AAAI 2021

1,961 papers

Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation

Umang Gupta, Aaron M Ferber, Bistra Dilkina, Greg Ver Steeg

Revisiting Iterative Back-Translation from the Perspective of Compositional Generalization

Yinuo Guo, Hualei Zhu, Zeqi Lin, Bei Chen, Jian-Guang Lou

Attentive Neural Point Processes for Event Forecasting

Yulong Gu

Efficient On-Chip Learning for Optical Neural Networks Through Power-Aware Sparse Zeroth-Order Optimization

Jiaqi Gu, Chenghao Feng, Zheng Zhao, Zhoufeng Ying, Ray T. Chen

Attribute-Guided Adversarial Training for Robustness to Natural Perturbations

Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Naman Goel, Alfonso Amayuelas, Amit Deshpande, Amit Sharma

Justicia: A Stochastic SAT Approach to Formally Verify Fairness

Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel

Uncertainty-Aware Multi-View Representation Learning

Yu Geng, Zongbo Han, Changqing Zhang, Qinghua Hu

Increasing Iterate Averaging for Solving Saddle-Point Problems

Yuan Gao, Christian Kroer, Donald Goldfarb

Addressing Domain Gap via Content Invariant Representation for Semantic Segmentation

Li Gao, Lefei Zhang, Qian Zhang

A Trace-restricted Kronecker-Factored Approximation to Natural Gradient

Kaixin Gao, Xiaolei Liu, Zhenghai Huang, Min Wang, Zidong Wang

On the Convergence of Communication-Efficient Local SGD for Federated Learning

Hongchang Gao, An Xu, Heng Huang

Stabilizing Q Learning Via Soft Mellowmax Operator

Yaozhong Gan, Zhe Zhang, Xiaoyang Tan

Diffusion Network Inference from Partial Observations

Ting Gan, Keqi Han, Hao Huang, Shi Ying, Yunjun Gao

HiGAN: Handwriting Imitation Conditioned on Arbitrary-Length Texts and Disentangled Styles

Ji Gan, Weiqiang Wang

Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances

Zhang-Hua Fu, Kai-Bin Qiu, Hongyuan Zha

Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment

Mengying Fu, Tianning Yuan, Fang Wan, Songcen Xu, Qixiang Ye

Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning

Haotian Fu, Hongyao Tang, Jianye Hao, Chen Chen, Xidong Feng

Few-Shot One-Class Classification via Meta-Learning

Ahmed Frikha, Denis Krompaß, Hans-Georg Köpken, Volker Tresp

Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression

Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe

Collaborative Group Learning

Shaoxiong Feng, Hongshen Chen, Xuancheng Ren, Zhuoye Ding, Kan Li

Learning to Augment for Data-scarce Domain BERT Knowledge Distillation

Lingyun Feng, Minghui Qiu, Yaliang Li, Hai-Tao Zheng, Ying Shen

SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations

Hao-Zhe Feng, Kezhi Kong, Minghao Chen, Tianye Zhang, Minfeng Zhu

UAG: Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks

Boyuan Feng, Yuke Wang, Yufei Ding

Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting

Amirreza Farnoosh, Bahar Azari, Sarah Ostadabbas

Learning to Reweight with Deep Interactions

Yang Fan, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu

Learning a Gradient-free Riemannian Optimizer on Tangent Spaces

Xiaomeng Fan, Zhi Gao, Yuwei Wu, Yunde Jia, Mehrtash Harandi

Adversarial Training and Provable Robustness: A Tale of Two Objectives

Jiameng Fan, Wenchao Li

Deep Graph Spectral Evolution Networks for Graph Topological Evolution

Negar Etemadyrad, Qingzhe Li, Liang Zhao

Almost Linear Time Density Level Set Estimation via DBSCAN

Hossein Esfandiari, Vahab Mirrokni, Peilin Zhong

Regret Bounds for Batched Bandits

Hossein Esfandiari, Amin Karbasi, Abbas Mehrabian, Vahab Mirrokni

Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems

Shohei Enomoro, Takeharu Eda

Projection-Free Bandit Optimization with Privacy Guarantees

Alina Ene, Huy L. Nguyen, Adrian Vladu

Adaptive Gradient Methods for Constrained Convex Optimization and Variational Inequalities

