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07/27/2023
Autocalibrating Gaze Tracking: A Demonstration through Gaze Typing
Miscalibration of gaze tracking devices and the resulting need for repea...
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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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11/14/2022
Towards Data-Driven Offline Simulations for Online Reinforcement Learning
Modern decision-making systems, from robots to web recommendation engine...
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10/31/2022
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
Learning to control an agent from data collected offline in a rich pixel...
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10/25/2022
Eigen Memory Tree
This work introduces the Eigen Memory Tree (EMT), a novel online memory ...
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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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07/12/2022
Contextual Bandits with Large Action Spaces: Made Practical
A central problem in sequential decision making is to develop algorithms...
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06/16/2022
Interaction-Grounded Learning with Action-inclusive Feedback
Consider the problem setting of Interaction-Grounded Learning (IGL), in ...
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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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02/10/2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
Large-scale machine learning systems often involve data distributed acro...
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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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06/09/2021
Interaction-Grounded Learning
Consider a prosthetic arm, learning to adapt to its user's control signa...
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06/09/2021
ChaCha for Online AutoML
We propose the ChaCha (Champion-Challengers) algorithm for making an onl...
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11/23/2020
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication
Large software systems tune hundreds of 'constants' to optimize their ru...
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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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06/10/2020
Efficient Contextual Bandits with Continuous Actions
We create a computationally tractable algorithm for contextual bandits w...
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04/07/2020
PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing
The global health threat from COVID-19 has been controlled in a number o...
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03/28/2020
Federated Residual Learning
We study a new form of federated learning where the clients train person...
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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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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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06/07/2019
Empirical Likelihood for Contextual Bandits
We apply empirical likelihood techniques to contextual bandit policy val...
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05/31/2019
Efficient Forward Architecture Search
We propose a neural architecture search (NAS) algorithm, Petridish, to i...
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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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01/02/2019
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
We investigate the feasibility of learning from both fully-labeled super...
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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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07/17/2018
Contextual Memory Trees
We design and study a Contextual Memory Tree (CMT), a learning memory co...
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03/06/2018
A Reductions Approach to Fair Classification
We present a systematic approach for achieving fairness in a binary clas...
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03/01/2018
On Polynomial Time PAC Reinforcement Learning with Rich Observations
We study the computational tractability of provably sample-efficient (PA...
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02/12/2018
Practical Evaluation and Optimization of Contextual Bandit Algorithms
We study and empirically optimize contextual bandit learning, exploratio...
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08/05/2017
Efficient Contextual Bandits in Non-stationary Worlds
Most contextual bandit algorithms minimize regret to the best fixed poli...
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04/28/2017
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
We propose to directly map raw visual observations and text input to act...
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03/03/2017
Active Learning for Cost-Sensitive Classification
We design an active learning algorithm for cost-sensitive multiclass cla...
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10/29/2016
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable
This paper studies systematic exploration for reinforcement learning wit...
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06/15/2016
Logarithmic Time One-Against-Some
We create a new online reduction of multiclass classification to binary ...
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05/16/2016
Off-policy evaluation for slate recommendation
This paper studies the evaluation of policies that recommend an ordered ...
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02/23/2016
Search Improves Label for Active Learning
We investigate active learning with access to two distinct oracles: Labe...
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02/08/2016
PAC Reinforcement Learning with Rich Observations
We propose and study a new model for reinforcement learning with rich ob...
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06/29/2015
Efficient and Parsimonious Agnostic Active Learning
We develop a new active learning algorithm for the streaming setting sat...
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03/18/2015
Learning to Search for Dependencies
We demonstrate that a dependency parser can be built using a credit assi...
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03/10/2015
Doubly Robust Policy Evaluation and Optimization
We study sequential decision making in environments where rewards are on...
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02/08/2015
Learning to Search Better Than Your Teacher
Methods for learning to search for structured prediction typically imita...
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10/02/2014
Scalable Nonlinear Learning with Adaptive Polynomial Expansions
Can we effectively learn a nonlinear representation in time comparable t...
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08/09/2014
Normalized Online Learning
We introduce online learning algorithms which are independent of feature...
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08/09/2014
Conditional Probability Tree Estimation Analysis and Algorithms
We consider the problem of estimating the conditional probability of a l...
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02/04/2014
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
We present a new algorithm for the contextual bandit learning problem, w...
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10/16/2012
Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits
We present and prove properties of a new offline policy evaluator for an...
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07/19/2012
Proceedings of the 29th International Conference on Machine Learning (ICML-12)
This is an index to the papers that appear in the Proceedings of the 29t...
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06/27/2012
Predicting Conditional Quantiles via Reduction to Classification
We show how to reduce the process of predicting general order statistics...
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01/31/2012