A GPU-tailored approach for training kernelized SVMs. - Andrew Cotter; Nathan Srebro; Joseph Keshet (KDD, 2011)

Better Mini-Batch Algorithms via Accelerated Gradient Methods. - Andrew Cotter; Ohad Shamir; Nati Srebro; Karthik Sridharan (NIPS, 2011)

Parallelism in dynamic well-spaced point sets. - Umut A. Acar; Andrew Cotter; Benoît Hudson; Duru Türkoglu (SPAA, 2011)

Explicit Approximations of the Gaussian Kernel - Andrew Cotter; Joseph Keshet; Nathan Srebro (CoRR, 2011)

Pegasos: primal estimated sub-gradient solver for SVM. - Shai Shalev-Shwartz; Yoram Singer; Nathan Srebro; Andrew Cotter (Math. Program., 2011)

Stochastic optimization for PCA and PLS. - Raman Arora; Andrew Cotter; Karen Livescu; Nathan Srebro (Allerton Conference, 2012)

The Kernelized Stochastic Batch Perceptron. - Andrew Cotter; Shai Shalev-Shwartz; Nathan Srebro (ICML, 2012)

Learning Optimally Sparse Support Vector Machines. - Andrew Cotter; Shai Shalev-Shwartz; Nati Srebro (ICML (1), 2013)

Stochastic Optimization of PCA with Capped MSG. - Raman Arora; Andrew Cotter; Nati Srebro (NIPS, 2013)

Dynamic well-spaced point sets. - Umut A. Acar; Andrew Cotter; Benoît Hudson; Duru Türkoglu (Comput. Geom., 2013)

Stochastic Optimization for Machine Learning. - Andrew Cotter (CoRR, 2013)

Monotonic Calibrated Interpolated Look-Up Tables. - Maya R. Gupta; Andrew Cotter; Jan Pfeifer; Konstantin Voevodski; Kevin Robert Canini; Alexander Mangylov; Wojtek Moczydlowski (CoRR, 2015)

A Light Touch for Heavily Constrained SGD. - Andrew Cotter; Maya R. Gupta; Jan Pfeifer (COLT, 2016)

Fast and Flexible Monotonic Functions with Ensembles of Lattices. - Mahdi Milani Fard; Kevin Robert Canini; Andrew Cotter; Jan Pfeifer; Maya R. Gupta (NIPS, 2016)

Satisfying Real-world Goals with Dataset Constraints. - Gabriel Goh; Andrew Cotter; Maya R. Gupta; Michael P. Friedlander (NIPS, 2016)

Monotonic Calibrated Interpolated Look-Up Tables. - Maya R. Gupta; Andrew Cotter; Jan Pfeifer; Konstantin Voevodski; Kevin Robert Canini; Alexander Mangylov; Wojtek Moczydlowski; Alexander Van Esbroeck (J. Mach. Learn. Res., 2016)

Constrained Interacting Submodular Groupings. - Andrew Cotter; Mahdi Milani Fard; Seungil You; Maya R. Gupta; Jeff A. Bilmes (ICML, 2018)

Diminishing Returns Shape Constraints for Interpretability and Regularization. - Maya R. Gupta; Dara Bahri; Andrew Cotter; Kevin Robert Canini (NeurIPS, 2018)

Interpretable Set Functions. - Andrew Cotter; Maya R. Gupta; Heinrich Jiang; James Muller; Taman Narayan; Serena Lutong Wang; Tao Zhu 005 (CoRR, 2018)

Proxy Fairness. - Maya R. Gupta; Andrew Cotter; Mahdi Milani Fard; Serena Lutong Wang (CoRR, 2018)

Two-Player Games for Efficient Non-Convex Constrained Optimization. - Andrew Cotter; Heinrich Jiang; Karthik Sridharan (ALT, 2019)

Shape Constraints for Set Functions. - Andrew Cotter; Maya R. Gupta; Heinrich Jiang; Erez Louidor; James Muller; Taman Narayan; Serena Lutong Wang; Tao Zhu 005 (ICML, 2019)

Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. - Andrew Cotter; Maya R. Gupta; Heinrich Jiang; Nathan Srebro; Karthik Sridharan; Serena Lutong Wang; Blake E. Woodworth; Seungil You (ICML, 2019)

On Making Stochastic Classifiers Deterministic. - Andrew Cotter; Maya R. Gupta; Harikrishna Narasimhan (NeurIPS, 2019)

Optimizing Generalized Rate Metrics with Three Players. - Harikrishna Narasimhan; Andrew Cotter; Maya R. Gupta (NeurIPS, 2019)

Optimizing Generalized Rate Metrics through Game Equilibrium. - Harikrishna Narasimhan; Andrew Cotter; Maya R. Gupta (CoRR, 2019)

Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. - Andrew Cotter; Heinrich Jiang; Maya R. Gupta; Serena Lutong Wang; Taman Narayan; Seungil You; Karthik Sridharan (J. Mach. Learn. Res., 2019)

Pairwise Fairness for Ranking and Regression. - Harikrishna Narasimhan; Andrew Cotter; Maya R. Gupta; Serena Lutong Wang (AAAI, 2020)

Approximate Heavily-Constrained Learning with Lagrange Multiplier Models. - Harikrishna Narasimhan; Andrew Cotter; Yichen Zhou; Serena Lutong Wang; Wenshuo Guo (NeurIPS, 2020)

Robust Optimization for Fairness with Noisy Protected Groups. - Serena Lutong Wang; Wenshuo Guo; Harikrishna Narasimhan; Andrew Cotter; Maya R. Gupta; Michael I. Jordan (NeurIPS, 2020)

Implicit rate-constrained optimization of non-decomposable objectives. - Abhishek Kumar; Harikrishna Narasimhan; Andrew Cotter (ICML, 2021)

Distilling Double Descent. - Andrew Cotter; Aditya Krishna Menon; Harikrishna Narasimhan; Ankit Singh Rawat; Sashank J. Reddi; Yichen Zhou (CoRR, 2021)

Churn Reduction via Distillation. - Heinrich Jiang; Harikrishna Narasimhan; Dara Bahri; Andrew Cotter; Afshin Rostamizadeh (ICLR, 2022)