Alina Ene, Huy L. Nguyen, Adrian Vladu

Learning Prediction Intervals for Model Performance

Benjamin Elder, Matthew Arnold, Anupama Murthi, Jiri Navratil

The Parameterized Complexity of Clustering Incomplete Data

Eduard Eiben, Robert Ganian, Iyad Kanj, Sebastian Ordyniak, Stefan Szeider

Reinforcement Learning with Trajectory Feedback

Yonathan Efroni, Nadav Merlis, Shie Mannor

Semi-Supervised Metric Learning: A Deep Resurrection

Ujjal Kr Dutta, Mehrtash Harandi, C Chandra Shekhar

Knowledge Refactoring for Inductive Program Synthesis

Sebastijan Dumancic, Tias Guns, Andrew Cropper

Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback

Yihan Du, Yuko Kuroki, Wei Chen

A One-Size-Fits-All Solution to Conservative Bandit Problems

Yihan Du, Siwei Wang, Longbo Huang

Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences

Andis Draguns, Emīls Ozoliņš, Agris Šostaks, Matīss Apinis, Karlis Freivalds

Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization

Kien Do, Truyen Tran, Svetha Venkatesh

Knowledge Refinery: Learning from Decoupled Label

Qianggang Ding, Sifan Wu, Tao Dai, Hao Sun, Jiadong Guo

Differentially Private and Communication Efficient Collaborative Learning

Jiahao Ding, Guannan Liang, Jinbo Bi, Miao Pan

Mercer Features for Efficient Combinatorial Bayesian Optimization

Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa

Learning with Retrospection

Xiang Deng, Zhongfei Zhang

Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models

Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George

Generalized Adversarially Learned Inference

Yatin Dandi, Homanga Bharadhwaj, Abhishek Kumar, Piyush Rai

Differentially Private Stochastic Coordinate Descent

Georgios Damaskinos, Celestine Mendler-Dünner, Rachid Guerraoui, Nikolaos Papandreou, Thomas Parnell

Loop Estimator for Discounted Values in Markov Reward Processes

Falcon Z. Dai, Matthew R. Walter

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning

Will Dabney, Andre Barreto, Mark Rowland, Robert Dadashi, John Quan

Type-augmented Relation Prediction in Knowledge Graphs

Zijun Cui, Pavan Kapanipathi, Kartik Talamadupula, Tian Gao, Qiang Ji

Cost-aware Graph Generation: A Deep Bayesian Optimization Approach

Jiaxu Cui, Bo Yang, Bingyi Sun, Jiming Liu

Computationally Tractable Riemannian Manifolds for Graph Embeddings

Calin Cruceru, Gary Becigneul, Octavian-Eugen Ganea

Transfer Learning for Efficient Iterative Safety Validation

Anthony Corso, Mykel J. Kochenderfer

Continuous-Time Attention for Sequential Learning

Jen-Tzung Chien, Yi-Hsiang Chen

Self-Progressing Robust Training

Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-jui Hsieh

Neighborhood Consensus Networks for Unsupervised Multi-view Outlier Detection

Li Cheng, Yijie Wang, Xinwang Liu

NASGEM: Neural Architecture Search via Graph Embedding Method

Hsin-Pai Cheng, Tunhou Zhang, Yixing Zhang, Shiyu Li, Feng Liang

HyDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks

Yuanyuan Chen, Boyang Li, Han Yu, Pengcheng Wu, Chunyan Miao

Deep Spiking Neural Network with Neural Oscillation and Spike-Phase Information

Yi Chen, Hong Qu, Malu Zhang, Yuchen Wang

Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks

Xin Chen, Lingxi Xie, Jun Wu, Longhui Wei, Yuhui Xu

Neural Relational Inference with Efficient Message Passing Mechanisms

Siyuan Chen, Jiahai Wang, Guoqing Li

THOR, Trace-based Hardware-driven Layer-Oriented Natural Gradient Descent Computation

