b'John Langford'

research

∙ 07/27/2023

Autocalibrating Gaze Tracking: A Demonstration through Gaze Typing

Miscalibration of gaze tracking devices and the resulting need for repea...

0 Akanksha Saran, et al. ∙

research

∙ 03/05/2023

Streaming Active Learning with Deep Neural Networks

Active learning is perhaps most naturally posed as an online learning pr...

0 Akanksha Saran, et al. ∙

research

∙ 11/14/2022

Towards Data-Driven Offline Simulations for Online Reinforcement Learning

Modern decision-making systems, from robots to web recommendation engine...

0 Shengpu Tang, et al. ∙

research

∙ 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...

0 Riashat Islam, et al. ∙

research

∙ 10/25/2022

Eigen Memory Tree

This work introduces the Eigen Memory Tree (EMT), a novel online memory ...

0 Mark Rucker, et al. ∙

research

∙ 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...

17 Alex Lamb, et al. ∙

research

∙ 07/12/2022

Contextual Bandits with Large Action Spaces: Made Practical

A central problem in sequential decision making is to develop algorithms...

0 Yinglun Zhu, et al. ∙

research

∙ 06/16/2022

Interaction-Grounded Learning with Action-inclusive Feedback

Consider the problem setting of Interaction-Grounded Learning (IGL), in ...

2 Tengyang Xie, et al. ∙

research

∙ 06/09/2022

Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information

In real-world reinforcement learning applications the learner's observat...

22 Yonathan Efroni, et al. ∙

research

∙ 02/10/2022

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization

Large-scale machine learning systems often involve data distributed acro...

11 Alberto Bietti, et al. ∙

research

∙ 10/17/2021

Provable RL with Exogenous Distractors via Multistep Inverse Dynamics

Many real-world applications of reinforcement learning (RL) require the ...

4 Yonathan Efroni, et al. ∙

research

∙ 06/09/2021

Interaction-Grounded Learning

Consider a prosthetic arm, learning to adapt to its user's control signa...

0 Tengyang Xie, et al. ∙

research

∙ 06/09/2021

ChaCha for Online AutoML

We propose the ChaCha (Champion-Challengers) algorithm for making an onl...

0 Qingyun Wu, et al. ∙

research

∙ 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...

12 Jayant Gupchup, et al. ∙

research

∙ 10/08/2020

Learning the Linear Quadratic Regulator from Nonlinear Observations

We introduce a new problem setting for continuous control called the LQR...

4 Zakaria Mhammedi, et al. ∙

research

∙ 06/10/2020

Efficient Contextual Bandits with Continuous Actions

We create a computationally tractable algorithm for contextual bandits w...

0 Maryam Majzoubi, et al. ∙

research

∙ 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...

0 Justin Chan, et al. ∙

research

∙ 03/28/2020

Federated Residual Learning

We study a new form of federated learning where the clients train person...

5 Alekh Agarwal, et al. ∙

research

∙ 11/13/2019

Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning

We present an algorithm, HOMER, for exploration and reinforcement learni...

15 Dipendra Misra, et al. ∙

research

∙ 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...

3 Jordan T. Ash, et al. ∙

research

∙ 06/07/2019

Empirical Likelihood for Contextual Bandits

We apply empirical likelihood techniques to contextual bandit policy val...

5 Nikos Karampatziakis, et al. ∙

research

∙ 05/31/2019

Efficient Forward Architecture Search

We propose a neural architecture search (NAS) algorithm, Petridish, to i...

4 Hanzhang Hu, et al. ∙

research

∙ 02/05/2019

Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting

We study contextual bandit learning with an abstract policy class and co...

38 Akshay Krishnamurthy, et al. ∙

research

∙ 01/25/2019

Provably efficient RL with Rich Observations via Latent State Decoding

We study the exploration problem in episodic MDPs with rich observations...

0 Simon S. Du, et al. ∙

research

∙ 01/02/2019

Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

We investigate the feasibility of learning from both fully-labeled super...

6 Chicheng Zhang, et al. ∙

research

∙ 11/21/2018

Model-Based Reinforcement Learning in Contextual Decision Processes

We study the sample complexity of model-based reinforcement learning in ...

