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06/13/2023
Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits
We consider policy optimization in contextual bandits, where one is give...
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06/01/2023
Exposing Attention Glitches with Flip-Flop Language Modeling
Why do large language models sometimes output factual inaccuracies and e...
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03/05/2023
Streaming Active Learning with Deep Neural Networks
Active learning is perhaps most naturally posed as an online learning pr...
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02/28/2023
Learning Hidden Markov Models Using Conditional Samples
This paper is concerned with the computational complexity of learning th...
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02/27/2023
Statistical Learning under Heterogenous Distribution Shift
This paper studies the prediction of a target 𝐳 from a pair of random va...
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10/19/2022
Transformers Learn Shortcuts to Automata
Algorithmic reasoning requires capabilities which are most naturally und...
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10/13/2022
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient
We consider a hybrid reinforcement learning setting (Hybrid RL), in whic...
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07/17/2022
Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models
A person walking along a city street who tries to model all aspects of t...
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06/21/2022
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL
We study reward-free reinforcement learning (RL) under general non-linea...
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06/09/2022
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information
In real-world reinforcement learning applications the learner's observat...
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03/08/2022
A Sharp Characterization of Linear Estimators for Offline Policy Evaluation
Offline policy evaluation is a fundamental statistical problem in reinfo...
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02/28/2022
Understanding Contrastive Learning Requires Incorporating Inductive Biases
Contrastive learning is a popular form of self-supervised learning that ...
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02/08/2022
Provable Reinforcement Learning with a Short-Term Memory
Real-world sequential decision making problems commonly involve partial ...
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11/21/2021
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
We consider the offline reinforcement learning problem, where the aim is...
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11/08/2021
Universal and data-adaptive algorithms for model selection in linear contextual bandits
Model selection in contextual bandits is an important complementary prob...
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10/21/2021
Anti-Concentrated Confidence Bonuses for Scalable Exploration
Intrinsic rewards play a central role in handling the exploration-exploi...
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10/17/2021
Provable RL with Exogenous Distractors via Multistep Inverse Dynamics
Many real-world applications of reinforcement learning (RL) require the ...
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10/12/2021
Sparsity in Partially Controllable Linear Systems
A fundamental concept in control theory is that of controllability, wher...
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07/05/2021
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
A recurring theme in statistical learning, online learning, and beyond i...
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07/03/2021
Bayesian decision-making under misspecified priors with applications to meta-learning
Thompson sampling and other Bayesian sequential decision-making algorith...
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06/18/2021
Investigating the Role of Negatives in Contrastive Representation Learning
Noise contrastive learning is a popular technique for unsupervised repre...
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06/17/2021
Gone Fishing: Neural Active Learning with Fisher Embeddings
There is an increasing need for effective active learning algorithms tha...
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02/14/2021
Model-free Representation Learning and Exploration in Low-rank MDPs
The low rank MDP has emerged as an important model for studying represen...
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10/08/2020
Learning the Linear Quadratic Regulator from Nonlinear Observations
We introduce a new problem setting for continuous control called the LQR...
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09/18/2020
Private Reinforcement Learning with PAC and Regret Guarantees
Motivated by high-stakes decision-making domains like personalized medic...
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08/24/2020
Contrastive learning, multi-view redundancy, and linear models
Self-supervised learning is an empirically successful approach to unsupe...
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06/22/2020
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Partial observability is a common challenge in many reinforcement learni...
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06/22/2020
Information Theoretic Regret Bounds for Online Nonlinear Control
This work studies the problem of sequential control in an unknown, nonli...
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06/19/2020
Open Problem: Model Selection for Contextual Bandits
In statistical learning, algorithms for model selection allow the learne...
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06/18/2020
Provably adaptive reinforcement learning in metric spaces
We study reinforcement learning in continuous state and action spaces en...
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06/18/2020
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
In order to deal with the curse of dimensionality in reinforcement learn...
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06/10/2020
Efficient Contextual Bandits with Continuous Actions
We create a computationally tractable algorithm for contextual bandits w...
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03/04/2020
Contrastive estimation reveals topic posterior information to linear models
Contrastive learning is an approach to representation learning that util...
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02/26/2020
Corrupted Multidimensional Binary Search: Learning in the Presence of Irrational Agents
Standard game-theoretic formulations for settings like contextual pricin...
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02/18/2020
Adaptive Estimator Selection for Off-Policy Evaluation
We develop a generic data-driven method for estimator selection in off-p...
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02/07/2020
Reward-Free Exploration for Reinforcement Learning
Exploration is widely regarded as one of the most challenging aspects of...
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01/19/2020
Algebraic and Analytic Approaches for Parameter Learning in Mixture Models
We present two different approaches for parameter learning in several mi...
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12/31/2019
Scalable Hierarchical Clustering with Tree Grafting
We introduce Grinch, a new algorithm for large-scale, non-greedy hierarc...
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12/09/2019
Optimism in Reinforcement Learning with Generalized Linear Function Approximation
We design a new provably efficient algorithm for episodic reinforcement ...
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11/13/2019
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
We present an algorithm, HOMER, for exploration and reinforcement learni...
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10/30/2019
Sample Complexity of Learning Mixtures of Sparse Linear Regressions
In the problem of learning mixtures of linear regressions, the goal is t...
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10/09/2019
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers
We consider the dynamic assortment optimization problem under the multin...
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07/22/2019
Doubly robust off-policy evaluation with shrinkage
We design a new family of estimators for off-policy evaluation in contex...
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06/09/2019
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
We design a new algorithm for batch active learning with deep neural net...
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06/03/2019
Model selection for contextual bandits
We introduce the problem of model selection for contextual bandits, wher...
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04/21/2019
Trace Reconstruction: Generalized and Parameterized
In the beautifully simple-to-state problem of trace reconstruction, the ...
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02/05/2019
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
We study contextual bandit learning with an abstract policy class and co...
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01/25/2019
Provably efficient RL with Rich Observations via Latent State Decoding
We study the exploration problem in episodic MDPs with rich observations...
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11/21/2018
Model-Based Reinforcement Learning in Contextual Decision Processes
We study the sample complexity of model-based reinforcement learning in ...
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06/28/2018
