AAAI 2022

1,624 papers

Generalized Stochastic Matching

Alireza Farhadi, Jacob Gilbert, MohammadTaghi Hajiaghayi

Fast and More Powerful Selective Inference for Sparse High-Order Interaction Model

Diptesh Das, Vo Nguyen Le Duy, Hiroyuki Hanada, Koji Tsuda, Ichiro Takeuchi

Entropy Estimation via Normalizing Flow

Ziqiao Ao, Jinglai Li

MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems

Zefang Zong, Meng Zheng, Yong Li, Depeng Jin

Efficient Device Scheduling with Multi-Job Federated Learning

Chendi Zhou, Ji Liu, Juncheng Jia, Jingbo Zhou, Yang Zhou

Equity Promotion in Online Resource Allocation

Pan Xu, Yifan Xu

Stochastic Goal Recognition Design Problems with Suboptimal Agents

Christabel Wayllace, William Yeoh

Classical Planning with Avoid Conditions

Marcel Steinmetz, Jorg Hoffmann, Alisa Kovtunova, Stefan Borgwardt

Qubit Routing Using Graph Neural Network Aided Monte Carlo Tree Search

Animesh Sinha, Utkarsh Azad, Harjinder Singh

Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring

Yunzhuang Shen, Yuan Sun, Xiaodong Li, Andrew Eberhard, Andreas Ernst

Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction

Marc Rigter, Danial Dervovic, Parisa Hassanzadeh, Jason Long, Parisa Zehtabi

Efficient Encoding of Cost Optimal Delete-Free Planning as SAT

Masood Feyzbakhsh Rankooh, Jussi Rintanen

Formula Synthesis in Propositional Dynamic Logic with Shuffle

Sophie Pinchinat, Sasha Rubin, François Schwarzentruber

A Distributional Framework for Risk-Sensitive End-to-End Planning in Continuous MDPs

Noah Patton, Jihwan Jeong, Mike Gimelfarb, Scott Sanner

Deciding Unsolvability in Temporal Planning under Action Non-Self-Overlapping

Stefan Panjkovic, Andrea Micheli, Alessandro Cimatti

Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability Using Tree Search and Graph Neural Networks

Kevin Osanlou, Jeremy Frank, Andrei Bursuc, Tristan Cazenave, Eric Jacopin

Differential Assessment of Black-Box AI Agents

Rashmeet Kaur Nayyar, Pulkit Verma, Siddharth Srivastava

Risk-Aware Stochastic Shortest Path

Tobias Meggendorfer

Bridging LTLf Inference to GNN Inference for Learning LTLf Formulae

Weilin Luo, Pingjia Liang, Jianfeng Du, Hai Wan, Bo Peng

Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs

Siow Meng Low, Akshat Kumar, Scott Sanner

Planning to Avoid Side Effects

Toryn Q. Klassen, Sheila A. McIlraith, Christian Muise, Jarvis Xu

NICE: Robust Scheduling through Reinforcement Learning-Guided Integer Programming

Luke Kenworthy, Siddharth Nayak, Christopher Chin, Hamsa Balakrishnan

A* Search and Bound-Sensitive Heuristics for Oversubscription Planning

Michael Katz, Emil Keyder

Bounding Quality in Diverse Planning

Michael Katz, Shirin Sohrabi, Octavian Udrea

Learning Probably Approximately Complete and Safe Action Models for Stochastic Worlds

Brendan Juba, Roni Stern

Learning to Solve Routing Problems via Distributionally Robust Optimization

Yuan Jiang, Yaoxin Wu, Zhiguang Cao, Jie Zhang

Speeding Up the RUL¯ Dynamic-Controllability-Checking Algorithm for Simple Temporal Networks with Uncertainty

Luke Hunsberger, Roberto Posenato

Homomorphisms of Lifted Planning Tasks: The Case for Delete-Free Relaxation Heuristics

Rostislav Horčík, Daniel Fišer, Álvaro Torralba

Reconfiguring Shortest Paths in Graphs

Kshitij Gajjar, Agastya Vibhuti Jha, Manish Kumar, Abhiruk Lahiri

Operator-Potential Heuristics for Symbolic Search

Daniel Fišer, Álvaro Torralba, Jorg Hoffmann

Explainable Planner Selection for Classical Planning

Patrick Ferber, Jendrik Seipp

Robustification of Online Graph Exploration Methods

Franziska Eberle, Alexander Lindermayr, Nicole Megow, Lukas Nölke, Jens Schlöter

Inconsistent Planning: When in Doubt, Toss a Coin!

