AAAI 2022

1,624 papers

Learning Losses for Strategic Classification

Tosca Lechner, Ruth Urner

Stability Verification in Stochastic Control Systems via Neural Network Supermartingales

Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger

Episodic Policy Gradient Training

Hung Le, Majid Abdolshah, Thommen K. George, Kien Do, Dung Nguyen

Interpretable Clustering via Multi-Polytope Machines

Connor Lawless, Jayant Kalagnanam, Lam M Nguyen, Dzung Phan, Chandra Reddy

Optimization for Classical Machine Learning Problems on the GPU

Sören Laue, Mark Blacher, Joachim Giesen

Learning Not to Learn: Nature versus Nurture In Silico

Robert Tjarko Lange, Henning Sprekeler

Learning Adversarial Markov Decision Processes with Delayed Feedback

Tal Lancewicki, Aviv Rosenberg, Yishay Mansour

Tight Neural Network Verification via Semidefinite Relaxations and Linear Reformulations

Jianglin Lan, Yang Zheng, Alessio Lomuscio

TrustAL: Trustworthy Active Learning Using Knowledge Distillation

Beong-woo Kwak, Youngwook Kim, Yu Jin Kim, Seung-won Hwang, Jinyoung Yeo

Gradient Based Activations for Accurate Bias-Free Learning

Vinod K. Kurmi, Rishabh Sharma, Yash Vardhan Sharma, Vinay P Namboodiri

Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization

Yufei Kuang, Miao Lu, Jie Wang, Qi Zhou, Bin Li

Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method

James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck

Deep Reinforcement Learning Policies Learn Shared Adversarial Features across MDPs

Ezgi Korkmaz

HNO: High-Order Numerical Architecture for ODE-Inspired Deep Unfolding Networks

Lin Kong, Wei Sun, Fanhua Shang, Yuanyuan Liu, Hongying Liu

Directed Graph Auto-Encoders

Georgios Kollias, Vasileios Kalantzis, Tsuyoshi Ide, Aurélie Lozano, Naoki Abe

Introducing Symmetries to Black Box Meta Reinforcement Learning

Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram Friesen, Junhyuk Oh

Towards a Rigorous Evaluation of Time-Series Anomaly Detection

Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, Sungroh Yoon

Fast Monte-Carlo Approximation of the Attention Mechanism

Hyunjun Kim, JeongGil Ko

A Nested Bi-level Optimization Framework for Robust Few Shot Learning

Krishnateja Killamsetty, Changbin Li, Chen Zhao, Feng Chen, Rishabh Iyer

A Computable Definition of the Spectral Bias

Jonas Kiessling, Filip Thor

The Effect of Manifold Entanglement and Intrinsic Dimensionality on Learning

Daniel Kienitz, Ekaterina Komendantskaya, Michael Lones

Spatial Frequency Bias in Convolutional Generative Adversarial Networks

Mahyar Khayatkhoei, Ahmed Elgammal

Same State, Different Task: Continual Reinforcement Learning without Interference

Samuel Kessler, Jack Parker-Holder, Philip Ball, Stefan Zohren, Stephen J. Roberts

Dist2Cycle: A Simplicial Neural Network for Homology Localization

Alexandros D Keros, Vidit Nanda, Kartic Subr

Optimal Tensor Transport

Tanguy Kerdoncuff, Rémi Emonet, Michaël Perrot, Marc Sebban

Partial Wasserstein Covering

Keisuke Kawano, Satoshi Koide, Keisuke Otaki

iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection

Ramneet Kaur, Susmit Jha, Anirban Roy, Sangdon Park, Edgar Dobriban

Instance-Sensitive Algorithms for Pure Exploration in Multinomial Logit Bandit

Nikolai Karpov, Qin Zhang

Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired Classifiers

Payam Karisani

Self-Supervised Enhancement of Latent Discovery in GANs

Adarsh Kappiyath, Silpa Vadakkeeveetil Sreelatha, S. Sumitra

Classifying Emails into Human vs Machine Category

Changsung Kang, Hongwei Shang, Jean-Marc Langlois

Fully Spiking Variational Autoencoder

Hiromichi Kamata, Yusuke Mukuta, Tatsuya Harada

Adaptive Kernel Graph Neural Network

Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Yanfang Ye

Creativity of AI: Automatic Symbolic Option Discovery for Facilitating Deep Reinforcement Learning

