Lihong Li

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Google ∙ Microsoft

Research Scientist at Google Inc.

Near-optimal Representation Learning for Linear Bandits and Linear RL

This paper studies representation learning for multi-task linear bandits...
02/08/2021 ∙ by Jiachen Hu, et al. ∙ 0 ∙ share

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CoinDICE: Off-Policy Confidence Interval Estimation

We study high-confidence behavior-agnostic off-policy evaluation in rein...
10/22/2020 ∙ by Bo Dai, et al. ∙ 0 ∙ share

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Neural Thompson Sampling

Thompson Sampling (TS) is one of the most effective algorithms for solvi...
10/02/2020 ∙ by Weitong Zhang, et al. ∙ 7 ∙ share

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Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL

Reinforcement learning (RL) in episodic, factored Markov decision proces...
08/31/2020 ∙ by Xiaoyu Chen, et al. ∙ 9 ∙ share

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Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders

Off-policy evaluation (OPE) in reinforcement learning is an important pr...
07/27/2020 ∙ by Andrew Bennett, et al. ∙ 5 ∙ share

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Off-Policy Evaluation via the Regularized Lagrangian

The recently proposed distribution correction estimation (DICE) family o...
07/07/2020 ∙ by Mengjiao Yang, et al. ∙ 4 ∙ share

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Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning

Off-policy estimation for long-horizon problems is important in many rea...
03/24/2020 ∙ by Ali Mousavi, et al. ∙ 7 ∙ share

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Batch Stationary Distribution Estimation

We consider the problem of approximating the stationary distribution of ...
03/02/2020 ∙ by Junfeng Wen, et al. ∙ 7 ∙ share

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GenDICE: Generalized Offline Estimation of Stationary Values

An important problem that arises in reinforcement learning and Monte Car...
02/21/2020 ∙ by Ruiyi Zhang, et al. ∙ 11 ∙ share

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Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing

Deep Reinforcement Learning (RL) is proven powerful for decision making ...
02/12/2020 ∙ by Ge Liu, et al. ∙ 11 ∙ share

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AlgaeDICE: Policy Gradient from Arbitrary Experience

In many real-world applications of reinforcement learning (RL), interact...
12/04/2019 ∙ by Ofir Nachum, et al. ∙ 0 ∙ share

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Neural Contextual Bandits with Upper Confidence Bound-Based Exploration

We study the stochastic contextual bandit problem, where the reward is g...
11/11/2019 ∙ by Dongruo Zhou, et al. ∙ 16 ∙ share

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Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation

Infinite horizon off-policy policy evaluation is a highly challenging ta...
10/16/2019 ∙ by Ziyang Tang, et al. ∙ 0 ∙ share

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Randomized Exploration in Generalized Linear Bandits

We study two randomized algorithms for generalized linear bandits, GLM-T...
06/21/2019 ∙ by Branislav Kveton, et al. ∙ 2 ∙ share

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DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections

In many real-world reinforcement learning applications, access to the en...
06/10/2019 ∙ by Ofir Nachum, et al. ∙ 0 ∙ share

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A Kernel Loss for Solving the Bellman Equation

Value function learning plays a central role in many state-of-the-art re...
05/25/2019 ∙ by Yihao Feng, et al. ∙ 0 ∙ share

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Neural Logic Machines

We propose the Neural Logic Machine (NLM), a neural-symbolic architectur...
04/26/2019 ∙ by Honghua Dong, et al. ∙ 0 ∙ share

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Policy Certificates: Towards Accountable Reinforcement Learning

The performance of a reinforcement learning algorithm can vary drastical...
11/07/2018 ∙ by Christoph Dann, et al. ∙ 0 ∙ share

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Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation

We consider the off-policy estimation problem of estimating the expected...
10/29/2018 ∙ by Qiang Liu, et al. ∙ 0 ∙ share

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Adversarial Attacks on Stochastic Bandits

We study adversarial attacks that manipulate the reward signals to contr...
10/29/2018 ∙ by Kwang-Sung Jun, et al. ∙ 0 ∙ share

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Neural Approaches to Conversational AI

The present paper surveys neural approaches to conversational AI that ha...
09/21/2018 ∙ by Jianfeng Gao, et al. ∙ 0 ∙ share

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Data Poisoning Attacks in Contextual Bandits

We study offline data poisoning attacks in contextual bandits, a class o...
08/17/2018 ∙ by Yuzhe Ma, et al. ∙ 0 ∙ share

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Scalable Bilinear π Learning Using State and Action Features

Approximate linear programming (ALP) represents one of the major algorit...
04/27/2018 ∙ by Yichen Chen, et al. ∙ 0 ∙ share

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Subgoal Discovery for Hierarchical Dialogue Policy Learning

Developing conversational agents to engage in complex dialogues is chall...
04/20/2018 ∙ by Da Tang, et al. ∙ 0 ∙ share

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Smoothed Dual Embedding Control

We revisit the Bellman optimality equation with Nesterov's smoothing tec...
12/29/2017 ∙ by Bo Dai, et al. ∙ 0 ∙ share

