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07/16/2023
MESOB: Balancing Equilibria Social Optimality
Motivated by bid recommendation in online ad auctions, this paper consid...
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12/29/2022
Offline Policy Optimization in RL with Variance Regularizaton
Learning policies from fixed offline datasets is a key challenge to scal...
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10/14/2022
A Reinforcement Learning Approach to Estimating Long-term Treatment Effects
Randomized experiments (a.k.a. A/B tests) are a powerful tool for estima...
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10/07/2021
Understanding Domain Randomization for Sim-to-real Transfer
Reinforcement learning encounters many challenges when applied directly ...
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07/01/2021
A Map of Bandits for E-commerce
The rich body of Bandit literature not only offers a diverse toolbox of ...
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04/06/2021
On the Optimality of Batch Policy Optimization Algorithms
Batch policy optimization considers leveraging existing data for policy ...
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02/08/2021
Near-optimal Representation Learning for Linear Bandits and Linear RL
This paper studies representation learning for multi-task linear bandits...
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10/22/2020
CoinDICE: Off-Policy Confidence Interval Estimation
We study high-confidence behavior-agnostic off-policy evaluation in rein...
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10/02/2020
Neural Thompson Sampling
Thompson Sampling (TS) is one of the most effective algorithms for solvi...
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08/31/2020
Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL
Reinforcement learning (RL) in episodic, factored Markov decision proces...
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07/27/2020
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
Off-policy evaluation (OPE) in reinforcement learning is an important pr...
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07/07/2020
Off-Policy Evaluation via the Regularized Lagrangian
The recently proposed distribution correction estimation (DICE) family o...
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03/24/2020
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning
Off-policy estimation for long-horizon problems is important in many rea...
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03/02/2020
Batch Stationary Distribution Estimation
We consider the problem of approximating the stationary distribution of ...
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02/21/2020
GenDICE: Generalized Offline Estimation of Stationary Values
An important problem that arises in reinforcement learning and Monte Car...
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02/12/2020
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing
Deep Reinforcement Learning (RL) is proven powerful for decision making ...
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12/04/2019
AlgaeDICE: Policy Gradient from Arbitrary Experience
In many real-world applications of reinforcement learning (RL), interact...
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11/11/2019
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration
We study the stochastic contextual bandit problem, where the reward is g...
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10/16/2019
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation
Infinite horizon off-policy policy evaluation is a highly challenging ta...
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06/21/2019
Randomized Exploration in Generalized Linear Bandits
We study two randomized algorithms for generalized linear bandits, GLM-T...
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06/10/2019
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
In many real-world reinforcement learning applications, access to the en...
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05/25/2019
A Kernel Loss for Solving the Bellman Equation
Value function learning plays a central role in many state-of-the-art re...
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04/26/2019
Neural Logic Machines
We propose the Neural Logic Machine (NLM), a neural-symbolic architectur...
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11/07/2018
Policy Certificates: Towards Accountable Reinforcement Learning
The performance of a reinforcement learning algorithm can vary drastical...
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10/29/2018
Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
We consider the off-policy estimation problem of estimating the expected...
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10/29/2018
Adversarial Attacks on Stochastic Bandits
We study adversarial attacks that manipulate the reward signals to contr...
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09/21/2018
Neural Approaches to Conversational AI
The present paper surveys neural approaches to conversational AI that ha...
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08/17/2018
Data Poisoning Attacks in Contextual Bandits
We study offline data poisoning attacks in contextual bandits, a class o...
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04/27/2018
Scalable Bilinear π Learning Using State and Action Features
Approximate linear programming (ALP) represents one of the major algorit...
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04/20/2018
Subgoal Discovery for Hierarchical Dialogue Policy Learning
Developing conversational agents to engage in complex dialogues is chall...
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12/29/2017
Smoothed Dual Embedding Control
We revisit the Bellman optimality equation with Nesterov's smoothing tec...
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12/29/2017
Boosting the Actor with Dual Critic
This paper proposes a new actor-critic-style algorithm called Dual Actor...
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11/15/2017
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems
We present a new algorithm that significantly improves the efficiency of...
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04/10/2017
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Building a dialogue agent to fulfill complex tasks, such as travel plann...
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03/21/2017
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems
Language understanding is a key component in a spoken dialogue system. I...
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03/03/2017
End-to-End Task-Completion Neural Dialogue Systems
One of the major drawbacks of modularized task-completion dialogue syste...
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02/28/2017
Provably Optimal Algorithms for Generalized Linear Contextual Bandits
Contextual bandits are widely used in Internet services from news recomm...
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02/28/2017
Scaffolding Networks: Incremental Learning and Teaching Through Questioning
We introduce a new paradigm of learning for reasoning, understanding, an...
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02/25/2017
Stochastic Variance Reduction Methods for Policy Evaluation
Policy evaluation is a crucial step in many reinforcement-learning proce...
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12/17/2016
A User Simulator for Task-Completion Dialogues
Despite widespread interests in reinforcement-learning for task-oriented...
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11/06/2016
Neuro-Symbolic Program Synthesis
Recent years have seen the proposal of a number of neural architectures ...
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11/03/2016
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear
To use deep reinforcement learning in the wild, we might hope for an age...
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09/03/2016
Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access
This paper proposes KB-InfoBot -- a multi-turn dialogue agent which help...
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06/12/2016
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...
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11/14/2015
Deep Reinforcement Learning with a Natural Language Action Space
This paper introduces a novel architecture for reinforcement learning wi...
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11/11/2015
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
We study the problem of off-policy value evaluation in reinforcement lea...
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09/10/2015
Recurrent Reinforcement Learning: A Hybrid Approach
Successful applications of reinforcement learning in real-world problems...
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06/10/2015
The Online Coupon-Collector Problem and Its Application to Lifelong Reinforcement Learning
Transferring knowledge across a sequence of related tasks is an importan...
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06/10/2015
On the Prior Sensitivity of Thompson Sampling
The empirically successful Thompson Sampling algorithm for stochastic ba...
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06/10/2015
