Shi Dong | DeepAI

DeepAI AI Chat

Featured Co-authors

Michael I. Jordan
283 publications
Tengyu Ma
97 publications
Yifan Sun
74 publications
Benjamin Van Roy
63 publications
Chenguang Zhu
57 publications
Jiantao Jiao
46 publications
Zhengyuan Zhou
39 publications
Jennifer Dy
33 publications
Zhifeng Wang
21 publications
Dilip Arumugam
20 publications
Ajay Joshi
20 publications

∙ 06/04/2023

Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reinforcement learning from human feedback (RLHF) has emerged as a relia...

0 Banghua Zhu, et al. ∙

∙ 05/19/2023

Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models

A centerpiece of the ever-popular reinforcement learning from human feed...

0 Wanqiao Xu, et al. ∙

∙ 12/24/2022

Inclusive Artificial Intelligence

Prevailing methods for assessing and comparing generative AIs incentiviz...

0 Dilip Arumugam, et al. ∙

∙ 11/29/2022

Posterior Sampling for Continuing Environments

We develop an extension of posterior sampling for reinforcement learning...

0 Wanqiao Xu, et al. ∙

∙ 07/07/2022

A unified interpretable intelligent learning diagnosis framework for smart education

Intelligent learning diagnosis is a critical engine of smart education, ...

1 Zhifeng Wang, et al. ∙

∙ 03/23/2022

Online Encrypted Skype Identification Based on an Updating Mechanism

The machine learning algorithm is gaining prominence in traffic identifi...

0 Shi Dong, et al. ∙

∙ 02/10/2021

Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent State

We design a simple reinforcement learning agent that, with a specificati...

0 Shi Dong, et al. ∙

∙ 09/23/2020

Using Undersampling with Ensemble Learning to Identify Factors Contributing to Preterm Birth

In this paper, we propose Ensemble Learning models to identify factors c...

0 Shi Dong, et al. ∙

∙ 12/13/2019

Provably Efficient Reinforcement Learning with Aggregated States

We establish that an optimistic variant of Q-learning applied to a finit...

0 Shi Dong, et al. ∙

∙ 11/26/2019

Summarizing CPU and GPU Design Trends with Product Data

Moore's Law and Dennard Scaling have guided the semiconductor industry f...

0 Yifan Sun, et al. ∙

∙ 11/18/2019

Comments on the Du-Kakade-Wang-Yang Lower Bounds

Du, Kakade, Wang, and Yang recently established intriguing lower bounds ...

0 Benjamin Van Roy, et al. ∙

∙ 05/12/2019

On the Performance of Thompson Sampling on Logistic Bandits

We study the logistic bandit, in which rewards are binary with success p...

10 Shi Dong, et al. ∙

∙ 10/15/2018

MGSim + MGMark: A Framework for Multi-GPU System Research

The rapidly growing popularity and scale of data-parallel workloads dema...

0 Yifan Sun, et al. ∙

∙ 05/30/2018

An Information-Theoretic Analysis for Thompson Sampling with Many Actions

Information-theoretic Bayesian regret bounds of Russo and Van Roy captur...

0 Shi Dong, et al. ∙

∙ 05/30/2018

An Information-Theoretic Analysis of Thompson Sampling for Large Action Spaces

Information-theoretic Bayesian regret bounds of Russo and Van Roy captur...

0 Shi Dong, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world