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03/03/2021
Successor Feature Sets: Generalizing Successor Representations Across Policies
Successor-style representations have many advantages for reinforcement l...
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02/24/2021
Understanding and Mitigating Accuracy Disparity in Regression
With the widespread deployment of large-scale prediction systems in high...
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12/19/2020
Fundamental Limits and Tradeoffs in Invariant Representation Learning
Many machine learning applications involve learning representations that...
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10/10/2020
An Empirical Investigation of Beam-Aware Training in Supertagging
Structured prediction is often approached by training a locally normaliz...
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10/16/2019
Conditional Learning of Fair Representations
We propose a novel algorithm for learning fair representations that can ...
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07/14/2019
Learning Neural Networks with Adaptive Regularization
Feed-forward neural networks can be understood as a combination of an in...
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06/19/2019
Inherent Tradeoffs in Learning Fair Representation
With the prevalence of machine learning in high-stakes applications, esp...
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06/19/2019
Adversarial Task-Specific Privacy Preservation under Attribute Attack
With the prevalence of machine learning services, crowdsourced data cont...
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01/27/2019
On Learning Invariant Representation for Domain Adaptation
Due to the ability of deep neural nets to learn rich representations, re...
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12/12/2018
An Empirical Study of Example Forgetting during Deep Neural Network Learning
Inspired by the phenomenon of catastrophic forgetting, we investigate th...
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11/01/2018
Learning Beam Search Policies via Imitation Learning
Beam search is widely used for approximate decoding in structured predic...
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05/28/2018
Dual Policy Iteration
Recently, a novel class of Approximate Policy Iteration (API) algorithms...
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10/02/2017
Unsupervised Learning for Nonlinear PieceWise Smooth Hybrid Systems
This paper introduces a novel system identification and tracking method ...
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05/26/2017
Multiple Source Domain Adaptation with Adversarial Training of Neural Networks
While domain adaptation has been actively researched in recent years, mo...
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02/12/2017
Supervised Learning for Controlled Dynamical System Learning
We develop a framework for reducing the identification of controlled dyn...
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01/23/2017
Constant Size Molecular Descriptors For Use With Machine Learning
A set of molecular descriptors whose length is independent of molecular ...
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06/20/2013
Galerkin Methods for Complementarity Problems and Variational Inequalities
Complementarity problems and variational inequalities arise in a wide va...
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07/10/2012
A Spectral Learning Approach to Range-Only SLAM
We present a novel spectral learning algorithm for simultaneous localiza...
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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 ...
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10/30/2010
Predictive State Temporal Difference Learning
We propose a new approach to value function approximation which combines...
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12/12/2009
Closing the Learning-Planning Loop with Predictive State Representations
A central problem in artificial intelligence is that of planning to maxi...
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10/06/2009
