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no code implementations • 3 Mar 2021 • Kianté Brantley, Soroush Mehri, Geoffrey J. Gordon

They also form a natural bridge between model-based and model-free RL methods: like the former they make predictions about future experiences, and like the latter they allow efficient prediction of total discounted rewards.

1 code implementation • 24 Feb 2021 • Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon, Han Zhao

With the widespread deployment of large-scale prediction systems in high-stakes domains, e. g., face recognition, criminal justice, etc., disparity in prediction accuracy between different demographic subgroups has called for fundamental understanding on the source of such disparity and algorithmic intervention to mitigate it.

no code implementations • 19 Dec 2020 • Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar

A wide range of machine learning applications such as privacy-preserving learning, algorithmic fairness, and domain adaptation/generalization among others, involve learning invariant representations of the data that aim to achieve two competing goals: (a) maximize information or accuracy with respect to a target response, and (b) maximize invariance or independence with respect to a set of protected features (e. g., for fairness, privacy, etc).

1 code implementation • Findings of the Association for Computational Linguistics 2020 • Renato Negrinho, Matthew R. Gormley, Geoffrey J. Gordon

This approach leads to mismatches as, during training, the model is not exposed to its mistakes and does not use beam search.

1 code implementation • ICLR 2020 • Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon

We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting.

no code implementations • 25 Sep 2019 • Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon

With the prevalence of machine learning services, crowdsourced data containing sensitive information poses substantial privacy challenges.

1 code implementation • NeurIPS 2019 • Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Geoffrey J. Gordon

Feed-forward neural networks can be understood as a combination of an intermediate representation and a linear hypothesis.

no code implementations • NeurIPS 2020 • Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon

Meanwhile, it is clear that in general there is a tension between minimizing information leakage and maximizing task accuracy.

no code implementations • NeurIPS 2019 • Han Zhao, Geoffrey J. Gordon

On the upside, we prove that if the group-wise Bayes optimal classifiers are close, then learning fair representations leads to an alternative notion of fairness, known as the accuracy parity, which states that the error rates are close between groups.

2 code implementations • 27 Jan 2019 • Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon

Our result characterizes a fundamental tradeoff between learning invariant representations and achieving small joint error on both domains when the marginal label distributions differ from source to target.

2 code implementations • ICLR 2019 • Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, Geoffrey J. Gordon

Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks.

no code implementations • NeurIPS 2018 • Han Zhao, Shanghang Zhang, Guanhang Wu, José M. F. Moura, Joao P. Costeira, Geoffrey J. Gordon

In this paper we propose new generalization bounds and algorithms under both classification and regression settings for unsupervised multiple source domain adaptation.

Ranked #3 on Domain Adaptation on GTA5+Synscapes to Cityscapes

1 code implementation • NeurIPS 2018 • Renato Negrinho, Matthew R. Gormley, Geoffrey J. Gordon

Beam search is widely used for approximate decoding in structured prediction problems.

no code implementations • NeurIPS 2018 • Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell

Recently, a novel class of Approximate Policy Iteration (API) algorithms have demonstrated impressive practical performance (e. g., ExIt from [2], AlphaGo-Zero from [27]).

no code implementations • ICLR 2018 • Han Zhao, Shanghang Zhang, Guanhang Wu, Jo\~{a}o P. Costeira, Jos\'{e} M. F. Moura, Geoffrey J. Gordon

We propose a new generalization bound for domain adaptation when there are multiple source domains with labeled instances and one target domain with unlabeled instances.

4 code implementations • 26 May 2017 • Han Zhao, Shanghang Zhang, Guanhang Wu, João P. Costeira, José M. F. Moura, Geoffrey J. Gordon

As a step toward bridging the gap, we propose a new generalization bound for domain adaptation when there are multiple source domains with labeled instances and one target domain with unlabeled instances.

no code implementations • ICML 2017 • Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell

We demonstrate that AggreVaTeD --- a policy gradient extension of the Imitation Learning (IL) approach of (Ross & Bagnell, 2014) --- can leverage such an oracle to achieve faster and better solutions with less training data than a less-informed Reinforcement Learning (RL) technique.

1 code implementation • 12 Feb 2017 • Ahmed Hefny, Carlton Downey, Geoffrey J. Gordon

We propose a framework for modeling and estimating the state of controlled dynamical systems, where an agent can affect the system through actions and receives partial observations.

1 code implementation • 23 Jan 2017 • Christopher R. Collins, Geoffrey J. Gordon, O. Anatole von Lilienfeld, David J. Yaron

A set of molecular descriptors whose length is independent of molecular size is developed for machine learning models that target thermodynamic and electronic properties of molecules.

no code implementations • 20 Jun 2013 • Geoffrey J. Gordon

One promising idea is Galerkin approximation, in which we search for the best answer within the span of a given set of basis functions.

no code implementations • NeurIPS 2010 • Byron Boots, Geoffrey J. Gordon

We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification.

3 code implementations • 2 Nov 2010 • Stephane Ross, Geoffrey J. Gordon, J. Andrew Bagnell

Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i. i. d.

no code implementations • 6 Oct 2009 • Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon

We introduce the Reduced-Rank Hidden Markov Model (RR-HMM), a generalization of HMMs that can model smooth state evolution as in Linear Dynamical Systems (LDSs) as well as non-log-concave predictive distributions as in continuous-observation HMMs.

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