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Implicit Rate-Constrained Optimization of Non-decomposable Objectives


Jul 29, 2021
Abhishek Kumar, Harikrishna Narasimhan, Andrew Cotter

* ICML 2021; Code available at https://github.com/google-research/google-research/tree/master/implicit_constrained_optimization 

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Churn Reduction via Distillation


Jun 04, 2021
Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh


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Distilling Double Descent


Feb 13, 2021
Andrew Cotter, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sashank J. Reddi, Yichen Zhou


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Robust Optimization for Fairness with Noisy Protected Groups


Feb 21, 2020
Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Michael I. Jordan


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Optimizing Generalized Rate Metrics through Game Equilibrium


Sep 06, 2019
Harikrishna Narasimhan, Andrew Cotter, Maya Gupta


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Pairwise Fairness for Ranking and Regression


Jun 12, 2019
Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Serena Wang


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Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints


Sep 28, 2018
Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, Seungil You


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Two-Player Games for Efficient Non-Convex Constrained Optimization


Sep 28, 2018
Andrew Cotter, Heinrich Jiang, Karthik Sridharan


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Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals


Sep 11, 2018
Andrew Cotter, Heinrich Jiang, Serena Wang, Taman Narayan, Maya Gupta, Seungil You, Karthik Sridharan


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Proxy Fairness


Jun 28, 2018
Maya Gupta, Andrew Cotter, Mahdi Milani Fard, Serena Wang


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Interpretable Set Functions


May 31, 2018
Andrew Cotter, Maya Gupta, Heinrich Jiang, James Muller, Taman Narayan, Serena Wang, Tao Zhu


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Satisfying Real-world Goals with Dataset Constraints


May 03, 2017
Gabriel Goh, Andrew Cotter, Maya Gupta, Michael Friedlander


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A Light Touch for Heavily Constrained SGD


Oct 24, 2016
Andrew Cotter, Maya Gupta, Jan Pfeifer

* 29th Annual Conference on Learning Theory, pp. 729-771, 2016 

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Monotonic Calibrated Interpolated Look-Up Tables


Jan 20, 2016
Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Canini, Alexander Mangylov, Wojtek Moczydlowski, Alex van Esbroeck

* To appear (with minor revisions), Journal Machine Learning Research 2016 

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Stochastic Optimization for Machine Learning


Aug 15, 2013
Andrew Cotter

* PhD Thesis 

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Stochastic Optimization of PCA with Capped MSG


Jul 05, 2013
Raman Arora, Andrew Cotter, Nathan Srebro


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The Kernelized Stochastic Batch Perceptron


Jun 21, 2012
Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro


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Explicit Approximations of the Gaussian Kernel


Sep 21, 2011
Andrew Cotter, Joseph Keshet, Nathan Srebro

* 11 pages, 2 tables, 2 figures 

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Better Mini-Batch Algorithms via Accelerated Gradient Methods


Jun 22, 2011
Andrew Cotter, Ohad Shamir, Nathan Srebro, Karthik Sridharan


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