Mu Jin, Zhihao Ma, Kebing Jin, Hankz Hankui Zhuo, Chen Chen

Fast Graph Neural Tangent Kernel via Kronecker Sketching

Shunhua Jiang, Yunze Man, Zhao Song, zheng Yu, Danyang Zhuo

Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data

Shenwang Jiang, Jianan Li, Ying Wang, Bo Huang, Zhang Zhang

Learning Expected Emphatic Traces for Deep RL

Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt

Label Hallucination for Few-Shot Classification

Yiren Jian, Lorenzo Torresani

Towards Discriminant Analysis Classifiers Using Online Active Learning via Myoelectric Interfaces

Andres G Jaramillo-Yanez, Marco E. Benalcázar, Sebastian Sardina, Fabio Zambetta

Group-Aware Threshold Adaptation for Fair Classification

Taeuk Jang, Pengyi Shi, Xiaoqian Wang

Causal Discovery in Hawkes Processes by Minimum Description Length

Amirkasra Jalaldoust, Kateřina Hlaváčková-Schindler, Claudia Plant

Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes

Guillermo Infante, Anders Jonsson, Vicenç Gómez

Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets

Yingsong Huang, Bing Bai, Shengwei Zhao, Kun Bai, Fei Wang

Achieving Counterfactual Fairness for Causal Bandit

Wen Huang, Lu Zhang, Xintao Wu

Multi-View Clustering on Topological Manifold

Shudong Huang, Ivor Tsang, Zenglin Xu, Jiancheng Lv, Quan-Hui Liu

Towards Automating Model Explanations with Certified Robustness Guarantees

Mengdi Huai, Jinduo Liu, Chenglin Miao, Liuyi Yao, Aidong Zhang

Adversarial Examples Can Be Effective Data Augmentation for Unsupervised Machine Learning

Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Chia-Mu Yu

Anytime Guarantees under Heavy-Tailed Data

Matthew J. Holland

Reinforcement Learning of Causal Variables Using Mediation Analysis

Tue Herlau, Rasmus Larsen

Toward Physically Realizable Quantum Neural Networks

Mohsen Heidari, Ananth Grama, Wojciech Szpankowski

Multi-Mode Tensor Space Clustering Based on Low-Tensor-Rank Representation

Yicong He, George K. Atia

Wasserstein Unsupervised Reinforcement Learning

Shuncheng He, Yuhang Jiang, Hongchang Zhang, Jianzhun Shao, Xiangyang Ji

Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning under Class Distribution Mismatch

Rundong He, Zhongyi Han, Yang Yang, Yilong Yin

SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data

Chaoyang He, Emir Ceyani, Keshav Balasubramanian, Murali Annavaram, Salman Avestimehr

Cross-Domain Few-Shot Graph Classification

Kaveh Hassani

End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification

Jun-Yi Hang, Min-ling Zhang, yanghe feng, Xiaocheng Song

Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing

Bing Han, Cheng Wang, Kaushik Roy

A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions

Anthony GX-Chen, Veronica Chelu, Blake A. Richards, Joelle Pineau

TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs

Shubham Gupta, Sahil Manchanda, Srikanta Bedathur, Sayan Ranu

Improved Gradient-Based Adversarial Attacks for Quantized Networks

Kartik Gupta, Thalaiyasingam Ajanthan

Self-Supervised Pre-training for Protein Embeddings Using Tertiary Structures

Yuzhi Guo, Jiaxiang Wu, Hehuan Ma, Junzhou Huang

Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks

Yijie Guo, Qiucheng Wu, Honglak Lee

Adaptive Orthogonal Projection for Batch and Online Continual Learning

Yiduo Guo, Wenpeng Hu, Dongyan Zhao, Bing Liu

Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods

Xin Guo, Anran Hu, Junzi Zhang

Balanced Self-Paced Learning for AUC Maximization

Bin Gu, Chenkang Zhang, Huan Xiong, Heng Huang

GoTube: Scalable Statistical Verification of Continuous-Depth Models

Sophie A. Gruenbacher, Mathias Lechner, Ramin Hasani, Daniela Rus, Thomas A. Henzinger

Semi-supervised Conditional Density Estimation with Wasserstein Laplacian Regularisation

