AAAI 2022

1,624 papers

Fusion Multiple Kernel K-means

Yi Zhang, Xinwang Liu, Jiyuan Liu, Sisi Dai, Changwang Zhang

Tailor Versatile Multi-Modal Learning for Multi-Label Emotion Recognition

Yi Zhang, Mingyuan Chen, Jundong Shen, Chongjun Wang

A Multi-Agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning

Sai Qian Zhang, Jieyu Lin, Qi Zhang

Rethinking Influence Functions of Neural Networks in the Over-Parameterized Regime

Rui Zhang, Shihua Zhang

Gaussian Process Bandits with Aggregated Feedback

Mengyan Zhang, Russell Tsuchida, Cheng Soon Ong

Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings

Matthew S. Zhang, Murat A Erdogdu, Animesh Garg

Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-trained Source Models

Luxin Zhang, Pascal Germain, Yacine Kessaci, Christophe Biernacki

Categorical Neighbour Correlation Coefficient (CnCor) for Detecting Relationships between Categorical Variables

Lifeng Zhang, Shimo Yang, Hongxun Jiang

Co-promotion Predictions of Financing Market and Sales Market: A Cooperative-Competitive Attention Approach

Lei Zhang, Wang Xiang, Chuang Zhao, Hongke Zhao, Rui Li

Multi-Agent Reinforcement Learning with General Utilities via Decentralized Shadow Reward Actor-Critic

Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel

State Deviation Correction for Offline Reinforcement Learning

Hongchang Zhang, Jianzhun Shao, Yuhang Jiang, Shuncheng He, Guanwen Zhang

MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning

Baoquan Zhang, Xutao Li, Shanshan Feng, Yunming Ye, Rui Ye

Efficient Decentralized Stochastic Gradient Descent Method for Nonconvex Finite-Sum Optimization Problems

Wenkang Zhan, Gang Wu, Hongchang Gao

SimSR: Simple Distance-Based State Representations for Deep Reinforcement Learning

Hongyu Zang, Xin Li, Mingzhong Wang

Fractional Adaptive Linear Units

Julio Zamora, Anthony D. Rhodes, Lama Nachman

TS2Vec: Towards Universal Representation of Time Series

Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang

Unsupervised Learning of Compositional Scene Representations from Multiple Unspecified Viewpoints

Jinyang Yuan, Bin Li, Xiangyang Xue

MIA-Former: Efficient and Robust Vision Transformers via Multi-Grained Input-Adaptation

Zhongzhi Yu, Yonggan Fu, Sicheng Li, Chaojian Li, Yingyan Lin

Low-Pass Graph Convolutional Network for Recommendation

Wenhui Yu, Zixin Zhang, Zheng Qin

Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms

Runpeng Yu, Hong Zhu, Kaican Li, Lanqing Hong, Rui Zhang

Natural Black-Box Adversarial Examples against Deep Reinforcement Learning

Mengran Yu, Shiliang Sun

SAIL: Self-Augmented Graph Contrastive Learning

Lu Yu, Shichao Pei, Lizhong Ding, Jun Zhou, Longfei Li

Hindsight Network Credit Assignment: Efficient Credit Assignment in Networks of Discrete Stochastic Units

Kenny Young

Early-Bird GCNs: Graph-Network Co-optimization towards More Efficient GCN Training and Inference via Drawing Early-Bird Lottery Tickets

Haoran You, Zhihan Lu, Zijian Zhou, Yonggan Fu, Yingyan Lin

BM-NAS: Bilevel Multimodal Neural Architecture Search

Yihang Yin, Siyu Huang, Xiang Zhang

AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators

Yihang Yin, Qingzhong Wang, Siyu Huang, Haoyi Xiong, Xiang Zhang

Distributed Randomized Sketching Kernel Learning

Rong Yin, Yong Liu, Dan Meng

BATUDE: Budget-Aware Neural Network Compression Based on Tucker Decomposition

Miao Yin, Huy Phan, Xiao Zang, Siyu Liao, Bo Yuan

Stage Conscious Attention Network (SCAN): A Demonstration-Conditioned Policy for Few-Shot Imitation

Jia-Fong Yeh, Chi-Ming Chung, Hung-Ting Su, Yi-Ting Chen, Winston H. Hsu

Lifelong Generative Modelling Using Dynamic Expansion Graph Model

Fei Ye, Adrian G. Bors

Exploiting Invariance in Training Deep Neural Networks

Chengxi Ye, Xiong Zhou, Tristan McKinney, Yanfeng Liu, Qinggang Zhou

LOGICDEF: An Interpretable Defense Framework against Adversarial Examples via Inductive Scene Graph Reasoning

