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03/26/2023
Inverse Reinforcement Learning without Reinforcement Learning
Inverse Reinforcement Learning (IRL) is a powerful set of techniques for...
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03/01/2023
The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
We propose a novel approach to addressing two fundamental challenges in ...
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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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08/19/2022
Game-Theoretic Algorithms for Conditional Moment Matching
A variety of problems in econometrics and machine learning, including in...
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08/03/2022
Sequence Model Imitation Learning with Unobserved Contexts
We consider imitation learning problems where the expert has access to a...
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05/30/2022
Minimax Optimal Online Imitation Learning via Replay Estimation
Online imitation learning is the problem of how best to mimic expert dem...
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02/02/2022
Causal Imitation Learning under Temporally Correlated Noise
We develop algorithms for imitation learning from policy data that was c...
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11/17/2021
On the Effectiveness of Iterative Learning Control
Iterative learning control (ILC) is a powerful technique for high perfor...
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10/05/2021
A Critique of Strictly Batch Imitation Learning
Recent work by Jarrett et al. attempts to frame the problem of offline i...
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03/04/2021
Of Moments and Matching: Trade-offs and Treatments in Imitation Learning
We provide a unifying view of a large family of previous imitation learn...
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02/04/2021
Feedback in Imitation Learning: The Three Regimes of Covariate Shift
Imitation learning practitioners have often noted that conditioning poli...
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09/21/2020
CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models
Given access to accurate dynamical models, modern planning approaches ar...
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03/31/2020
TRON: A Fast Solver for Trajectory Optimization with Non-Smooth Cost Functions
Trajectory optimization is an important tool for control and planning of...
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03/31/2020
Exploration in Action Space
Parameter space exploration methods with black-box optimization have rec...
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03/09/2020
Planning and Execution using Inaccurate Models with Provable Guarantees
Models used in modern planning problems to simulate outcomes of real wor...
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05/27/2019
Provably Efficient Imitation Learning from Observation Alone
We study Imitation Learning (IL) from Observations alone (ILFO) in large...
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01/31/2019
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective
Black-box optimizers that explore in parameter space have often been sho...
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11/16/2018
An Algorithmic Perspective on Imitation Learning
As robots and other intelligent agents move from simple environments and...
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05/29/2018
Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning
In this paper, we propose to combine imitation and reinforcement learnin...
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05/28/2018
Dual Policy Iteration
Recently, a novel class of Approximate Policy Iteration (API) algorithms...
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10/30/2017
Log-DenseNet: How to Sparsify a DenseNet
Skip connections are increasingly utilized by deep neural networks to im...
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09/25/2017
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Recurrent neural networks (RNNs) are a vital modeling technique that rel...
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08/22/2017
Anytime Neural Network: a Versatile Trade-off Between Computation and Accuracy
Anytime predictors first produce crude results quickly, and then continu...
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09/28/2016
A Discriminative Framework for Anomaly Detection in Large Videos
We address an anomaly detection setting in which training sequences are ...
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08/01/2016
Learning Transferable Policies for Monocular Reactive MAV Control
The ability to transfer knowledge gained in previous tasks into new cont...
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07/28/2016
Introspective Perception: Learning to Predict Failures in Vision Systems
As robots aspire for long-term autonomous operations in complex dynamic ...
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11/28/2014
Solving Games with Functional Regret Estimation
We propose a novel online learning method for minimizing regret in large...
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11/24/2014
Vision and Learning for Deliberative Monocular Cluttered Flight
Cameras provide a rich source of information while being passive, cheap ...
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11/18/2014
A Unified View of Large-scale Zero-sum Equilibrium Computation
The task of computing approximate Nash equilibria in large zero-sum exte...
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10/27/2014
Visual Chunking: A List Prediction Framework for Region-Based Object Detection
We consider detecting objects in an image by iteratively selecting from ...
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06/23/2014
Reinforcement and Imitation Learning via Interactive No-Regret Learning
Recent work has demonstrated that problems-- particularly imitation lear...
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02/24/2014
Near Optimal Bayesian Active Learning for Decision Making
How should we gather information to make effective decisions? We address...
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08/15/2013
Computational Rationalization: The Inverse Equilibrium Problem
Modeling the purposeful behavior of imperfect agents from a small number...
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05/11/2013
Learning Policies for Contextual Submodular Prediction
Many prediction domains, such as ad placement, recommendation, trajector...
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12/12/2012
Learning with Scope, with Application to Information Extraction and Classification
In probabilistic approaches to classification and information extraction...
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11/07/2012
Learning Monocular Reactive UAV Control in Cluttered Natural Environments
Autonomous navigation for large Unmanned Aerial Vehicles (UAVs) is fairl...
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06/20/2012
Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification
Dealing with uncertainty in Bayesian Network structures using maximum a ...
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05/09/2012
Convex Coding
Inspired by recent work on convex formulations of clustering (Lashkari &...
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03/05/2012
Agnostic System Identification for Model-Based Reinforcement Learning
A fundamental problem in control is to learn a model of a system from ob...
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02/09/2012
Predicting Contextual Sequences via Submodular Function Maximization
Sequence optimization, where the items in a list are ordered to maximize...
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08/16/2011
Stability Conditions for Online Learnability
Stability is a general notion that quantifies the sensitivity of a learn...
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05/10/2011
Generalized Boosting Algorithms for Convex Optimization
Boosting is a popular way to derive powerful learners from simpler hypot...
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11/02/2010