Olivier Graffeuille, Yun Sing Koh, Jörg Wicker, Moritz K Lehmann

LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks

Adam Goodge, Bryan Hooi, See-Kiong Ng, Wee Siong Ng

Partial Multi-Label Learning via Large Margin Nearest Neighbour Embeddings

Xiuwen Gong, Dong Yuan, Wei Bao

Regularized Modal Regression on Markov-Dependent Observations: A Theoretical Assessment

Tieliang Gong, Yuxin Dong, Hong Chen, Wei Feng, Bo Dong

Learning Large DAGs by Combining Continuous Optimization and Feedback Arc Set Heuristics

Pierre Gillot, Pekka Parviainen

DiPS: Differentiable Policy for Sketching in Recommender Systems

Aritra Ghosh, Saayan Mitra, Andrew Lan

Recovering the Propensity Score from Biased Positive Unlabeled Data

Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke Rundensteiner

Algorithmic Concept-Based Explainable Reasoning

Dobrik Georgiev, Pietro Barbiero, Dmitry Kazhdan, Petar Veličković, Pietro Lió

Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion

Ruiqi Gao, Jianwen Xie, Siyuan Huang, Yufan Ren, Song-chun Zhu

Enhancing Counterfactual Classification Performance via Self-Training

Ruijiang Gao, Max Biggs, Wei Sun, Ligong Han

Smoothing Advantage Learning

Yaozhong Gan, Zhe Zhang, Xiaoyang Tan

JFB: Jacobian-Free Backpropagation for Implicit Networks

Samy Wu Fung, Howard Heaton, Qiuwei Li, Daniel Mckenzie, Stanley Osher

Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting

Yuwei Fu, Di Wu, Benoit Boulet

Modification-Fair Cluster Editing

Vincent Froese, Leon Kellerhals, Rolf Niedermeier

Scaling Neural Program Synthesis with Distribution-Based Search

Nathanaël Fijalkow, Guillaume Lagarde, Théo Matricon, Kevin Ellis, Pierre Ohlmann

KerGNNs: Interpretable Graph Neural Networks with Graph Kernels

Aosong Feng, Chenyu You, Shiqiang Wang, Leandros Tassiulas

Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification

Zhiyu Fang, Xiaobin Zhu, Chun Yang, Zheng Han, Jingyan Qin

Up to 100x Faster Data-Free Knowledge Distillation

Gongfan Fang, Kanya Mo, Xinchao Wang, Jie Song, Shitao Bei

Dynamic Nonlinear Matrix Completion for Time-Varying Data Imputation

Jicong Fan

Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win

Utku Evci, Yani Ioannou, Cem Keskin, Yann Dauphin

Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP

Sepideh Esmaeilpour, Bing Liu, Eric Robertson, Lei Shu

Adaptive and Universal Algorithms for Variational Inequalities with Optimal Convergence

Alina Ene, Huy Lê Nguyễn

Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples

Wei Duan, Junyu Xuan, Maoying Qiao, Jie Lu

Disentangled Spatiotemporal Graph Generative Models

Yuanqi Du, Xiaojie Guo, Hengning Cao, Yanfang Ye, Liang Zhao

Online Certification of Preference-Based Fairness for Personalized Recommender Systems

Virginie Do, Sam Corbett-Davies, Jamal Atif, Nicolas Usunier

Meta Propagation Networks for Graph Few-shot Semi-supervised Learning

Kaize Ding, Jianling Wang, James Caverlee, Huan Liu

Bayesian Optimization over Permutation Spaces

Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa, Dae Hyun Kim

Reducing Flipping Errors in Deep Neural Networks

Xiang Deng, Yun Xiao, Bo Long, Zhongfei Zhang

Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes

Florent Delgrange, Ann Nowe, Guillermo A. Pérez

Gradient Temporal Difference with Momentum: Stability and Convergence

Rohan Deb, Shalabh Bhatnagar

First-Order Convex Fitting and Its Application to Economics and Optimization

Quinlan Dawkins, Minbiao Han, Haifeng Xu

KOALA: A Kalman Optimization Algorithm with Loss Adaptivity

Aram Davtyan, Sepehr Sameni, Llukman Cerkezi, Givi Meishvili, Adam Bielski

How Good Are Low-Rank Approximations in Gaussian Process Regression?

Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos

Implicit Gradient Alignment in Distributed and Federated Learning

Yatin Dandi, Luis Barba, Martin Jaggi