Yi Ouyang | DeepAI

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∙ 01/12/2023

An Approach to Stochastic Dynamic Games with Asymmetric Information and Hidden Actions

We consider in discrete time, a general class of sequential stochastic d...

0 Yi Ouyang, et al. ∙

∙ 01/07/2023

On vectorial functions with maximal number of bent components

We study vectorial functions with maximal number of bent components in t...

0 Xianhong Xie, et al. ∙

∙ 08/15/2022

Grasping Core Rules of Time Series through Pure Models

Time series underwent the transition from statistics to deep learning, a...

0 Gedi Liu, et al. ∙

∙ 08/19/2021

A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems

We revisit the Thompson sampling algorithm to control an unknown linear ...

0 Mukul Gagrani, et al. ∙

∙ 08/18/2021

Scalable regret for learning to control network-coupled subsystems with unknown dynamics

We consider the problem of controlling an unknown linear quadratic Gauss...

0 Sagar Sudhakara, et al. ∙

∙ 02/18/2021

Causal Inference Q-Network: Toward Resilient Reinforcement Learning

Deep reinforcement learning (DRL) has demonstrated impressive performanc...

0 Chao-Han Huck Yang, et al. ∙

∙ 11/24/2020

Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation

Automated Vehicles require exhaustive testing in simulation to detect as...

6 Daisuke Nishiyama, et al. ∙

∙ 11/09/2020

Thompson sampling for linear quadratic mean-field teams

We consider optimal control of an unknown multi-agent linear quadratic (...

0 Mukul Gagrani, et al. ∙

∙ 02/20/2020

Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning

Recent deep neural networks based techniques, especially those equipped ...

15 Chao-Han Huck Yang, et al. ∙

∙ 01/27/2020

Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems

Regret analysis is challenging in Multi-Agent Reinforcement Learning (MA...

53 Seyed Mohammad Asghari, et al. ∙

∙ 12/09/2019

Learning Latent State Spaces for Planning through Reward Prediction

Model-based reinforcement learning methods typically learn models for hi...

5 Aaron Havens, et al. ∙

∙ 10/17/2019

Online Learning in Planar Pushing with Combined Prediction Model

Pushing is a useful robotic capability for positioning and reorienting o...

0 Huidong Gao, et al. ∙

∙ 05/24/2019

Learning Cross-Domain Representation with Multi-Graph Neural Network

Learning effective embedding has been proved to be useful in many real-w...

0 Yi Ouyang, et al. ∙

∙ 05/13/2019

Construction of three classes of Strictly Optimal Frequency-Hopping Sequence Sets

In this paper, we construct three classes of strictly optimal frequency-...

0 Yi Ouyang, et al. ∙

∙ 12/03/2018

A Unified Approach to Dynamic Decision Problems with Asymmetric Information - Part II: Strategic Agents

We study a general class of dynamic games with asymmetric information wh...

0 Hamidreza Tavafoghi, et al. ∙

∙ 12/03/2018

A Unified Approach to Dynamic Decision Problems with Asymmetric Information - Part I: Non-Strategic Agents

We study a general class of dynamic multi-agent decision problems with a...

0 Hamidreza Tavafoghi, et al. ∙

∙ 08/24/2018

Linear complexity of generalized cyclotomic sequences of period 2p^m

In this paper, we construct two generalized cyclotomic binary sequences ...

0 Yi Ouyang, et al. ∙

∙ 02/15/2018

CompetitiveBike: Competitive Prediction of Bike-Sharing Apps Using Heterogeneous Crowdsourced Data

In recent years, bike-sharing systems have been deployed in many cities,...

0 Yi Ouyang, et al. ∙

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