AAAI 2022

1,624 papers

Max-Margin Contrastive Learning

Anshul Shah, Suvrit Sra, Rama Chellappa, Anoop Cherian

Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks

Prithviraj Sen, Breno W. S. R. de Carvalho, Ryan Riegel, Alexander Gray

Neural Networks Classify through the Class-Wise Means of Their Representations

Mohamed El Amine Seddik, Mohamed Tamaazousti

Graph Filtration Kernels

Till Schulz, Pascal Welke, Stefan Wrobel

Chaining Value Functions for Off-Policy Learning

Simon Schmitt, John Shawe-Taylor, Hado van Hasselt

Scaling Up Influence Functions

Andrea Schioppa, Polina Zablotskaia, David Vilar, Artem Sokolov

Verification of Neural-Network Control Systems by Integrating Taylor Models and Zonotopes

Christian Schilling, Marcelo Forets, Sebastián Guadalupe

VACA: Designing Variational Graph Autoencoders for Causal Queries

Pablo Sanchez-Martin, Miriam Rateike, Isabel Valera

Reverse Differentiation via Predictive Coding

Tommaso Salvatori, Yuhang Song, Zhenghua Xu, Thomas Lukasiewicz, Rafal Bogacz

Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel k-Means, and Heat Kernel

Shota Saito

Knowledge Distillation via Constrained Variational Inference

Ardavan Saeedi, Yuria Utsumi, Li Sun, Kayhan Batmanghelich, Li-wei Lehman

FedSoft: Soft Clustered Federated Learning with Proximal Local Updating

Yichen Ruan, Carlee Joe-Wong

Interpretable Neural Subgraph Matching for Graph Retrieval

Indradyumna Roy, Venkata Sai Baba Reddy Velugoti, Soumen Chakrabarti, Abir De

Offline Reinforcement Learning as Anti-exploration

Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Leonard Hussenot, Olivier Bachem

Conditional Loss and Deep Euler Scheme for Time Series Generation

Carl Remlinger, Joseph Mikael, Romuald Elie

On Causally Disentangled Representations

Abbavaram Gowtham Reddy, Benin Godfrey L, Vineeth N Balasubramanian

Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments

Mitas Ray, Lillian J. Ratliff, Dmitriy Drusvyatskiy, Maryam Fazel

Sublinear Time Approximation of Text Similarity Matrices

Archan Ray, Nicholas Monath, Andrew McCallum, Cameron Musco

DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems

Martin Rapp, Ramin Khalili, Kilian Pfeiffer, Jörg Henkel

Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification

Anshuka Rangi, Long Tran-Thanh, Haifeng Xu, Massimo Franceschetti

I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding

Sirisha Rambhatla, Zhengping Che, Yan Liu

Federated Nearest Neighbor Classification with a Colony of Fruit-Flies

Parikshit Ram, Kaushik Sinha

DeepType 2: Superhuman Entity Linking, All You Need Is Type Interactions

Jonathan Raiman

Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model

Xin Qiu, Risto Miikkulainen

How Private Is Your RL Policy? An Inverse RL Based Analysis Framework

Kritika Prakash, Fiza Husain, Praveen Paruchuri, Sujit Gujar

Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learning

Wachirawit Ponghiran, Kaushik Roy

On the Impossibility of Non-trivial Accuracy in Presence of Fairness Constraints

Carlos Pinzón, Catuscia Palamidessi, Pablo Piantanida, Frank Valencia

Covered Information Disentanglement: Model Transparency via Unbiased Permutation Importance

João P. B. Pereira, Erik S. G. Stroes, Aeilko H. Zwinderman, Evgeni Levin

CC-CERT: A Probabilistic Approach to Certify General Robustness of Neural Networks

Mikhail Pautov, Nurislam Tursynbek, Marina Munkhoeva, Nikita Muravev, Aleksandr Petiushko

Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation

Joonhyung Park, Hajin Shim, Eunho Yang

Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing

Joonhyung Park, June Yong Yang, Jinwoo Shin, Sung Ju Hwang, Eunho Yang

Deformable Graph Convolutional Networks

Jinyoung Park, Sungdong Yoo, Jihwan Park, Hyunwoo J. Kim

Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning

Giseung Park, Sungho Choi, Youngchul Sung

Competing Mutual Information Constraints with Stochastic Competition-Based Activations for Learning Diversified Representations

