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07/20/2023
Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples
Backdoor attacks are serious security threats to machine learning models...
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06/29/2023
Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features
Recent studies have demonstrated the susceptibility of deep neural netwo...
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06/08/2023
Negotiated Reasoning: On Provably Addressing Relative Over-Generalization
Over-generalization is a thorny issue in cognitive science, where people...
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05/18/2023
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning
The difficulty of appropriately assigning credit is particularly heighte...
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05/10/2023
Learning Optimal "Pigovian Tax" in Sequential Social Dilemmas
In multi-agent reinforcement learning, each agent acts to maximize its i...
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05/08/2023
Information Design in Multi-Agent Reinforcement Learning
Reinforcement learning (RL) mimics how humans and animals interact with ...
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02/23/2023
Diverse Policy Optimization for Structured Action Space
Enhancing the diversity of policies is beneficial for robustness, explor...
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01/31/2023
Learning Roles with Emergent Social Value Orientations
Social dilemmas can be considered situations where individual rationalit...
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12/30/2022
Estimating Latent Population Flows from Aggregated Data via Inversing Multi-Marginal Optimal Transport
We study the problem of estimating latent population flows from aggregat...
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10/31/2022
Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning
The permutation flow shop scheduling (PFSS), aiming at finding the optim...
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10/09/2022
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport
Few-shot classification aims to learn a classifier to recognize unseen c...
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09/15/2022
Differentially Private Estimation of Hawkes Process
Point process models are of great importance in real world applications....
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08/05/2022
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
Imbalanced data pose challenges for deep learning based classification m...
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06/25/2022
BackdoorBench: A Comprehensive Benchmark of Backdoor Learning
Backdoor learning is an emerging and important topic of studying the vul...
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02/28/2022
Learning Neural Hamiltonian Dynamics: A Methodological Overview
The past few years have witnessed an increased interest in learning Hami...
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02/09/2022
Obtaining Dyadic Fairness by Optimal Transport
Fairness has been taken as a critical metric on machine learning models....
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02/08/2022
Multi-Agent Path Finding with Prioritized Communication Learning
Multi-agent pathfinding (MAPF) has been widely used to solve large-scale...
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12/20/2021
Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning
Critical sectors of human society are progressing toward the adoption of...
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12/09/2021
VMAgent: Scheduling Simulator for Reinforcement Learning
A novel simulator called VMAgent is introduced to help RL researchers be...
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10/18/2021
Learning Prototype-oriented Set Representations for Meta-Learning
Learning from set-structured data is a fundamental problem that has rece...
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10/18/2021
Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient
Improving the resilience of a network protects the system from natural d...
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10/15/2021
Low-rank Matrix Recovery With Unknown Correspondence
We study a matrix recovery problem with unknown correspondence: given th...
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09/15/2021
Self-Training with Differentiable Teacher
Self-training achieves enormous success in various semi-supervised and w...
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09/08/2021
Sqrt(d) Dimension Dependence of Langevin Monte Carlo
This article considers the popular MCMC method of unadjusted Langevin Mo...
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06/03/2021
Influence Estimation and Maximization via Neural Mean-Field Dynamics
We propose a novel learning framework using neural mean-field (NMF) dyna...
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05/18/2021
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
Multi-agent reinforcement learning (MARL) becomes more challenging in th...
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04/23/2021
Theoretical Study of Random Noise Defense against Query-Based Black-Box Attacks
The query-based black-box attacks, which don't require any knowledge abo...
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03/14/2021
Mean Field Game GAN
We propose a novel mean field games (MFGs) based GAN(generative adversar...
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03/05/2021
Graph-Based Tri-Attention Network for Answer Ranking in CQA
In community-based question answering (CQA) platforms, automatic answer ...
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03/01/2021
Reinforcement Learning for Adaptive Mesh Refinement
Large-scale finite element simulations of complex physical systems gover...
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02/21/2021
Dealing with Non-Stationarity in Multi-Agent Reinforcement Learning via Trust Region Decomposition
Non-stationarity is one thorny issue in multi-agent reinforcement learni...
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02/09/2021
Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning
When solving a complex task, humans will spontaneously form teams and to...
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02/05/2021
Learning High Dimensional Wasserstein Geodesics
We propose a new formulation and learning strategy for computing the Was...
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02/04/2021
Hawkes Processes on Graphons
We propose a novel framework for modeling multiple multivariate point pr...
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12/19/2020
Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances
For the traveling salesman problem (TSP), the existing supervised learni...
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12/10/2020
Learning Graphons via Structured Gromov-Wasserstein Barycenters
We propose a novel and principled method to learn a nonparametric graph ...
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11/30/2020
A Hypergradient Approach to Robust Regression without Correspondence
We consider a regression problem, where the correspondence between input...
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11/08/2020
Reliable Off-policy Evaluation for Reinforcement Learning
In a sequential decision-making problem, off-policy evaluation (OPE) est...
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07/09/2020
Inductive Relational Matrix Completion
Data sparsity and cold-start issues emerge as two major bottlenecks for ...
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07/07/2020
GraphOpt: Learning Optimization Models of Graph Formation
Formation mechanisms are fundamental to the study of complex networks, b...
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06/29/2020
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification
Graph neural networks are promising architecture for learning and infere...
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06/16/2020
Network Diffusions via Neural Mean-Field Dynamics
We propose a novel learning framework based on neural mean-field dynamic...
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06/16/2020
Hessian-Free High-Resolution Nesterov Acceleration for Sampling
We propose an accelerated-gradient-based MCMC method. It relies on a mod...
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06/16/2020
Hessian-Free High-Resolution Nesterov Accelerationfor Sampling
We propose an accelerated-gradient-based MCMC method. It relies on a mod...
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06/10/2020
Learning to Incentivize Other Learning Agents
The challenge of developing powerful and general Reinforcement Learning ...
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04/17/2020
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning
Traditional centralized multi-agent reinforcement learning (MARL) algori...
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02/22/2020
Learning Cost Functions for Optimal Transport
Learning the cost function for optimal transport from observed transport...
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02/21/2020
Transformer Hawkes Process
Modern data acquisition routinely produce massive amounts of event seque...
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02/20/2020
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients
Common Stochastic Gradient MCMC methods approximate gradients by stochas...
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02/19/2020