/article/download/17189/16996	[ "Jean-François Ton", "Dino Sejdinovic", "Kenji Fukumizu" ]	The inaccessibility of controlled randomized trials due to inherent constraints in many fields of science has been a fundamental issue in causal inference. In this paper, we focus on distinguishing the cause from effect in the bivariate setting under limited observational data. Based on recent developments in meta lear...	main	Machine Learning	10.1609/aaai.v35i11.17189	35	11	9897-9905	official	2007.02809	title_snapshot
10.1609/aaai.v35i11.17190	Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior	https://ojs.aaai.org/index.php/AAAI/article/view/17190	https://ojs.aaai.org/index.php/AAAI/article/download/17190/16997	[ "Anh Tong", "Toan M Tran", "Hung Bui", "Jaesik Choi" ]	Choosing a proper set of kernel functions is an important problem in learning Gaussian Process (GP) models since each kernel structure has different model complexity and data fitness. Recently, automatic kernel composition methods provide not only accurate prediction but also attractive interpretability through search-...	main	Machine Learning	10.1609/aaai.v35i11.17190	35	11	9906-9914	official	2012.11339	title_snapshot
10.1609/aaai.v35i11.17191	Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems	https://ojs.aaai.org/index.php/AAAI/article/view/17191	https://ojs.aaai.org/index.php/AAAI/article/download/17191/16998	[ "Anh Tong", "Jaesik Choi" ]	Recent advances in Deep Gaussian Processes (DGPs) show the potential to have more expressive representation than that of traditional Gaussian Processes (GPs). However, there exists a pathology of deep Gaussian processes that their learning capacities reduce significantly when the number of layers increases. In this pap...	main	Machine Learning	10.1609/aaai.v35i11.17191	35	11	9915-9922	official	2010.09301	title_snapshot
10.1609/aaai.v35i11.17192	Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods	https://ojs.aaai.org/index.php/AAAI/article/view/17192	https://ojs.aaai.org/index.php/AAAI/article/download/17192/16999	[ "Nicholay Topin", "Stephanie Milani", "Fei Fang", "Manuela Veloso" ]	Current work in explainable reinforcement learning generally produces policies in the form of a decision tree over the state space. Such policies can be used for formal safety verification, agent behavior prediction, and manual inspection of important features. However, existing approaches fit a decision tree after tra...	main	Machine Learning	10.1609/aaai.v35i11.17192	35	11	9923-9931	official	2102.13045	title_snapshot
10.1609/aaai.v35i11.17193	Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach	https://ojs.aaai.org/index.php/AAAI/article/view/17193	https://ojs.aaai.org/index.php/AAAI/article/download/17193/17000	[ "Cuong Tran", "Ferdinando Fioretto", "Pascal Van Hentenryck" ]	A critical concern in data-driven decision making is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of the sensitive attributes is essential, while, in practice, these attributes may not b...	main	Machine Learning	10.1609/aaai.v35i11.17193	35	11	9932-9939	official	2009.12562	title_snapshot
10.1609/aaai.v35i11.17194	Learning Adjustment Sets from Observational and Limited Experimental Data	https://ojs.aaai.org/index.php/AAAI/article/view/17194	https://ojs.aaai.org/index.php/AAAI/article/download/17194/17001	[ "Sofia Triantafillou", "Greg Cooper" ]	Estimating causal effects from observational data is not always possible due to confounding. Identifying a set of appropriate covariates (adjustment set) and adjusting for their influence can remove confounding bias; however, such a set is often not identifiable from observational data alone. Experimental data allow un...	main	Machine Learning	10.1609/aaai.v35i11.17194	35	11	9940-9948	official	2005.08749	title_snapshot
10.1609/aaai.v35i11.17195	*-CFQ: Analyzing the Scalability of Machine Learning on a Compositional Task	https://ojs.aaai.org/index.php/AAAI/article/view/17195	https://ojs.aaai.org/index.php/AAAI/article/download/17195/17002	[ "Dmitry Tsarkov", "Tibor Tihon", "Nathan Scales", "Nikola Momchev", "Danila Sinopalnikov", "Nathanael Schärli" ]	We present *-CFQ ("star-CFQ"): a suite of large-scale datasets of varying scope based on the CFQ semantic parsing benchmark, designed for principled investigation of the scalability of machine learning systems in a realistic compositional task setting. Using this suite, we conduct a series of experiments investigating ...	main	Machine Learning	10.1609/aaai.v35i11.17195	35	11	9949-9957	official	2012.08266	title_snapshot
10.1609/aaai.v35i11.17196	Toward Robust Long Range Policy Transfer	https://ojs.aaai.org/index.php/AAAI/article/view/17196	https://ojs.aaai.org/index.php/AAAI/article/download/17196/17003	[ "Wei-Cheng Tseng", "Jin-Siang Lin", "Yao-Min Feng", "Min Sun" ]	Humans can master a new task within a few trials by drawing upon skills acquired through prior experience. To mimic this capability, hierarchical models combining primitive policies learned from prior tasks have been proposed. However, these methods fall short comparing to the human's range of transferability. We propo...	main	Machine Learning	10.1609/aaai.v35i11.17196	35	11	9958-9966	official	2103.02957	title_snapshot
10.1609/aaai.v35i11.17197	Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks	https://ojs.aaai.org/index.php/AAAI/article/view/17197	https://ojs.aaai.org/index.php/AAAI/article/download/17197/17004	[ "Russell Tsuchida", "Tim Pearce", "Chris Van der Heide", "Fred Roosta", "Marcus Gallagher" ]	Analysing and computing with Gaussian processes arising from infinitely wide neural networks has recently seen a resurgence in popularity. Despite this, many explicit covariance functions of networks with activation functions used in modern networks remain unknown. Furthermore, while the kernels of deep networks can be...	main	Machine Learning	10.1609/aaai.v35i11.17197	35	11	9967-9977	official	2002.08517	title_snapshot

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