Konstantinos P. Panousis, Anastasios Antoniadis, Sotirios Chatzis

Bag Graph: Multiple Instance Learning Using Bayesian Graph Neural Networks

Soumyasundar Pal, Antonios Valkanas, Florence Regol, Mark Coates

Random Tensor Theory for Tensor Decomposition

Mohamed Ouerfelli, Mohamed Tamaazousti, Vincent Rivasseau

Clustering Approach to Solve Hierarchical Classification Problem Complexity

Aomar Osmani, Massinissa Hamidi, Pegah Alizadeh

Improving Evidential Deep Learning via Multi-Task Learning

Dongpin Oh, Bonggun Shin

Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation

Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon

Out of Distribution Data Detection Using Dropout Bayesian Neural Networks

Andre T. Nguyen, Fred Lu, Gary Lopez Munoz, Edward Raff, Charles Nicholas

A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations

Krishna Prasad Neupane, Ervine Zheng, Yu Kong, Qi Yu

Is Your Data Relevant?: Dynamic Selection of Relevant Data for Federated Learning

Lokesh Nagalapatti, Ruhi Sharma Mittal, Ramasuri Narayanam

Unsupervised Reinforcement Learning in Multiple Environments

Mirco Mutti, Mattia Mancassola, Marcello Restelli

Constraint Sampling Reinforcement Learning: Incorporating Expertise for Faster Learning

Tong Mu, Georgios Theocharous, David Arbour, Emma Brunskill

Provable Guarantees for Understanding Out-of-Distribution Detection

Peyman Morteza, Yixuan Li

Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks

Seungyong Moon, Gaon An, Hyun Oh Song

Learning Bayesian Networks in the Presence of Structural Side Information

Ehsan Mokhtarian, Sina Akbari, Fateme Jamshidi, Jalal Etesami, Negar Kiyavash

The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection

Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar

Simple Unsupervised Graph Representation Learning

Yujie Mo, Liang Peng, Jie Xu, Xiaoshuang Shi, Xiaofeng Zhu

An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity

Siddhartha Mishra, Nicholas Monath, Michael Boratko, Ariel Kobren, Andrew McCallum

Temporal Knowledge Graph Completion Using Box Embeddings

Johannes Messner, Ralph Abboud, İsmail İlkan Ceylan

Top-Down Deep Clustering with Multi-Generator GANs

Daniel P. M. de Mello, Renato M. Assunção, Fabricio Murai

What Can We Learn Even from the Weakest? Learning Sketches for Programmatic Strategies

Leandro C. Medeiros, David S. Aleixo, Levi H. S. Lelis

Curiosity-Driven Exploration via Latent Bayesian Surprise

Pietro Mazzaglia, Ozan Catal, Tim Verbelen, Bart Dhoedt

Coordinate Descent on the Orthogonal Group for Recurrent Neural Network Training

Estelle Massart, Vinayak Abrol

An Experimental Design Approach for Regret Minimization in Logistic Bandits

Blake Mason, Kwang-Sung Jun, Lalit Jain

Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks

Simone Marullo, Matteo Tiezzi, Marco Gori, Stefano Melacci

When AI Difficulty Is Easy: The Explanatory Power of Predicting IRT Difficulty

Fernando Martínez-Plumed, David Castellano, Carlos Monserrat-Aranda, José Hernández-Orallo

fGOT: Graph Distances Based on Filters and Optimal Transport

Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia, Pascal Frossard

Exploring Safer Behaviors for Deep Reinforcement Learning

Enrico Marchesini, Davide Corsi, Alessandro Farinelli

Hard to Forget: Poisoning Attacks on Certified Machine Unlearning

Neil G. Marchant, Benjamin I. P. Rubinstein, Scott Alfeld

Cross-Lingual Adversarial Domain Adaptation for Novice Programming

Ye Mao, Farzaneh Khoshnevisan, Thomas Price, Tiffany Barnes, Min Chi

Sharp Restricted Isometry Property Bounds for Low-Rank Matrix Recovery Problems with Corrupted Measurements

Ziye Ma, Yingjie Bi, Javad Lavaei, Somayeh Sojoudi

Recursive Reasoning Graph for Multi-Agent Reinforcement Learning

Xiaobai Ma, David Isele, Jayesh K. Gupta, Kikuo Fujimura, Mykel J. Kochenderfer

Efficient Continuous Control with Double Actors and Regularized Critics

Jiafei Lyu, Xiaoteng Ma, Jiangpeng Yan, Xiu Li

Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning

Gengyu Lyu, Xiang Deng, Yanan Wu, Songhe Feng

Adapt to Environment Sudden Changes by Learning a Context Sensitive Policy

Fan-Ming Luo, Shengyi Jiang, Yang Yu, Zongzhang Zhang, Yi-Feng Zhang

Frozen Pretrained Transformers as Universal Computation Engines

Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch

Fast and Data Efficient Reinforcement Learning from Pixels via Non-parametric Value Approximation

