b'Bo An'

research

∙ 03/27/2023

On the Importance of Feature Separability in Predicting Out-Of-Distribution Error

Estimating the generalization performance is practically challenging on ...

0 Renchunzi Xie, et al. ∙

research

∙ 02/07/2023

Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning

Recent advances in multi-agent reinforcement learning (MARL) allow agent...

0 Rundong Wang, et al. ∙

research

∙ 02/07/2023

Population-size-Aware Policy Optimization for Mean-Field Games

In this work, we attempt to bridge the two fields of finite-agent and in...

0 Pengdeng Li, et al. ∙

research

∙ 01/27/2023

Reinforcement Learning from Diverse Human Preferences

The complexity of designing reward functions has been a major obstacle t...

0 Wanqi Xue, et al. ∙

research

∙ 12/08/2022

Logit Clipping for Robust Learning against Label Noise

In the presence of noisy labels, designing robust loss functions is crit...

0 Hongxin Wei, et al. ∙

research

∙ 12/06/2022

PrefRec: Preference-based Recommender Systems for Reinforcing Long-term User Engagement

Current advances in recommender systems have been remarkably successful ...

0 Wanqi Xue, et al. ∙

research

∙ 10/18/2022

RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning

Despite the recent advancement in multi-agent reinforcement learning (MA...

5 Wei Qiu, et al. ∙

research

∙ 09/24/2022

Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems

Belief Propagation (BP) is an important message-passing algorithm for va...

0 Yanchen Deng, et al. ∙

research

∙ 07/12/2022

Offline Equilibrium Finding

Offline reinforcement learning (Offline RL) is an emerging field that ha...

0 Shuxin Li, et al. ∙

research

∙ 06/17/2022

Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets

Deep neural networks usually perform poorly when the training dataset su...

0 Hongxin Wei, et al. ∙

research

∙ 06/01/2022

ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor

Long-term engagement is preferred over immediate engagement in sequentia...

1 Wanqi Xue, et al. ∙

research

∙ 05/27/2022

Off-Beat Multi-Agent Reinforcement Learning

We investigate model-free multi-agent reinforcement learning (MARL) in e...

4 Wei Qiu, et al. ∙

research

∙ 05/19/2022

Mitigating Neural Network Overconfidence with Logit Normalization

Detecting out-of-distribution inputs is critical for safe deployment of ...

0 Hongxin Wei, et al. ∙

research

∙ 04/11/2022

A Unified Perspective on Deep Equilibrium Finding

Extensive-form games provide a versatile framework for modeling interact...

0 Xinrun Wang, et al. ∙

research

∙ 01/17/2022

NSGZero: Efficiently Learning Non-Exploitable Policy in Large-Scale Network Security Games with Neural Monte Carlo Tree Search

How resources are deployed to secure critical targets in networks can be...

0 Wanqi Xue, et al. ∙

research

∙ 01/16/2022

GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation

This paper studies weakly supervised domain adaptation(WSDA) problem, wh...

0 Renchunzi Xie, et al. ∙

research

∙ 12/08/2021

Pretrained Cost Model for Distributed Constraint Optimization Problems

Distributed Constraint Optimization Problems (DCOPs) are an important su...

0 Yanchen Deng, et al. ∙

research

∙ 12/04/2021

Cooperation, Retaliation and Forgiveness in Revision Games

Revision game is a very new model formulating the situation where player...

0 Dong Hao, et al. ∙

research

∙ 09/28/2021

Reinforcement Learning for Quantitative Trading

Quantitative trading (QT), which refers to the usage of mathematical mod...

0 Shuo Sun, et al. ∙

research

∙ 08/09/2021

Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning

Recent studies in multi-agent communicative reinforcement learning (MACR...

0 Wanqi Xue, et al. ∙

research

∙ 06/21/2021

Open-set Label Noise Can Improve Robustness Against Inherent Label Noise

Learning with noisy labels is a practically challenging problem in weakl...

6 Hongxin Wei, et al. ∙

research

∙ 06/16/2021

Multi-Class Classification from Single-Class Data with Confidences

Can we learn a multi-class classifier from only data of a single class? ...

0 Yuzhou Cao, et al. ∙

research

∙ 06/13/2021

Contingency-Aware Influence Maximization: A Reinforcement Learning Approach

The influence maximization (IM) problem aims at finding a subset of seed...

23 Haipeng Chen, et al. ∙

research

∙ 06/11/2021

On the Robustness of Average Losses for Partial-Label Learning

Partial-label (PL) learning is a typical weakly supervised classificatio...

0 Jiaqi Lv, et al. ∙

research

∙ 06/02/2021

Solving Large-Scale Extensive-Form Network Security Games via Neural Fictitious Self-Play

Securing networked infrastructures is important in the real world. The p...

