Ray Jiang | DeepAI

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Featured Co-authors

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∙ 08/07/2023

AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning

StarCraft II is one of the most challenging simulated reinforcement lear...

0 Michael Mathieu, et al. ∙

∙ 02/09/2023

Scaling Goal-based Exploration via Pruning Proto-goals

One of the gnarliest challenges in reinforcement learning (RL) is explor...

0 Akhil Bagaria, et al. ∙

∙ 09/15/2022

Human-level Atari 200x faster

The task of building general agents that perform well over a wide range ...

0 Steven Kapturowski, et al. ∙

∙ 07/12/2021

Learning Expected Emphatic Traces for Deep RL

Off-policy sampling and experience replay are key for improving sample e...

0 Ray Jiang, et al. ∙

∙ 06/21/2021

Emphatic Algorithms for Deep Reinforcement Learning

Off-policy learning allows us to learn about possible policies of behavi...

0 Ray Jiang, et al. ∙

∙ 02/07/2020

Causally Correct Partial Models for Reinforcement Learning

In reinforcement learning, we can learn a model of future observations a...

17 Danilo J. Rezende, et al. ∙

∙ 11/08/2019

Reducing Sentiment Bias in Language Models via Counterfactual Evaluation

Recent improvements in large-scale language models have driven progress ...

0 Po-Sen Huang, et al. ∙

∙ 07/28/2019

Wasserstein Fair Classification

We propose an approach to fair classification that enforces independence...

7 Ray Jiang, et al. ∙

∙ 02/27/2019

Degenerate Feedback Loops in Recommender Systems

Machine learning is used extensively in recommender systems deployed in ...

14 Ray Jiang, et al. ∙

∙ 07/24/2018

Learning from Delayed Outcomes with Intermediate Observations

Optimizing for long term value is desirable in many practical applicatio...

4 Timothy A. Mann, et al. ∙

∙ 03/05/2018

Optimizing Slate Recommendations via Slate-CVAE

The slate recommendation problem aims to find the "optimal" ordering of ...

0 Ray Jiang, et al. ∙

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