Alexander Long, Alan Blair, Herke van Hoof

Optimistic Initialization for Exploration in Continuous Control

Sam Lobel, Omer Gottesman, Cameron Allen, Akhil Bagaria, George Konidaris

Deep Graph Clustering via Dual Correlation Reduction

Yue Liu, Wenxuan Tu, sihang zhou, Xinwang Liu, Linxuan Song

Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching

Xin Liu, Yangqiu Song

Trusted Multi-View Deep Learning with Opinion Aggregation

Wei Liu, Xiaodong Yue, Yufei Chen, Thierry Denoeux

Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors

Suyuan Liu, Siwei Wang, Pei Zhang, Kai Xu, Xinwang Liu

Transformer with Memory Replay

Rui Liu, Barzan Mozafari

Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning

Jinxin Liu, Donglin Wang, Qiangxing Tian, Zhengyu Chen

Deep Amortized Relational Model with Group-Wise Hierarchical Generative Process

Huafeng Liu, Tong Zhou, Jiaqi Wang

Stationary Diffusion State Neural Estimation for Multiview Clustering

Chenghua Liu, Zhuolin Liao, Yixuan Ma, Kun Zhan

Learning Parameterized Task Structure for Generalization to Unseen Entities

Anthony Liu, Sungryull Sohn, Mahdi Qazwini, Honglak Lee

Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization

Pierre Liotet, Francesco Vidaich, Alberto Maria Metelli, Marcello Restelli

Conjugated Discrete Distributions for Distributional Reinforcement Learning

Björn Lindenberg, Jonas Nordqvist, Karl-Olof Lindahl

RareGAN: Generating Samples for Rare Classes

Zinan Lin, Hao Liang, Giulia Fanti, Vyas Sekar

SCRIB: Set-Classifier with Class-Specific Risk Bounds for Blackbox Models

Zhen Lin, Lucas Glass, M. Brandon Westover, Cao Xiao, Jimeng Sun

Deep Unsupervised Hashing with Latent Semantic Components

Qinghong Lin, Xiaojun Chen, Qin Zhang, Shaotian Cai, Wenzhe Zhao

On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations

Li-Chung Lin, Cheng-Hung Liu, Chih-Ming Chen, Kai-Chin Hsu, I-Feng Wu

Conditional Local Convolution for Spatio-Temporal Meteorological Forecasting

Haitao Lin, Zhangyang Gao, Yongjie Xu, Lirong Wu, Ling Li

Robust Graph-Based Multi-View Clustering

Weixuan Liang, Xinwang Liu, sihang zhou, Jiyuan Liu, Siwei Wang

Zeroth-Order Optimization for Composite Problems with Functional Constraints

Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu

Sharp Analysis of Random Fourier Features in Classification

Zhu Li

A Hybrid Causal Structure Learning Algorithm for Mixed-Type Data

Yan Li, Rui Xia, Chunchen Liu, Liang Sun

A Fully Single Loop Algorithm for Bilevel Optimization without Hessian Inverse

Junyi Li, Bin Gu, Heng Huang

Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model

Jialian Li, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu

Chunk Dynamic Updating for Group Lasso with ODEs

Diyang Li, Bin Gu

Invariant Information Bottleneck for Domain Generalization

Bo Li, Yifei Shen, Yezhen Wang, Wenzhen Zhu, Colorado Reed

Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates

Dan Ley, Umang Bhatt, Adrian Weller

Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling

Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin

Augmentation-Free Self-Supervised Learning on Graphs

Namkyeong Lee, Junseok Lee, Chanyoung Park

Fast and Efficient MMD-Based Fair PCA via Optimization over Stiefel Manifold

Junghyun Lee, Gwangsu Kim, Mahbod Olfat, Mark Hasegawa-Johnson, Chang D. Yoo

Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks

Jun-Tae Lee, Hyunsin Park, Sungrack Yun, Simyung Chang

Differentially Private Normalizing Flows for Synthetic Tabular Data Generation

Jaewoo Lee, Minjung Kim, Yonghyun Jeong, Youngmin Ro