AAAI 2020

1,794 papers

Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack

Ziwei Guan, Kaiyi Ji, Donald J. Bucci Jr., Timothy Y. Hu, Joseph Palombo

AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows

Aditya Grover, Christopher Chute, Rui Shu, ZHANGJIE CAO, Stefano Ermon

Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development

Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Jinfeng Yi

Online Metric Learning for Multi-Label Classification

Xiuwen Gong, Dong Yuan, Wei Bao

Robust Gradient-Based Markov Subsampling

Tieliang Gong, Quanhan Xi, Chen Xu

Adversarially Robust Distillation

Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein

Diachronic Embedding for Temporal Knowledge Graph Completion

Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, Pascal Poupart

Reinforcement Learning with Non-Markovian Rewards

Maor Gaon, Ronen Brafman

Modeling Dialogues with Hashcode Representations: A Nonparametric Approach

Sahil Garg, Irina Rish, Guillermo Cecchi, Palash Goyal, Sarik Ghazarian

Improved Algorithms for Conservative Exploration in Bandits

Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta

Revisiting Bilinear Pooling: A Coding Perspective

Zhi Gao, Yuwei Wu, Xiaoxun Zhang, Jindou Dai, Yunde Jia

A Multi-Channel Neural Graphical Event Model with Negative Evidence

Tian Gao, Dharmashankar Subramanian, Karthikeyan Shanmugam, Debarun Bhattacharjya, Nicholas Mattei

Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis

Quanxue Gao, Huanhuan Lian, Qianqian Wang, Gan Sun

Tensor-SVD Based Graph Learning for Multi-View Subspace Clustering

Quanxue Gao, Wei Xia, Zhizhen Wan, Deyan Xie, Pu Zhang

Infinity Learning: Learning Markov Chains from Aggregate Steady-State Observations

Jianfei Gao, Mohamed A. Zahran, Amit Sheoran, Sonia Fahmy, Bruno Ribeiro

Adaptive Convolutional ReLUs

Hongyang Gao, Lei Cai, Shuiwang Ji

On the Parameterized Complexity of Clustering Incomplete Data into Subspaces of Small Rank

Robert Ganian, Iyad Kanj, Sebastian Ordyniak, Stefan Szeider

Fast and Deep Graph Neural Networks

Claudio Gallicchio, Alessio Micheli

Induction of Subgoal Automata for Reinforcement Learning

Daniel Furelos-Blanco, Mark Law, Alessandra Russo, Krysia Broda, Anders Jonsson

Training Decision Trees as Replacement for Convolution Layers

Wolfgang Fuhl, Gjergji Kasneci, Wolfgang Rosenstiel, Enkeljda Kasneci

Learning Triple Embeddings from Knowledge Graphs

Valeria Fionda, Giuseppe Pirrò

Privacy-Preserving Gaussian Process Regression – A Modular Approach to the Application of Homomorphic Encryption

Peter Fenner, Edward Pyzer-Knapp

Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness

Huijie Feng, Chunpeng Wu, Guoyang Chen, Weifeng Zhang, Yang Ning

Distributionally Robust Counterfactual Risk Minimization

Louis Faury, Ugo Tanielian, Elvis DOHMATOB, Elena Smirnova, Flavian Vasile

Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning

Jicong Fan, Yuqian Zhang, Madeleine Udell

Unsupervised Metric Learning with Synthetic Examples

Ujjal Kr Dutta, Mehrtash Harandi, C. Chandra Sekhar

An Information-Theoretic Quantification of Discrimination with Exempt Features

Sanghamitra Dutta, Praveen Venkatesh, Piotr Mardziel, Anupam Datta, Pulkit Grover

On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning

Aritra Dutta, El Houcine Bergou, Ahmed M. Abdelmoniem, Chen-Yu Ho, Atal Narayan Sahu

Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data

Xin Du, Yulong Pei, Wouter Duivesteijn, Mykola Pechenizkiy

Gradient-Aware Model-Based Policy Search

Pierluca D'Oro, Alberto Maria Metelli, Andrea Tirinzoni, Matteo Papini, Marcello Restelli

Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning

Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Xiangnan He

Integrating Overlapping Datasets Using Bivariate Causal Discovery

Anish Dhir, Ciaran M. Lee

Optimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework

Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa, Alan Fern

Reinforcing Neural Network Stability with Attractor Dynamics

Hanming Deng, Yang Hua, Tao Song, Zhengui XUE, Ruhui Ma

System Identification with Time-Aware Neural Sequence Models

Thomas Demeester

Capsule Routing via Variational Bayes

Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos Kollias

Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning

Kristopher De Asis, Alan Chan, Silviu Pitis, Richard Sutton, Daniel Graves

Making Existing Clusterings Fairer: Algorithms, Complexity Results and Insights

Ian Davidson, S.S Ravi

DNNs as Layers of Cooperating Classifiers

Marelie Davel, Marthinus Theunissen, Arnold Pretorius, Etienne Barnard

A Skip-Connected Evolving Recurrent Neural Network for Data Stream Classification under Label Latency Scenario

Monidipa Das, Mahardhika Pratama, Jie Zhang, Yew Soon Ong

Explainable Data Decompositions

Sebastian Dalleiger, Jilles Vreeken

A Tale of Two-Timescale Reinforcement Learning with the Tightest Finite-Time Bound

Gal Dalal, Balázs Szörényi, Gugan Thoppe

Label Error Correction and Generation through Label Relationships

Zijun Cui, Yong Zhang, Qiang Ji

Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking

Eric Crawford, Joelle Pineau

Forgetting to Learn Logic Programs

Andrew Cropper

Representing Closed Transformation Paths in Encoded Network Latent Space

Marissa Connor, Christopher Rozell

A Constraint-Based Approach to Learning and Explanation

Gabriele Ciravegna, Francesco Giannini, Stefano Melacci, Marco Maggini, Marco Gori

Deep Mixed Effect Model Using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare

Ingyo Chung, Saehoon Kim, Juho Lee, Kwang Joon Kim, Sung Ju Hwang

Active Learning in the Geometric Block Model

Eli Chien, Antonia Tulino, Jaime Llorca

A General Approach to Fairness with Optimal Transport

Chiappa Silvia, Jiang Ray, Stepleton Tom, Pacchiano Aldo, Jiang Heinrich

Suspicion-Free Adversarial Attacks on Clustering Algorithms

Anshuman Chhabra, Abhishek Roy, Prasant Mohapatra

Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets

Ziqiang Cheng, Yang Yang, Wei Wang, Wenjie Hu, Yueting Zhuang

Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions

Weiyu Cheng, Yanyan Shen, Linpeng Huang

Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples

Minhao Cheng, Jinfeng Yi, Pin-Yu Chen, Huan Zhang, Cho-jui Hsieh

Towards Better Forecasting by Fusing Near and Distant Future Visions

Jiezhu Cheng, Kaizhu Huang, Zibin Zheng

Distilling Portable Generative Adversarial Networks for Image Translation

Hanting Chen, Yunhe Wang, Han Shu, Changyuan Wen, Chunjing XU

InstaNAS: Instance-Aware Neural Architecture Search

An-Chieh Cheng, Chieh Hubert Lin, Da-Cheng Juan, Wei Wei, Min Sun

Semi-Supervised Learning under Class Distribution Mismatch

Yanbei Chen, Xiatian Zhu, Wei Li, Shaogang Gong

Compressed Self-Attention for Deep Metric Learning

Ziye Chen, Mingming Gong, Yanwu Xu, Chaohui Wang, Kun Zhang

Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation

Ze-Sen Chen, Xuan Wu, Qing-Guo Chen, Yao Hu, Min-ling Zhang

Optimal Attack against Autoregressive Models by Manipulating the Environment

Yiding Chen, Xiaojin Zhu

AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection

Xu Chen, Brett Wujek

Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

Weiqi Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao

Adversarial-Learned Loss for Domain Adaptation

Minghao Chen, Shuai Zhao, Haifeng Liu, Deng Cai

Multi-View Clustering in Latent Embedding Space

Man-Sheng Chen, Ling Huang, Chang-Dong Wang, Dong Huang

Outlier Detection Ensemble with Embedded Feature Selection

Li Cheng, Yijie Wang, Xinwang Liu, Bin Li

Weakly Supervised Disentanglement by Pairwise Similarities

Junxiang Chen, Kayhan Batmanghelich

A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks

Jinghui Chen, Dongruo Zhou, Jinfeng Yi, Quanquan Gu

Variational Metric Scaling for Metric-Based Meta-Learning

JIAXIN CHEN, Li-Ming Zhan, Xiao-ming Wu, Fu-lai Chung

Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback

Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Jingbang Chen

Generative Adversarial Networks for Video-to-Video Domain Adaptation

Jiawei Chen, Yuexiang Li, Kai Ma, Yefeng Zheng

LS-Tree: Model Interpretation When the Data Are Linguistic

Jianbo Chen, Michael Jordan

ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System

Huangxun Chen, Chenyu Huang, Qianyi Huang, Qian Zhang, Wei Wang

Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View

Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou

Online Knowledge Distillation with Diverse Peers

Defang Chen, Jian-Ping Mei, Can Wang, Yan Feng, Chun Chen

HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation

Chao Chen, Zhihang Fu, Zhihong Chen, Sheng Jin, Zhaowei Cheng

Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control

Chacha Chen, Hua Wei, Nan Xu, Guanjie Zheng, Ming Yang

A New Ensemble Adversarial Attack Powered by Long-Term Gradient Memories

Zhaohui Che, Ali Borji, Guangtao Zhai, Suiyi Ling, Jing Li

Robust Data Programming with Precision-guided Labeling Functions

Oishik Chatterjee, Ganesh Ramakrishnan, Sunita Sarawagi

A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models

Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang

Reinforcement Learning When All Actions Are Not Always Available

Yash Chandak, Georgios Theocharous, Blossom Metevier, Philip Thomas

Lifelong Learning with a Changing Action Set

Yash Chandak, Georgios Theocharous, Chris Nota, Philip Thomas

Asking the Right Questions to the Right Users: Active Learning with Imperfect Oracles

Shayok Chakraborty

Exponential Family Graph Embeddings

Abdulkadir Çelikkanat, Fragkiskos D. Malliaros

Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks

Yuan Cao, Quanquan Gu

Fatigue-Aware Bandits for Dependent Click Models

Junyu Cao, Wei Sun, Zuo-Jun (Max) Shen, Markus Ettl

Active Ordinal Querying for Tuplewise Similarity Learning

Gregory Canal, Stefano Fenu, Christopher Rozell

Predicting Propositional Satisfiability via End-to-End Learning

Chris Cameron, Rex Chen, Jason Hartford, Kevin Leyton-brown

Deterministic Value-Policy Gradients

Qingpeng Cai, Ling Pan, Pingzhong Tang

A Multi-Scale Approach for Graph Link Prediction

Lei Cai, Shuiwang Ji

Information-Theoretic Understanding of Population Risk Improvement with Model Compression

Yuheng Bu, Weihao Gao, Shaofeng Zou, Venugopal Veeravalli

Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis

Elena Botoeva, Panagiotis Kouvaros, Jan Kronqvist, Alessio Lomuscio, Ruth Misener

Proximal Distilled Evolutionary Reinforcement Learning

Cristian Bodnar, Ben Day, Pietro Lió

A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control

Adel Bibi, El Houcine Bergou, Ozan Sener, Bernard Ghanem, Peter Richtarik

An Efficient Evolutionary Algorithm for Subset Selection with General Cost Constraints

Chao Bian, Chao Feng, Chao Qian, Yang Yu

Event-Driven Continuous Time Bayesian Networks

Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, Kush Varshney

Exploratory Combinatorial Optimization with Reinforcement Learning

Thomas Barrett, William Clements, Jakob Foerster, Alex Lvovsky

Midas: Microcluster-Based Detector of Anomalies in Edge Streams

Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos

Scalable Attentive Sentence Pair Modeling via Distilled Sentence Embedding

Oren Barkan, Noam Razin, Itzik Malkiel, Ori Katz, Avi Caciularu

Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees

Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik