AAAI 2023

2,023 papers

Heterogeneous Graph Masked Autoencoders

Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla

Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks

Bowen Tian, Qinliang Su, Jianxing Yu

Knowledge Amalgamation for Multi-Label Classification via Label Dependency Transfer

Jidapa Thadajarassiri, Thomas Hartvigsen, Walter Gerych, Xiangnan Kong, Elke Rundensteiner

DE-net: Dynamic Text-Guided Image Editing Adversarial Networks

Ming Tao, Bing-Kun Bao, Hao Tang, Fei Wu, Longhui Wei

Metric Multi-View Graph Clustering

Yuze Tan, Yixi Liu, Hongjie Wu, Jiancheng Lv, Shudong Huang

Federated Learning on Non-IID Graphs via Structural Knowledge Sharing

Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu

Fast Convergence in Learning Two-Layer Neural Networks with Separable Data

Hossein Taheri, CHRISTOS THRAMPOULIDIS

Domain Adaptation with Adversarial Training on Penultimate Activations

Tao Sun, Cheng Lu, Haibin Ling

Neural Spline Search for Quantile Probabilistic Modeling

Ruoxi Sun, Chun-Liang Li, Sercan Ö. Arik, Michael W. Dusenberry, Chen-Yu Lee

Fair-CDA: Continuous and Directional Augmentation for Group Fairness

Rui Sun, Fengwei Zhou, Zhenhua Dong, Chuanlong Xie, Lanqing Hong

Cooperative and Adversarial Learning: Co-enhancing Discriminability and Transferability in Domain Adaptation

Hui Sun, Zheng Xie, Xin-Ye Li, Ming Li

REMIT: Reinforced Multi-Interest Transfer for Cross-Domain Recommendation

Caiqi Sun, Jiewei Gu, Binbin Hu, Xin Dong, Hai Li

Scalable Optimal Multiway-Split Decision Trees with Constraints

Shivaram Subramanian, Wei Sun

Sharing Pattern Submodels for Prediction with Missing Values

Lena Stempfle, Ashkan Panahi, Fredrik D. Johansson

Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling

Gregory P. Spell, Simiao Ren, Leslie M. Collins, Jordan M. Malof

Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning

Jinhyun So, Ramy E. Ali, Basak Guler, Jiantao Jiao, A. Salman Avestimehr

Adaptive Mixing of Auxiliary Losses in Supervised Learning

Durga Sivasubramanian, Ayush Maheshwari, Prathosh AP, Pradeep Shenoy, Ganesh Ramakrishnan

SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers

Daniel Silver, Tirthak Patel, Aditya Ranjan, Harshitta Gandhi, William Cutler

Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI

Suzanna Sia, Anton Belyy, Amjad Almahairi, Madian Khabsa, Luke Zettlemoyer

A Generalized Unbiased Risk Estimator for Learning with Augmented Classes

Senlin Shu, Shuo He, Haobo Wang, Hongxin Wei, Tao Xiang

Concurrent Multi-Label Prediction in Event Streams

Xiao Shou, Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Kristin P. Bennett

Fixed-Weight Difference Target Propagation

Tatsukichi Shibuya, Nakamasa Inoue, Rei Kawakami, Ikuro Sato

Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning

Xiaoxiao Sheng, Zhiqiang Shen, Gang Xiao

ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-Cost Proxies

Yu Shen, Yang Li, Jian Zheng, Wentao Zhang, Peng Yao

Neighbor Contrastive Learning on Learnable Graph Augmentation

Xiao Shen, Dewang Sun, Shirui Pan, Xi Zhou, Laurence T. Yang

Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model

Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das

What Do You MEME? Generating Explanations for Visual Semantic Role Labelling in Memes

Shivam Sharma, Siddhant Agarwal, Tharun Suresh, Preslav Nakov, Md. Shad Akhtar

Multi-Source Survival Domain Adaptation

Ammar Shaker, Carolin Lawrence

Exploration via Epistemic Value Estimation

Simon Schmitt, John Shawe-Taylor, Hado van Hasselt

Dropout Is NOT All You Need to Prevent Gradient Leakage

Daniel Scheliga, Patrick Maeder, Marco Seeland

Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Synchronicity

Pritam Sarkar, Ali Etemad

Sparse Coding in a Dual Memory System for Lifelong Learning

Fahad Sarfraz, Elahe Arani, Bahram Zonooz

Representation Learning by Detecting Incorrect Location Embeddings

Sepehr Sameni, Simon Jenni, Paolo Favaro

Losses over Labels: Weakly Supervised Learning via Direct Loss Construction

Dylan Sam, J. Zico Kolter

Fast Offline Policy Optimization for Large Scale Recommendation

Otmane Sakhi, David Rohde, Alexandre Gilotte

Continual Learning with Scaled Gradient Projection

Gobinda Saha, Kaushik Roy

Simultaneously Updating All Persistence Values in Reinforcement Learning

Luca Sabbioni, Luca Al Daire, Lorenzo Bisi, Alberto Maria Metelli, Marcello Restelli