Yuan Yang, James C Kerce, Faramarz Fekri

Graph Pointer Neural Networks

Tianmeng Yang, Yujing Wang, Zhihan Yue, Yaming Yang, Yunhai Tong

Policy Optimization with Stochastic Mirror Descent

Long Yang, Yu Zhang, Gang Zheng, Qian Zheng, Pengfei Li

Training a Resilient Q-network against Observational Interference

Chao-Han Huck Yang, I-Te Danny Hung, Yi Ouyang, Pin-Yu Chen

Q-Ball: Modeling Basketball Games Using Deep Reinforcement Learning

Chen Yanai, Adir Solomon, Gilad Katz, Bracha Shapira, Lior Rokach

Learning to Identify Top Elo Ratings: A Dueling Bandits Approach

Xue Yan, Yali Du, Binxin Ru, Jun Wang, Haifeng Zhang

Seizing Critical Learning Periods in Federated Learning

Gang Yan, Hao Wang, Jian Li

Go Wider Instead of Deeper

Fuzhao Xue, Ziji Shi, Futao Wei, Yuxuan Lou, Yong Liu

Linearity-Aware Subspace Clustering

Yesong Xu, Shuo Chen, Jun Li, Jianjun Qian

Deep Incomplete Multi-View Clustering via Mining Cluster Complementarity

Jie Xu, CHAO LI, Yazhou Ren, Liang Peng, Yujie Mo

Constraints Penalized Q-learning for Safe Offline Reinforcement Learning

Haoran Xu, Xianyuan Zhan, Xiangyu Zhu

Learning-Augmented Algorithms for Online Steiner Tree

Chenyang Xu, Benjamin Moseley

Coordinating Momenta for Cross-Silo Federated Learning

An Xu, Heng Huang

Reinforcement Learning Augmented Asymptotically Optimal Index Policy for Finite-Horizon Restless Bandits

GUOJUN XIONG, Jian Li, Rahul Singh

GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation

RENCHUNZI XIE, Hongxin Wei, Lei Feng, Bo An

Active Learning for Domain Adaptation: An Energy-Based Approach

Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng

Towards Off-Policy Learning for Ranking Policies with Logged Feedback

Teng Xiao, Suhang Wang

AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method

Xiaoxia Wu, Yuege Xie, Simon Shaolei Du, Rachel Ward

Generalizing Reinforcement Learning through Fusing Self-Supervised Learning into Intrinsic Motivation

Keyu Wu, Min Wu, Zhenghua Chen, Yuecong Xu, Xiaoli Li

PUMA: Performance Unchanged Model Augmentation for Training Data Removal

Ga Wu, Masoud Hashemi, Christopher Srinivasa

An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation

Honguk Woo, Hyunsung Lee, SangWoo Cho

Structure Learning-Based Task Decomposition for Reinforcement Learning in Non-stationary Environments

Honguk Woo, Gwangpyo Yoo, Minjong Yoo

PluGeN: Multi-Label Conditional Generation from Pre-trained Models

Maciej Wołczyk, Magdalena Proszewska, Łukasz Maziarka, Maciej Zieba, Patryk Wielopolski

Prune and Tune Ensembles: Low-Cost Ensemble Learning with Sparse Independent Subnetworks

Tim Whitaker, Darrell Whitley

Symbolic Brittleness in Sequence Models: On Systematic Generalization in Symbolic Mathematics

Sean Welleck, Peter West, Jize Cao, Yejin Choi

Controlling Underestimation Bias in Reinforcement Learning via Quasi-median Operation

Wei Wei, Yujia Zhang, Jiye Liang, Lin Li, Yyuze Li

Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic

Zhihai Wang, Jie Wang, Qi Zhou, Bin Li, Houqiang Li

Max-Min Grouped Bandits

Zhenlin Wang, Jonathan Scarlett

Continual Learning through Retrieval and Imagination

Zhen Wang, Liu Liu, Yiqun Duan, Dacheng Tao

Efficient Causal Structure Learning from Multiple Interventional Datasets with Unknown Targets

Yunxia Wang, Fuyuan Cao, Kui Yu, Jiye Liang

Efficient Algorithms for General Isotone Optimization

Xiwen WANG, Jiaxi Ying, José Vinícius de M. Cardoso, Daniel P. Palomar

Boosting Active Learning via Improving Test Performance

Tianyang Wang, Xingjian Li, Pengkun Yang, Guosheng Hu, Xiangrui Zeng

Learngene: From Open-World to Your Learning Task

Qiu-Feng Wang, Xin Geng, Shu-Xia Lin, Shi-Yu Xia, Lei Qi

Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD

Jiayi Wang, Shiqiang Wang, Rong-Rong Chen, Mingyue Ji

Context Uncertainty in Contextual Bandits with Applications to Recommender Systems

Hao Wang, Yifei Ma, Hao Ding, Yuyang Wang

Spline-PINN: Approaching PDEs without Data Using Fast, Physics-Informed Hermite-Spline CNNs

Nils Wandel, Michael Weinmann, Michael Neidlin, Reinhard Klein

Robust Optimal Classification Trees against Adversarial Examples

Daniël Vos, Sicco Verwer

Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models

Sahil Verma, Keegan Hines, John P. Dickerson

PrivateMail: Supervised Manifold Learning of Deep Features with Privacy for Image Retrieval

Praneeth Vepakomma, Julia Balla, Ramesh Raskar

Listwise Learning to Rank Based on Approximate Rank Indicators

Thibaut Thonet, Yagmur Gizem Cinar, Eric Gaussier, Minghan Li, Jean-Michel Renders

SplitFed: When Federated Learning Meets Split Learning

Chandra Thapa, Pathum Chamikara Mahawaga Arachchige, Seyit Camtepe, Lichao Sun

SMINet: State-Aware Multi-Aspect Interests Representation Network for Cold-Start Users Recommendation

Wanjie Tao, Yu Li, Liangyue Li, Zulong Chen, Hong Wen

Powering Finetuning in Few-Shot Learning: Domain-Agnostic Bias Reduction with Selected Sampling

Ran Tao, Han Zhang, Yutong Zheng, Marios Savvides

With False Friends Like These, Who Can Notice Mistakes?

Lue Tao, Lei Feng, Jinfeng Yi, Songcan Chen

Optimal Sampling Gaps for Adaptive Submodular Maximization

Shaojie Tang, Jing Yuan

What about Inputting Policy in Value Function: Policy Representation and Policy-Extended Value Function Approximator

Hongyao Tang, Zhaopeng Meng, Jianye Hao, Chen Chen, Daniel Graves

FedProto: Federated Prototype Learning across Heterogeneous Clients

Yue Tan, Guodong Long, LU LIU, Tianyi Zhou, Qinghua Lu

Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability

Aviv Tamar, Daniel Soudry, Ev Zisselman

Consistency Regularization for Adversarial Robustness

Jihoon Tack, Sihyun Yu, Jongheon Jeong, Minseon Kim, Sung Ju Hwang

Explainable and Local Correction of Classification Models Using Decision Trees

Hirofumi Suzuki, Hiroaki Iwashita, Takuya Takagi, Keisuke Goto, Yuta Fujishige

Generalized Equivariance and Preferential Labeling for GNN Node Classification

Zeyu Sun, Wenjie Zhang, Lili Mou, Qihao Zhu, Yingfei Xiong

Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes

Yi-Xuan Sun, Wei Wang

Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency

Mingfei Sun, Sam Devlin, Katja Hofmann, Shimon Whiteson

Gradient-Based Novelty Detection Boosted by Self-Supervised Binary Classification

Jingbo Sun, Li Yang, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar

Reward-Weighted Regression Converges to a Global Optimum

Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber

Feature Importance Explanations for Temporal Black-Box Models

Akshay Sood, Mark Craven

ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems

Mahdi Soleymani, Ramy E. Ali, Hessam Mahdavifar, A. Salman Avestimehr

EqGNN: Equalized Node Opportunity in Graphs

Uriel Singer, Kira Radinsky

QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers

Daniel Silver, Tirthak Patel, Devesh Tiwari

Noise-Robust Learning from Multiple Unsupervised Sources of Inferred Labels

Amila Silva, Ling Luo, Shanika Karunasekera, Christopher Leckie

Constraint-Driven Explanations for Black-Box ML Models

Aditya A. Shrotri, Nina Narodytska, Alexey Ignatiev, Kuldeep S Meel, Joao Marques-Silva

Estimation of Local Average Treatment Effect by Data Combination

Kazuhiko Shinoda, Takahiro Hoshino

TRF: Learning Kernels with Tuned Random Features

Alistair Shilton, Sunil Gupta, Santu Rana, Arun Kumar Venkatesh, Svetha Venkatesh

Shape Prior Guided Attack: Sparser Perturbations on 3D Point Clouds

Zhenbo Shi, Zhi Chen, Zhenbo Xu, Wei Yang, Zhidong Yu

Learning Bounded Context-Free-Grammar via LSTM and the Transformer: Difference and the Explanations

Hui Shi, Sicun Gao, Yuandong Tian, Xinyun Chen, Jishen Zhao

Conditional Generative Model Based Predicate-Aware Query Approximation

Nikhil Sheoran, Subrata Mitra, Vibhor Porwal, Siddharth Ghetia, Jatin Varshney

HoD-Net: High-Order Differentiable Deep Neural Networks and Applications

Siyuan Shen, Tianjia Shao, Kun Zhou, Chenfanfu Jiang, Feng Luo

Online Apprenticeship Learning

Lior Shani, Tom Zahavy, Shie Mannor

Learning to Transfer with von Neumann Conditional Divergence

Ammar Shaker, Shujian Yu, Daniel Oñoro-Rubio