Geoffrey J. Gordon | DeepAI

DeepAI

Featured Co-authors

Yoshua Bengio
418 publications
Ruslan Salakhutdinov
179 publications
Kun Zhang
106 publications
Byron Boots
82 publications
Pradeep Ravikumar
71 publications
Yuan Tian
60 publications
Han Zhao
49 publications
Wen Sun
48 publications
José M. F. Moura
42 publications
Adam Trischler
42 publications
J Andrew Bagnell
41 publications

∙ 03/03/2021

Successor Feature Sets: Generalizing Successor Representations Across Policies

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

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...

Jianfeng Chi, et al. ∙

∙ 12/19/2020

Fundamental Limits and Tradeoffs in Invariant Representation Learning

Many machine learning applications involve learning representations that...

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...

Renato Negrinho, et al. ∙

∙ 10/16/2019

Conditional Learning of Fair Representations

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

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...

Han Zhao, et al. ∙

∙ 06/19/2019

Inherent Tradeoffs in Learning Fair Representation

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

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...

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...

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...

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...

Renato Negrinho, et al. ∙

∙ 05/28/2018

Dual Policy Iteration

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

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 ...

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...

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...

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 ...

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...

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...

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 ...

Stéphane Ross, et al. ∙

∙ 10/30/2010

Predictive State Temporal Difference Learning

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

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...

Byron Boots, et al. ∙

∙ 10/06/2009

Reduced-Rank Hidden Markov Models

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

Sajid M. Siddiqi, et al. ∙