On the Sample Complexity of Representation Learning in Multi-Task Bandits with Global and Local Structure

Alessio Russo, Alexandre Proutiere

Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose

Harry Rubin-Falcone, Joyce Lee, Jenna Wiens

Accommodating Audio Modality in CLIP for Multimodal Processing

Ludan Ruan, Anwen Hu, Yuqing Song, Liang Zhang, Sipeng Zheng

Inferring Patient Zero on Temporal Networks via Graph Neural Networks

Xiaolei Ru, Jack Murdoch Moore, Xin-Ya Zhang, Yeting Zeng, Gang Yan

Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal Distributions

Mohammad Rostami, Aram Galstyan

DisGUIDE: Disagreement-Guided Data-Free Model Extraction

Jonathan Rosenthal, Eric Enouen, Hung Viet Pham, Lin Tan

Planning and Learning with Adaptive Lookahead

Aviv Rosenberg, Assaf Hallak, Shie Mannor, Gal Chechik, Gal Dalal

ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning

Yi Rong, Xiongbo Lu, Zhaoyang Sun, Yaxiong Chen, Shengwu Xiong

Automata Cascades: Expressivity and Sample Complexity

Alessandro Ronca, Nadezda Alexandrovna Knorozova, Giuseppe De Giacomo

Hypernetworks for Zero-Shot Transfer in Reinforcement Learning

Sahand Rezaei-Shoshtari, Charlotte Morissette, Francois R. Hogan, Gregory Dudek, David Meger

Diffusing Gaussian Mixtures for Generating Categorical Data

Florence Regol, Mark Coates

Provable Detection of Propagating Sampling Bias in Prediction Models

Pavan Ravishankar, Qingyu Mo, Edward McFowland III, Daniel B. Neill

GLUECons: A Generic Benchmark for Learning under Constraints

Hossein Rajaby Faghihi, Aliakbar Nafar, Chen Zheng, Roshanak Mirzaee, Yue Zhang

Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes

Chao Qu, Xiaoyu Tan, Siqiao Xue, Xiaoming Shi, James Zhang

Gradient-Variation Bound for Online Convex Optimization with Constraints

Shuang Qiu, Xiaohan Wei, Mladen Kolar

Stochastic Contextual Bandits with Long Horizon Rewards

Yuzhen Qin, Yingcong Li, Fabio Pasqualetti, Maryam Fazel, Samet Oymak

Training Meta-Surrogate Model for Transferable Adversarial Attack

Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Cho-jui Hsieh

Mutual-Enhanced Incongruity Learning Network for Multi-Modal Sarcasm Detection

Yang Qiao, Liqiang Jing, Xuemeng Song, Xiaolin Chen, LEI ZHU

Mixture Uniform Distribution Modeling and Asymmetric Mix Distillation for Class Incremental Learning

Sunyuan Qiang, Jiayi Hou, Jun Wan, Yanyan Liang, Zhen Lei

Rethinking Data-Free Quantization as a Zero-Sum Game

Biao Qian, Yang Wang, Richang Hong, Meng Wang

CMVAE: Causal Meta VAE for Unsupervised Meta-Learning

Guodong Qi, Huimin Yu

Experimental Observations of the Topology of Convolutional Neural Network Activations

Emilie Purvine, Davis Brown, Brett Jefferson, Cliff Joslyn, Brenda Praggastis

A Model-Agnostic Heuristics for Selective Classification

Andrea Pugnana, Salvatore Ruggieri

Explaining Random Forests Using Bipolar Argumentation and Markov Networks

Nico Potyka, Xiang Yin, Francesca Toni

Latent Autoregressive Source Separation

Emilian Postolache, Giorgio Mariani, Michele Mancusi, Andrea Santilli, Luca Cosmo

Weighted Policy Constraints for Offline Reinforcement Learning

Zhiyong Peng, Changlin Han, Yadong Liu, Zongtan Zhou

Conceptual Reinforcement Learning for Language-Conditioned Tasks

shaohui peng, Xing Hu, Rui Zhang, Jiaming Guo, Qi Yi

Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction

Brahma S. Pavse, Josiah P. Hanna

Dynamic Structure Pruning for Compressing CNNs

Jun-Hyung Park, Yeachan Kim, Junho Kim, Joon-Young Choi, SangKeun Lee

Balanced Column-Wise Block Pruning for Maximizing GPU Parallelism

Cheonjun Park, Mincheol Park, Hyun Jae Oh, Minkyu Kim, Myung Kuk Yoon

Evidential Conditional Neural Processes

Deep Shankar Pandey, Qi Yu

Isometric Manifold Learning Using Hierarchical Flow

Ziqi Pan, Jianfu Zhang, Li Niu, Liqing Zhang

Geometric Inductive Biases for Identifiable Unsupervised Learning of Disentangled Representations

Ziqi Pan, Li Niu, Liqing Zhang

FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance

Zibin Pan, Shuyi Wang, Chi Li, Haijin Wang, Xiaoying Tang

Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty

Zhenyu Pan, Anshujit Sharma, Jerry Yao-Chieh Hu, Zhuo Liu, Ang Li

H-TSP: Hierarchically Solving the Large-Scale Traveling Salesman Problem

Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng, Li Zhao

Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration & Planning

Reda Ouhamma, Debabrota Basu, Odalric Maillard

Backpropagation-Free Deep Learning with Recursive Local Representation Alignment

Alexander G. Ororbia, Ankur Mali, Daniel Kifer, C. Lee Giles

Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks

Kentaro Ohno, Sekitoshi Kanai, Yasutoshi Ida

On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation

Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, Svetha Venkatesh, Raman Arora

Behavioral Learning in Security Games: Threat of Multi-Step Manipulative Attacks

Thanh H. Nguyen, Arunesh Sinha

Efficient and Accurate Learning of Mixtures of Plackett-Luce Models

Duc Nguyen, Anderson Y. Zhang

Counterfactual Learning with General Data-Generating Policies

Yusuke Narita, Kyohei Okumura, Akihiro Shimizu, Kohei Yata

Do Invariances in Deep Neural Networks Align with Human Perception?

Vedant Nanda, Ayan Majumdar, Camila Kolling, John P. Dickerson, Krishna P. Gummadi

An Operator Theoretic Approach for Analyzing Sequence Neural Networks

Ilan Naiman, Omri Azencot

Mean Estimation of Truncated Mixtures of Two Gaussians: A Gradient Based Approach

Sai Ganesh Nagarajan, Gerasimos Palaiopanos, Ioannis Panageas, Tushar Vaidya, Samson Yu

Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization

Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderon, Michael Bronstein

Corruption-Tolerant Algorithms for Generalized Linear Models

Bhaskar Mukhoty, Debojyoti Dey, Purushottam Kar

Exploring the Interaction between Local and Global Latent Configurations for Clustering Single-Cell RNA-Seq: A Unified Perspective

Nairouz Mrabah, Mohamed Mahmoud Amar, Mohamed Bouguessa, Abdoulaye Baniré Diallo

Fundamentals of Task-Agnostic Data Valuation

Mohammad Mohammadi Amiri, Frédéric Berdoz, Ramesh Raskar

Multiplex Graph Representation Learning via Common and Private Information Mining

Yujie Mo, Zongqian Wu, Yuhuan Chen, Xiaoshuang Shi, Heng Tao Shen

Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption

Matthias Mitterreiter, Marcel Koch, Joachim Giesen, Sören Laue

Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models

Pasquale Minervini, Luca Franceschi, Mathias Niepert

GENNAPE: Towards Generalized Neural Architecture Performance Estimators

Keith G. Mills, Fred X. Han, Jialin Zhang, Fabian Chudak, Ali Safari Mamaghani

AIO-P: Expanding Neural Performance Predictors beyond Image Classification

Keith G. Mills, Di Niu, Mohammad Salameh, Weichen Qiu, Fred X. Han

Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments

Osman Mian, Michael Kamp, Jilles Vreeken

Off-Policy Proximal Policy Optimization

Wenjia Meng, Qian Zheng, Gang Pan, Yilong Yin

MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series

Qianwen Meng, Hangwei Qian, Yong Liu, Lizhen Cui, Yonghui Xu

HyperJump: Accelerating HyperBand via Risk Modelling

Pedro Mendes, Maria Casimiro, Paolo Romano, David Garlan

The Unreasonable Effectiveness of Deep Evidential Regression

Nis Meinert, Jakob Gawlikowski, Alexander Lavin

Towards Interpreting and Utilizing Symmetry Property in Adversarial Examples

Shibin Mei, Chenglong Zhao, Bingbing Ni, Shengchao Yuan

VIDM: Video Implicit Diffusion Models

Kangfu Mei, Vishal Patel

Diffusion Models Beat GANs on Topology Optimization

François Mazé, Faez Ahmed