12 Wen Sun, et al. ∙

research

∙ 07/17/2018

Contextual Memory Trees

We design and study a Contextual Memory Tree (CMT), a learning memory co...

6 Wen Sun, et al. ∙

research

∙ 03/06/2018

A Reductions Approach to Fair Classification

We present a systematic approach for achieving fairness in a binary clas...

0 Alekh Agarwal, et al. ∙

research

∙ 03/01/2018

On Polynomial Time PAC Reinforcement Learning with Rich Observations

We study the computational tractability of provably sample-efficient (PA...

0 Christoph Dann, et al. ∙

research

∙ 02/12/2018

Practical Evaluation and Optimization of Contextual Bandit Algorithms

We study and empirically optimize contextual bandit learning, exploratio...

0 Alberto Bietti, et al. ∙

research

∙ 08/05/2017

Efficient Contextual Bandits in Non-stationary Worlds

Most contextual bandit algorithms minimize regret to the best fixed poli...

0 Haipeng Luo, et al. ∙

research

∙ 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...

0 Dipendra Misra, et al. ∙

research

∙ 03/03/2017

Active Learning for Cost-Sensitive Classification

We design an active learning algorithm for cost-sensitive multiclass cla...

0 Akshay Krishnamurthy, et al. ∙

research

∙ 10/29/2016

Contextual Decision Processes with Low Bellman Rank are PAC-Learnable

This paper studies systematic exploration for reinforcement learning wit...

0 Nan Jiang, et al. ∙

research

∙ 06/15/2016

Logarithmic Time One-Against-Some

We create a new online reduction of multiclass classification to binary ...

0 Hal Daumé III, et al. ∙

research

∙ 05/16/2016

Off-policy evaluation for slate recommendation

This paper studies the evaluation of policies that recommend an ordered ...

0 Adith Swaminathan, et al. ∙

research

∙ 02/23/2016

Search Improves Label for Active Learning

We investigate active learning with access to two distinct oracles: Labe...

0 Alina Beygelzimer, et al. ∙

research

∙ 02/08/2016

PAC Reinforcement Learning with Rich Observations

We propose and study a new model for reinforcement learning with rich ob...

0 Akshay Krishnamurthy, et al. ∙

research

∙ 06/29/2015

Efficient and Parsimonious Agnostic Active Learning

We develop a new active learning algorithm for the streaming setting sat...

0 Tzu-Kuo Huang, et al. ∙

research

∙ 03/18/2015

Learning to Search for Dependencies

We demonstrate that a dependency parser can be built using a credit assi...

0 Kai-Wei Chang, et al. ∙

research

∙ 03/10/2015

Doubly Robust Policy Evaluation and Optimization

We study sequential decision making in environments where rewards are on...

0 Miroslav Dudík, et al. ∙

research

∙ 02/08/2015

Learning to Search Better Than Your Teacher

Methods for learning to search for structured prediction typically imita...

0 Kai-Wei Chang, et al. ∙

research

∙ 10/02/2014

Scalable Nonlinear Learning with Adaptive Polynomial Expansions

Can we effectively learn a nonlinear representation in time comparable t...

0 Alekh Agarwal, et al. ∙

research

∙ 08/09/2014

Normalized Online Learning

We introduce online learning algorithms which are independent of feature...

0 Stéphane Ross, et al. ∙

research

∙ 08/09/2014

Conditional Probability Tree Estimation Analysis and Algorithms

We consider the problem of estimating the conditional probability of a l...

0 Alina Beygelzimer, et al. ∙

research

∙ 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...

0 Alekh Agarwal, et al. ∙

research

∙ 10/16/2012

Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits

We present and prove properties of a new offline policy evaluator for an...

0 Miroslav Dudík, et al. ∙

research

∙ 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...

0 John Langford, et al. ∙

research

∙ 06/27/2012

Predicting Conditional Quantiles via Reduction to Classification

We show how to reduce the process of predicting general order statistics...

0 John Langford, et al. ∙

research

∙ 01/31/2012

Learning Performance of Prediction Markets with Kelly Bettors

In evaluating prediction markets (and other crowd-prediction mechanisms)...

0 Alina Beygelzimer, et al. ∙

John Langford

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