Yuriy Dementiev, Fedor Fomin, Artur Ignatiev

The FF Heuristic for Lifted Classical Planning

Augusto B. Corrêa, Florian Pommerening, Malte Helmert, Guillem Francès

Competing for Resources: Estimating Adversary Strategy for Effective Plan Generation

Lukáš Chrpa, Pavel Rytíř, Rostislav Horčík, Stefan Edelkamp

PlanVerb: Domain-Independent Verbalization and Summary of Task Plans

Gerard Canal, Senka Krivić, Paul Luff, Andrew Coles

Making Translations to Classical Planning Competitive with Other HTN Planners

Gregor Behnke, Florian Pollitt, Daniel Höller, Pascal Bercher, Ron Alford

Synthesis from Satisficing and Temporal Goals

Suguman Bansal, Lydia Kavraki, Moshe Y. Vardi, Andrew Wells

Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise

Thom S. Badings, Alessandro Abate, Nils Jansen, David Parker, Hasan A. Poonawala

Extended Goal Recognition Design with First-Order Computation Tree Logic

Tsz-Chiu Au

Online Search with Best-Price and Query-Based Predictions

Spyros Angelopoulos, Shahin Kamali, Dehou Zhang

Goal Recognition as Reinforcement Learning

Leonardo Amado, Reuth Mirsky, Felipe Meneguzzi

Formal Semantics and Formally Verified Validation for Temporal Planning

Mohammad Abdulaziz, Lukas Koller

Do Feature Attribution Methods Correctly Attribute Features?

Yilun Zhou, Serena Booth, Marco Tulio Ribeiro, Julie Shah

Unsupervised Causal Binary Concepts Discovery with VAE for Black-Box Model Explanation

Thien Q Tran, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma

Fast Sparse Decision Tree Optimization via Reference Ensembles

Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen

DeepAuth: A DNN Authentication Framework by Model-Unique and Fragile Signature Embedding

Yingjie Lao, Weijie Zhao, Peng Yang, Ping Li

On the Fairness of Causal Algorithmic Recourse

Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller

Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks

Jinyuan Jia, Yupei Liu, Xiaoyu Cao, Neil Zhenqiang Gong

Shaping Noise for Robust Attributions in Neural Stochastic Differential Equations

Sumit Kumar Jha, Rickard Ewetz, Alvaro Velasquez, Arvind Ramanathan, Susmit Jha

Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values

Haewon Jeong, Hao Wang, Flavio P. Calmon

Achieving Long-Term Fairness in Sequential Decision Making

Yaowei Hu, Lu Zhang

Algorithmic Fairness Verification with Graphical Models

Bishwamittra Ghosh, Debabrota Basu, Kuldeep S Meel

Path-Specific Objectives for Safer Agent Incentives

Sebastian Farquhar, Ryan Carey, Tom Everitt

Towards Debiasing DNN Models from Spurious Feature Influence

Mengnan Du, Ruixiang Tang, Weijie Fu, Xia Hu

Cosine Model Watermarking against Ensemble Distillation

Laurent Charette, Lingyang Chu, Yizhou Chen, Jian Pei, Lanjun Wang

Incorporating Item Frequency for Differentially Private Set Union

Ricardo Silva Carvalho, Ke Wang, Lovedeep Singh Gondara

Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness

Carolyn Ashurst, Ryan Carey, Silvia Chiappa, Tom Everitt

Equilibrium Finding in Normal-Form Games via Greedy Regret Minimization

Hugh Zhang, Adam Lerer, Noam Brown

MLink: Linking Black-Box Models for Collaborative Multi-Model Inference

Mu Yuan, Lan Zhang, Xiang-Yang Li

Multi-Agent Incentive Communication via Decentralized Teammate Modeling

Lei Yuan, Jianhao Wang, Fuxiang Zhang, Chenghe Wang, Zongzhang Zhang

Learning the Optimal Recommendation from Explorative Users

Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu

Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards

Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, Bryan Kian Hsiang Low

Decentralized Mean Field Games

Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart

How Many Representatives Do We Need? The Optimal Size of a Congress Voting on Binary Issues

Manon Revel, Tao LIN, Daniel Halpern

Finding Nontrivial Minimum Fixed Points in Discrete Dynamical Systems: Complexity, Special Case Algorithms and Heuristics

Zirou Qiu, Chen Chen, Madhav Marathe, S.S. Ravi, Daniel J. Rosenkrantz

Generalization in Mean Field Games by Learning Master Policies

Sarah Perrin, Mathieu Lauriere, Julien Pérolat, Romuald Elie, Matthieu Geist

When Can the Defender Effectively Deceive Attackers in Security Games?

Thanh Nguyen, Haifeng Xu

A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning

Xueguang Lyu, Andrea Baisero, Yuchen Xiao, Christopher Amato

Shard Systems: Scalable, Robust and Persistent Multi-Agent Path Finding with Performance Guarantees

Christopher Leet, Jiaoyang Li, Sven Koenig

MDPGT: Momentum-Based Decentralized Policy Gradient Tracking

Zhanhong Jiang, Xian Yeow Lee, Sin Yong Tan, Kai Liang Tan, Aditya Balu

Anytime Multi-Agent Path Finding via Machine Learning-Guided Large Neighborhood Search

Taoan Huang, Jiaoyang Li, Sven Koenig, Bistra Dilkina

Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs

Mingyu Guo, Jialiang Li, Aneta Neumann, Frank Neumann, Hung Nguyen

Cooperative Multi-Agent Fairness and Equivariant Policies

Niko A. Grupen, Bart Selman, Daniel D. Lee

Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems

Qingxu Fu, Tenghai Qiu, Jianqiang Yi, Zhiqiang Pu, Shiguang Wu

Pretrained Cost Model for Distributed Constraint Optimization Problems

Yanchen Deng, Shufeng Kong, Bo An

Participatory Budgeting with Donations and Diversity Constraints

Jiehua Chen, Martin Lackner, Jan Maly

Flex Distribution for Bounded-Suboptimal Multi-Agent Path Finding

Shao-Hung Chan, Jiaoyang Li, Graeme Gange, Daniel Harabor, Peter J. Stuckey

Fixation Maximization in the Positional Moran Process

Joachim Brendborg, Panagiotis Karras, Andreas Pavlogiannis, Asger Ullersted Rasmussen, Josef Tkadlec

Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams

Erdem Biyik, Anusha Lalitha, Rajarshi Saha, Andrea Goldsmith, Dorsa Sadigh

Hedonic Games with Fixed-Size Coalitions

Vittorio Bilò, Gianpiero Monaco, Luca Moscardelli

Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning

Roy Zohar, Shie Mannor, Guy Tennenholtz

Self-Adaptive Imitation Learning: Learning Tasks with Delayed Rewards from Sub-optimal Demonstrations

Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou

Invariant Action Effect Model for Reinforcement Learning

Zheng-Mao Zhu, Shengyi Jiang, Yu-Ren Liu, Yang Yu, Kun Zhang

Structural Landmarking and Interaction Modelling: A “SLIM” Network for Graph Classification

Yaokang Zhu, Kai Zhang, Jun Wang, Haibin Ling, Jie Zhang

Neural Piecewise-Constant Delay Differential Equations

Qunxi Zhu, Yifei Shen, Dongsheng Li, Wei LIN

Programmatic Reward Design by Example

Weichao Zhou, Wenchao Li

Adaptive Pairwise Weights for Temporal Credit Assignment

Zeyu Zheng, Risto Vuorio, Richard Lewis, Satinder Singh

Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms

Liyuan Zheng, Tanner Fiez, Zane Alumbaugh, Benjamin Chasnov, Lillian J. Ratliff

LaSSL: Label-Guided Self-Training for Semi-supervised Learning

Zhen Zhao, Luping Zhou, Lei Wang, Yinghuan Shi, Yang Gao

Online Missing Value Imputation and Change Point Detection with the Gaussian Copula

Yuxuan Zhao, Eric Landgrebe, Eliot Shekhtman, Madeleine Udell

Error-Based Knockoffs Inference for Controlled Feature Selection

Xuebin Zhao, Hong Chen, Yingjie Wang, Weifu Li, Tieliang Gong

Well-Classified Examples Are Underestimated in Classification with Deep Neural Networks

Guangxiang Zhao, Wenkai Yang, Xuancheng Ren, Lei Li, Yunfang Wu

FedInv: Byzantine-Robust Federated Learning by Inversing Local Model Updates

Bo Zhao, peng sun, Tao Wang, Keyu Jiang

CLPA: Clean-Label Poisoning Availability Attacks Using Generative Adversarial Nets

Bingyin Zhao, Yingjie Lao

Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles

Zhijie Zhang, Wei Chen, Xiaoming Sun, Jialin Zhang

Robust Action Gap Increasing with Clipped Advantage Learning

Zhe Zhang, Yaozhong Gan, Xiaoyang Tan

Learning to Solve Travelling Salesman Problem with Hardness-Adaptive Curriculum

Zeyang Zhang, Ziwei Zhang, Xin Wang, Wenwu Zhu

ProtGNN: Towards Self-Explaining Graph Neural Networks

ZAIXI ZHANG, Qi Liu, Hao Wang, Chengqiang Lu, Cheekong Lee

Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning

Yuheng Zhang, Hanghang Tong, Yinglong Xia, Yan Zhu, Yuejie Chi