Mengyun Chen, Kaixin Gao, Xiaolei Liu, Zidong Wang, Ningxi Ni

Distributed Ranking with Communications: Approximation Analysis and Applications

Hong Chen, Yingjie Wang, Yulong Wang, Feng Zheng

Cross-Layer Distillation with Semantic Calibration

Defang Chen, Jian-Ping Mei, yuan zhang, Can Wang, Zhe Wang

Addressing Action Oscillations through Learning Policy Inertia

Chen Chen, Hongyao Tang, Jianye Hao, Wulong Liu, Zhaopeng Meng

Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning

Chao Chen, Dongsheng Li, Junchi Yan, Hanchi Huang, Xiaokang Yang

Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models

Tong Che, Xiaofeng Liu, Site Li, Yubin Ge, Ruixiang ZHANG

Using Hindsight to Anchor Past Knowledge in Continual Learning

Arslan Chaudhry, Albert Gordo, Puneet Dokania, Philip Torr, David Lopez-paz

Differentially Private Decomposable Submodular Maximization

Anamay Chaturvedi, Huy Lê Nguyễn, Lydia Zakynthinou

Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks

Xiangyu Chang, Yingcong Li, Samet Oymak, CHRISTOS THRAMPOULIDIS

On Online Optimization: Dynamic Regret Analysis of Strongly Convex and Smooth Problems

Ting-Jui Chang, Shahin Shahrampour

Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications

Haw-Shiuan Chang, Amol Agrawal, Andrew McCallum

A Multi-step-ahead Markov Conditional Forward Model with Cube Perturbations for Extreme Weather Forecasting

Chia-Yuan Chang, Cheng-Wei Lu, Chuan-Ju Wang

High-Confidence Off-Policy (or Counterfactual) Variance Estimation

Yash Chandak, Shiv Shankar, Philip S. Thomas

Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm

Saptarshi Chakraborty, Debolina Paul, Swagatam Das

Frivolous Units: Wider Networks Are Not Really That Wide

Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf

Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning

Paola Cascante-Bonilla, Fuwen Tan, Yanjun Qi, Vicente Ordonez

Counterfactual Explanations for Oblique Decision Trees:Exact, Efficient Algorithms

Miguel Á. Carreira-Perpiñán, Suryabhan Singh Hada

Dual Quaternion Knowledge Graph Embeddings

Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang

Provably Secure Federated Learning against Malicious Clients

Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong

Open-Set Recognition with Gaussian Mixture Variational Autoencoders

Alexander Cao, Yuan Luo, Diego Klabjan

A Blind Block Term Decomposition of High Order Tensors

Yunfeng Cai, Ping Li

Time Series Domain Adaptation via Sparse Associative Structure Alignment

Ruichu Cai, Jiawei Chen, Zijian Li, Wei Chen, Keli Zhang

Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation

Bowen Cai, Huan Fu, Rongfei Jia, Binqiang Zhao, Hua Li

Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently

Rebekka Burkholz, John Quackenbush

Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness

Anh Tuan Bui, Trung Le, He Zhao, Paul Montague, Olivier deVel

Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints

Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Aravind Srinivasan, Leonidas Tsepenekas

Sample-Specific Output Constraints for Neural Networks

Mathis Brosowsky, Florian Keck, Olaf Dünkel, Marius Zöllner

Fast Training of Provably Robust Neural Networks by SingleProp

Akhilan Boopathy, Lily Weng, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang

Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks

Yoonho Boo, Sungho Shin, Jungwook Choi, Wonyong Sung

Communication-Aware Collaborative Learning

Avrim Blum, Shelby Heinecke, Lev Reyzin

Understanding Decoupled and Early Weight Decay

Johan Bjorck, Kilian Q. Weinberger, Carla Gomes

Characterizing the Loss Landscape in Non-Negative Matrix Factorization

Johan Bjorck, Anmol Kabra, Kilian Q. Weinberger, Carla Gomes

Ordinal Historical Dependence in Graphical Event Models with Tree Representations

Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian

ExGAN: Adversarial Generation of Extreme Samples

Siddharth Bhatia, Arjit Jain, Bryan Hooi

A Theory of Independent Mechanisms for Extrapolation in Generative Models

Michel Besserve, Remy Sun, Dominik Janzing, Bernhard Schölkopf

Relative Variational Intrinsic Control

Kate Baumli, David Warde-Farley, Steven Hansen, Volodymyr Mnih