0 Wanqi Xue, et al. ∙

research

∙ 05/18/2021

CFR-MIX: Solving Imperfect Information Extensive-Form Games with Combinatorial Action Space

In many real-world scenarios, a team of agents coordinate with each othe...

0 Shuxin Li, et al. ∙

research

∙ 02/18/2021

L2E: Learning to Exploit Your Opponent

Opponent modeling is essential to exploit sub-optimal opponents in strat...

0 Zhe Wu, et al. ∙

research

∙ 02/17/2021

DO-GAN: A Double Oracle Framework for Generative Adversarial Networks

In this paper, we propose a new approach to train Generative Adversarial...

8 Aye Phyu Phyu Aung, et al. ∙

research

∙ 02/16/2021

RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents

Current value-based multi-agent reinforcement learning methods optimize ...

0 Wei Qiu, et al. ∙

research

∙ 02/13/2021

Learning from Similarity-Confidence Data

Weakly supervised learning has drawn considerable attention recently to ...

0 Yuzhou Cao, et al. ∙

research

∙ 12/23/2020

Commission Fee is not Enough: A Hierarchical Reinforced Framework for Portfolio Management

Portfolio management via reinforcement learning is at the forefront of f...

0 Rundong Wang, et al. ∙

research

∙ 12/22/2020

Personalized Adaptive Meta Learning for Cold-start User Preference Prediction

A common challenge in personalized user preference prediction is the col...

0 Runsheng Yu, et al. ∙

research

∙ 12/09/2020

MetaInfoNet: Learning Task-Guided Information for Sample Reweighting

Deep neural networks have been shown to easily overfit to biased trainin...

0 Hongxin Wei, et al. ∙

research

∙ 12/07/2020

Efficient Reservoir Management through Deep Reinforcement Learning

Dams impact downstream river dynamics through flow regulation and disrup...

18 Xinrun Wang, et al. ∙

research

∙ 12/07/2020

SpotTune: Leveraging Transient Resources for Cost-efficient Hyper-parameter Tuning in the Public Cloud

Hyper-parameter tuning (HPT) is crucial for many machine learning (ML) a...

0 Yan Li, et al. ∙

research

∙ 12/02/2020

SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning

Deep learning with noisy labels is a challenging task. Recent prominent ...

0 Zhuowei Wang, et al. ∙

research

∙ 10/05/2020

Pointwise Binary Classification with Pairwise Confidence Comparisons

Ordinary (pointwise) binary classification aims to learn a binary classi...

3 Lei Feng, et al. ∙

research

∙ 09/30/2020

Complexity and Algorithms for Exploiting Quantal Opponents in Large Two-Player Games

Solution concepts of traditional game theory assume entirely rational pl...

0 David Milec, et al. ∙

research

∙ 09/26/2020

Computing Ex Ante Coordinated Team-Maxmin Equilibria in Zero-Sum Multiplayer Extensive-Form Games

Computational game theory has many applications in the modern world in b...

0 Youzhi Zhang, et al. ∙

research

∙ 08/21/2020

Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication

With the rise of online e-commerce platforms, more and more customers pr...

11 Xu He, et al. ∙

research

∙ 08/21/2020

Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation

Online recommendation services recommend multiple commodities to users. ...

0 Xu He, et al. ∙

research

∙ 07/17/2020

Provably Consistent Partial-Label Learning

Partial-label learning (PLL) is a multi-class classification problem, wh...

5 Lei Feng, et al. ∙

research

∙ 06/07/2020

Learning Behaviors with Uncertain Human Feedback

Human feedback is widely used to train agents in many domains. However, ...

0 Xu He, et al. ∙

research

∙ 03/05/2020

Combating noisy labels by agreement: A joint training method with co-regularization

Deep Learning with noisy labels is a practically challenging problem in ...

0 Hongxin Wei, et al. ∙

research

∙ 12/30/2019

Learning from Multiple Complementary Labels

Complementary-label learning is a new weakly-supervised learning framewo...

0 Lei Feng, et al. ∙

research

∙ 11/18/2019

Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning

Existing value-factorized based Multi-Agent deep Reinforce-ment Learning...

0 Runsheng Yu, et al. ∙

research

∙ 11/16/2019

Learning Efficient Multi-agent Communication: An Information Bottleneck Approach

Many real-world multi-agent reinforcement learning applications require ...

0 Rundong Wang, et al. ∙

research

∙ 06/15/2019

Social Cost Guarantees in Smart Route Guidance

We model and study the problem of assigning traffic in an urban road net...

0 Paolo Serafino, et al. ∙

research

∙ 05/28/2019

Manipulating a Learning Defender and Ways to Counteract

In Stackelberg security games, information about the attacker's type (i....

0 Jiarui Gan, et al. ∙

research

∙ 03/03/2019

Competitive Bridge Bidding with Deep Neural Networks

The game of bridge consists of two stages: bidding and playing. While pl...

0 Jiang Rong, et al. ∙

Bo An

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