research
∙
09/14/2023
Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context
Financial simulators play an important role in enhancing forecasting acc...
share
research
∙
08/17/2023
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making
Market making (MM) has attracted significant attention in financial trad...
research
∙
06/19/2023
Controlling Type Confounding in Ad Hoc Teamwork with Instance-wise Teammate Feedback Rectification
Ad hoc teamwork requires an agent to cooperate with unknown teammates wi...
research
∙
06/18/2023
Weakly Supervised Regression with Interval Targets
This paper investigates an interesting weakly supervised regression sett...
research
∙
06/15/2023
Partial-Label Regression
Partial-label learning is a popular weakly supervised learning setting t...
research
∙
06/13/2023
Synapse: Leveraging Few-Shot Exemplars for Human-Level Computer Control
This paper investigates the design of few-shot exemplars for computer au...
research
∙
06/06/2023
State Regularized Policy Optimization on Data with Dynamics Shift
In many real-world scenarios, Reinforcement Learning (RL) algorithms are...
research
∙
03/27/2023
On the Importance of Feature Separability in Predicting Out-Of-Distribution Error
Estimating the generalization performance is practically challenging on ...
research
∙
02/07/2023
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning
Recent advances in multi-agent reinforcement learning (MARL) allow agent...
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...
research
∙
01/27/2023
Reinforcement Learning from Diverse Human Preferences
The complexity of designing reward functions has been a major obstacle t...
research
∙
12/08/2022
Logit Clipping for Robust Learning against Label Noise
In the presence of noisy labels, designing robust loss functions is crit...
research
∙
12/06/2022
PrefRec: Preference-based Recommender Systems for Reinforcing Long-term User Engagement
Current advances in recommender systems have been remarkably successful ...
research
∙
10/18/2022
RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning
Despite the recent advancement in multi-agent reinforcement learning (MA...
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...
research
∙
07/12/2022
Offline Equilibrium Finding
Offline reinforcement learning (Offline RL) is an emerging field that ha...
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...
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...
research
∙
05/27/2022
Off-Beat Multi-Agent Reinforcement Learning
We investigate model-free multi-agent reinforcement learning (MARL) in e...
research
∙
05/19/2022
Mitigating Neural Network Overconfidence with Logit Normalization
Detecting out-of-distribution inputs is critical for safe deployment of ...
research
∙
04/11/2022
A Unified Perspective on Deep Equilibrium Finding
Extensive-form games provide a versatile framework for modeling interact...
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...
research
∙
01/16/2022
GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation
This paper studies weakly supervised domain adaptation(WSDA) problem, wh...
research
∙
12/08/2021
Pretrained Cost Model for Distributed Constraint Optimization Problems
Distributed Constraint Optimization Problems (DCOPs) are an important su...
research
∙
12/04/2021
Cooperation, Retaliation and Forgiveness in Revision Games
Revision game is a very new model formulating the situation where player...
research
∙
09/28/2021
Reinforcement Learning for Quantitative Trading
Quantitative trading (QT), which refers to the usage of mathematical mod...
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...
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...
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? ...
research
∙
06/13/2021
Contingency-Aware Influence Maximization: A Reinforcement Learning Approach
The influence maximization (IM) problem aims at finding a subset of seed...
research
∙
06/11/2021
On the Robustness of Average Losses for Partial-Label Learning
Partial-label (PL) learning is a typical weakly supervised classificatio...
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...
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...
research
∙
02/18/2021
L2E: Learning to Exploit Your Opponent
Opponent modeling is essential to exploit sub-optimal opponents in strat...
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...
research
∙
02/16/2021
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
Current value-based multi-agent reinforcement learning methods optimize ...
research
∙
02/13/2021
Learning from Similarity-Confidence Data
Weakly supervised learning has drawn considerable attention recently to ...
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...
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...
research
∙
12/09/2020
MetaInfoNet: Learning Task-Guided Information for Sample Reweighting
Deep neural networks have been shown to easily overfit to biased trainin...
research
∙
12/07/2020
Efficient Reservoir Management through Deep Reinforcement Learning
Dams impact downstream river dynamics through flow regulation and disrup...
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...
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 ...
research
∙
10/05/2020
Pointwise Binary Classification with Pairwise Confidence Comparisons
Ordinary (pointwise) binary classification aims to learn a binary classi...
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...
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...
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...
research
∙
08/21/2020
Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation
Online recommendation services recommend multiple commodities to users. ...
research
∙
07/17/2020
Provably Consistent Partial-Label Learning
Partial-label learning (PLL) is a multi-class classification problem, wh...
research
∙
06/07/2020