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06/04/2023
Fine-Tuning Language Models with Advantage-Induced Policy Alignment
Reinforcement learning from human feedback (RLHF) has emerged as a relia...
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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...
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12/24/2022
Inclusive Artificial Intelligence
Prevailing methods for assessing and comparing generative AIs incentiviz...
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11/29/2022
Posterior Sampling for Continuing Environments
We develop an extension of posterior sampling for reinforcement learning...
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07/07/2022
A unified interpretable intelligent learning diagnosis framework for smart education
Intelligent learning diagnosis is a critical engine of smart education, ...
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03/23/2022
Online Encrypted Skype Identification Based on an Updating Mechanism
The machine learning algorithm is gaining prominence in traffic identifi...
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02/10/2021
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent State
We design a simple reinforcement learning agent that, with a specificati...
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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...
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12/13/2019
Provably Efficient Reinforcement Learning with Aggregated States
We establish that an optimistic variant of Q-learning applied to a finit...
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11/26/2019
Summarizing CPU and GPU Design Trends with Product Data
Moore's Law and Dennard Scaling have guided the semiconductor industry f...
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11/18/2019
Comments on the Du-Kakade-Wang-Yang Lower Bounds
Du, Kakade, Wang, and Yang recently established intriguing lower bounds ...
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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...
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10/15/2018
MGSim + MGMark: A Framework for Multi-GPU System Research
The rapidly growing popularity and scale of data-parallel workloads dema...
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05/30/2018
An Information-Theoretic Analysis for Thompson Sampling with Many Actions
Information-theoretic Bayesian regret bounds of Russo and Van Roy captur...
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05/30/2018