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06/21/2023
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
In this paper, we study representation learning in partially observable ...
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11/18/2022
Provable Defense against Backdoor Policies in Reinforcement Learning
We propose a provable defense mechanism against backdoor policies in rei...
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10/30/2022
Representation Learning for General-sum Low-rank Markov Games
We study multi-agent general-sum Markov games with nonlinear function ap...
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06/29/2022
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization
Online influence maximization aims to maximize the influence spread of a...
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06/22/2022
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks
Bilevel optimization have gained growing interests, with numerous applic...
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06/05/2022
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization
Directed Evolution (DE), a landmark wet-lab method originated in 1960s, ...
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06/01/2022
Byzantine-Robust Online and Offline Distributed Reinforcement Learning
We consider a distributed reinforcement learning setting where multiple ...
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05/29/2022
Provable Benefits of Representational Transfer in Reinforcement Learning
We study the problem of representational transfer in RL, where an agent ...
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02/10/2022
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory
Off-Policy Evaluation (OPE) serves as one of the cornerstones in Reinfor...
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01/31/2022
Optimal Estimation of Off-Policy Policy Gradient via Double Fitted Iteration
Policy gradient (PG) estimation becomes a challenge when we are not allo...
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01/31/2022
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach
We present BRIEE (Block-structured Representation learning with Interlea...
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10/09/2021
Representation Learning for Online and Offline RL in Low-rank MDPs
This work studies the question of Representation Learning in RL: how can...
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06/11/2021
Corruption-Robust Offline Reinforcement Learning
We study the adversarial robustness in offline reinforcement learning. G...
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02/23/2021
Controllable and Diverse Text Generation in E-commerce
In E-commerce, a key challenge in text generation is to find a good trad...
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02/16/2021
Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments
We study black-box reward poisoning attacks against reinforcement learni...
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02/11/2021
Robust Policy Gradient against Strong Data Corruption
We study the problem of robust reinforcement learning under adversarial ...
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09/05/2020
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners
Successful teaching requires an assumption of how the learner learns - h...
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06/16/2020
Task-agnostic Exploration in Reinforcement Learning
Efficient exploration is one of the main challenges in reinforcement lea...
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06/16/2020
The Teaching Dimension of Q-learning
In this paper, we initiate the study of sample complexity of teaching, t...
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04/29/2020
Neural Additive Models: Interpretable Machine Learning with Neural Nets
Deep neural networks (DNNs) are powerful black-box predictors that have ...
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03/27/2020
Adaptive Reward-Poisoning Attacks against Reinforcement Learning
In reward-poisoning attacks against reinforcement learning (RL), an atta...
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10/13/2019
Policy Poisoning in Batch Reinforcement Learning and Control
We study a security threat to batch reinforcement learning and control w...
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03/05/2019
Online Data Poisoning Attack
We study data poisoning attacks in the online learning setting where the...
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12/13/2018
Training Set Camouflage
We introduce a form of steganography in the domain of machine learning w...
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10/22/2018
Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models
Generalized additive models (GAMs) are favored in many regression and bi...
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10/15/2018
An Optimal Control Approach to Sequential Machine Teaching
Given a sequential learning algorithm and a target model, sequential mac...
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02/25/2018
Teacher Improves Learning by Selecting a Training Subset
We call a learner super-teachable if a teacher can trim down an iid trai...
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01/24/2018
