Geoffrey J. Gordon | DeepAI

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∙ 03/03/2021

Successor Feature Sets: Generalizing Successor Representations Across Policies

Successor-style representations have many advantages for reinforcement l...

8 Kianté Brantley, et al. ∙

∙ 02/24/2021

Understanding and Mitigating Accuracy Disparity in Regression

With the widespread deployment of large-scale prediction systems in high...

0 Jianfeng Chi, et al. ∙

∙ 12/19/2020

Fundamental Limits and Tradeoffs in Invariant Representation Learning

Many machine learning applications involve learning representations that...

0 Han Zhao, et al. ∙

∙ 10/10/2020

An Empirical Investigation of Beam-Aware Training in Supertagging

Structured prediction is often approached by training a locally normaliz...

0 Renato Negrinho, et al. ∙

∙ 10/16/2019

Conditional Learning of Fair Representations

We propose a novel algorithm for learning fair representations that can ...

0 Han Zhao, et al. ∙

∙ 07/14/2019

Learning Neural Networks with Adaptive Regularization

Feed-forward neural networks can be understood as a combination of an in...

3 Han Zhao, et al. ∙

∙ 06/19/2019

Inherent Tradeoffs in Learning Fair Representation

With the prevalence of machine learning in high-stakes applications, esp...

0 Han Zhao, et al. ∙

∙ 06/19/2019

Adversarial Task-Specific Privacy Preservation under Attribute Attack

With the prevalence of machine learning services, crowdsourced data cont...

0 Han Zhao, et al. ∙

∙ 01/27/2019

On Learning Invariant Representation for Domain Adaptation

Due to the ability of deep neural nets to learn rich representations, re...

0 Han Zhao, et al. ∙

∙ 12/12/2018

An Empirical Study of Example Forgetting during Deep Neural Network Learning

Inspired by the phenomenon of catastrophic forgetting, we investigate th...

14 Mariya Toneva, et al. ∙

∙ 11/01/2018

Learning Beam Search Policies via Imitation Learning

Beam search is widely used for approximate decoding in structured predic...

0 Renato Negrinho, et al. ∙

∙ 05/28/2018

Dual Policy Iteration

Recently, a novel class of Approximate Policy Iteration (API) algorithms...

0 Wen Sun, et al. ∙

∙ 10/02/2017

Unsupervised Learning for Nonlinear PieceWise Smooth Hybrid Systems

This paper introduces a novel system identification and tracking method ...

0 Gilwoo Lee, et al. ∙

∙ 05/26/2017

Multiple Source Domain Adaptation with Adversarial Training of Neural Networks

While domain adaptation has been actively researched in recent years, mo...

0 Han Zhao, et al. ∙

∙ 02/12/2017

Supervised Learning for Controlled Dynamical System Learning

We develop a framework for reducing the identification of controlled dyn...

0 Ahmed Hefny, et al. ∙

∙ 01/23/2017

Constant Size Molecular Descriptors For Use With Machine Learning

A set of molecular descriptors whose length is independent of molecular ...

0 Christopher R. Collins, et al. ∙

∙ 06/20/2013

Galerkin Methods for Complementarity Problems and Variational Inequalities

Complementarity problems and variational inequalities arise in a wide va...

0 Geoffrey J. Gordon, et al. ∙

∙ 07/10/2012

A Spectral Learning Approach to Range-Only SLAM

We present a novel spectral learning algorithm for simultaneous localiza...

0 Byron Boots, et al. ∙

∙ 11/02/2010

A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

Sequential prediction problems such as imitation learning, where future ...

0 Stéphane Ross, et al. ∙

∙ 10/30/2010

Predictive State Temporal Difference Learning

We propose a new approach to value function approximation which combines...

0 Byron Boots, et al. ∙

∙ 12/12/2009

Closing the Learning-Planning Loop with Predictive State Representations

A central problem in artificial intelligence is that of planning to maxi...

0 Byron Boots, et al. ∙

∙ 10/06/2009

Reduced-Rank Hidden Markov Models

We introduce the Reduced-Rank Hidden Markov Model (RR-HMM), a generaliza...

0 Sajid M. Siddiqi, et al. ∙

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