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Boosting the Actor with Dual Critic

This paper proposes a new actor-critic-style algorithm called Dual Actor...
12/29/2017 ∙ by Bo Dai, et al. ∙ 0 ∙ share

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BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems

We present a new algorithm that significantly improves the efficiency of...
11/15/2017 ∙ by Zachary Lipton, et al. ∙ 0 ∙ share

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Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning

Building a dialogue agent to fulfill complex tasks, such as travel plann...
04/10/2017 ∙ by Baolin Peng, et al. ∙ 0 ∙ share

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Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems

Language understanding is a key component in a spoken dialogue system. I...
03/21/2017 ∙ by Xiujun Li, et al. ∙ 0 ∙ share

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End-to-End Task-Completion Neural Dialogue Systems

One of the major drawbacks of modularized task-completion dialogue syste...
03/03/2017 ∙ by Xiujun Li, et al. ∙ 0 ∙ share

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Provably Optimal Algorithms for Generalized Linear Contextual Bandits

Contextual bandits are widely used in Internet services from news recomm...
02/28/2017 ∙ by Lihong Li, et al. ∙ 0 ∙ share

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Scaffolding Networks: Incremental Learning and Teaching Through Questioning

We introduce a new paradigm of learning for reasoning, understanding, an...
02/28/2017 ∙ by Asli Celikyilmaz, et al. ∙ 0 ∙ share

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Stochastic Variance Reduction Methods for Policy Evaluation

Policy evaluation is a crucial step in many reinforcement-learning proce...
02/25/2017 ∙ by Simon S. Du, et al. ∙ 0 ∙ share

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A User Simulator for Task-Completion Dialogues

Despite widespread interests in reinforcement-learning for task-oriented...
12/17/2016 ∙ by Xiujun Li, et al. ∙ 0 ∙ share

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Neuro-Symbolic Program Synthesis

Recent years have seen the proposal of a number of neural architectures ...
11/06/2016 ∙ by Emilio Parisotto, et al. ∙ 0 ∙ share

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Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear

To use deep reinforcement learning in the wild, we might hope for an age...
11/03/2016 ∙ by Zachary C Lipton, et al. ∙ 0 ∙ share

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Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access

This paper proposes KB-InfoBot -- a multi-turn dialogue agent which help...
09/03/2016 ∙ by Bhuwan Dhingra, et al. ∙ 0 ∙ share

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Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads

We introduce an online popularity prediction and tracking task as a benc...
06/12/2016 ∙ by Ji He, et al. ∙ 0 ∙ share

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Deep Reinforcement Learning with a Natural Language Action Space

This paper introduces a novel architecture for reinforcement learning wi...
11/14/2015 ∙ by Ji He, et al. ∙ 0 ∙ share

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Doubly Robust Off-policy Value Evaluation for Reinforcement Learning

We study the problem of off-policy value evaluation in reinforcement lea...
11/11/2015 ∙ by Nan Jiang, et al. ∙ 0 ∙ share

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Recurrent Reinforcement Learning: A Hybrid Approach

Successful applications of reinforcement learning in real-world problems...
09/10/2015 ∙ by Xiujun Li, et al. ∙ 0 ∙ share

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The Online Coupon-Collector Problem and Its Application to Lifelong Reinforcement Learning

Transferring knowledge across a sequence of related tasks is an importan...
06/10/2015 ∙ by Emma Brunskill, et al. ∙ 0 ∙ share

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On the Prior Sensitivity of Thompson Sampling

The empirically successful Thompson Sampling algorithm for stochastic ba...
06/10/2015 ∙ by Che-Yu Liu, et al. ∙ 0 ∙ share

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An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives

We consider a contextual version of multi-armed bandit problem with glob...
06/10/2015 ∙ by Shipra Agrawal, et al. ∙ 0 ∙ share

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Doubly Robust Policy Evaluation and Optimization

We study sequential decision making in environments where rewards are on...
03/10/2015 ∙ by Miroslav Dudík, et al. ∙ 0 ∙ share

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On Minimax Optimal Offline Policy Evaluation

This paper studies the off-policy evaluation problem, where one aims to ...
09/12/2014 ∙ by Lihong Li, et al. ∙ 0 ∙ share

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Counterfactual Estimation and Optimization of Click Metrics for Search Engines

Optimizing an interactive system against a predefined online metric is p...
03/07/2014 ∙ by Lihong Li, et al. ∙ 0 ∙ share

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Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

We present a new algorithm for the contextual bandit learning problem, w...
02/04/2014 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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Sample Complexity of Multi-task Reinforcement Learning

Transferring knowledge across a sequence of reinforcement-learning tasks...
09/26/2013 ∙ by Emma Brunskill, et al. ∙ 0 ∙ share

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Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits

We present and prove properties of a new offline policy evaluator for an...
10/16/2012 ∙ by Miroslav Dudík, et al. ∙ 0 ∙ share

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Lihong Li

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