Maya R. Gupta

Publication Activity (10 Years)

Years Active: 2000-2024
Publications (10 Years): 44

Top Venues

CoRR

ICML

NeurIPS

J. Mach. Learn. Res.

Publications

Taman Narayan, Serena Lutong Wang, Kevin Robert Canini, Maya R. Gupta
Expected Pinball Loss For Quantile Regression And Inverse CDF Estimation. Trans. Mach. Learn. Res. 2024 (2024)
Sen Zhao, Erez Louidor, Oleksandr Mangylov, Maya R. Gupta
Global Optimization Networks. CoRR (2022)
Sen Zhao, Erez Louidor, Maya R. Gupta
Global Optimization Networks. ICML (2022)
Serena Wang, Maya R. Gupta, Seungil You
Quit When You Can: Efficient Evaluation of Ensembles by Optimized Ordering. ACM J. Emerg. Technol. Comput. Syst. 17 (4) (2021)
Heinrich Jiang, Maya R. Gupta
Bootstrapping for Batch Active Sampling. KDD (2021)
Nathan Zhang, Kevin Canini, Sean Silva, Maya R. Gupta
Fast Linear Interpolation. ACM J. Emerg. Technol. Comput. Syst. 17 (2) (2021)
Taman Narayan, Serena Wang, Kevin Robert Canini, Maya R. Gupta
Regularization Strategies for Quantile Regression. CoRR (2021)
Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan, Mahdi Milani Fard, Maya R. Gupta
Optimizing Black-box Metrics with Adaptive Surrogates. CoRR (2020)
Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Serena Wang
Pairwise Fairness for Ranking and Regression. AAAI (2020)
Dara Bahri, Heinrich Jiang, Maya R. Gupta
Deep k-NN for Noisy Labels. ICML (2020)
Serena Wang, Maya R. Gupta
Deontological Ethics By Monotonicity Shape Constraints. AISTATS (2020)
Serena Wang, Maya R. Gupta
Deontological Ethics By Monotonicity Shape Constraints. CoRR (2020)
Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan
Robust Optimization for Fairness with Noisy Protected Groups. CoRR (2020)
Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan
Robust Optimization for Fairness with Noisy Protected Groups. NeurIPS (2020)
Dara Bahri, Heinrich Jiang, Maya R. Gupta
Deep k-NN for Noisy Labels. CoRR (2020)
Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan, Mahdi Milani Fard, Maya R. Gupta
Optimizing Black-box Metrics with Adaptive Surrogates. ICML (2020)
Maya R. Gupta, Erez Louidor, Oleksandr Mangylov, Nobu Morioka, Taman Narayan, Sen Zhao
Multidimensional Shape Constraints. ICML (2020)
Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta
Optimizing Generalized Rate Metrics with Three Players. NeurIPS (2019)
Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya R. Gupta
Metric-Optimized Example Weights. ICML (2019)
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Erez Louidor, James Muller, Taman Narayan, Serena Wang, Tao Zhu
Shape Constraints for Set Functions. ICML (2019)
Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta
Optimizing Generalized Rate Metrics through Game Equilibrium. CoRR (2019)
Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Serena Wang
Pairwise Fairness for Ranking and Regression. CoRR (2019)
Andrew Cotter, Heinrich Jiang, Maya R. Gupta, Serena Wang, Taman Narayan, Seungil You, Karthik Sridharan
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. J. Mach. Learn. Res. 20 (2019)
Andrew Cotter, Maya R. Gupta, Harikrishna Narasimhan
On Making Stochastic Classifiers Deterministic. NeurIPS (2019)
Heinrich Jiang, Maya R. Gupta
Minimum-Margin Active Learning. CoRR (2019)
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake E. Woodworth, Seungil You
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. ICML (2019)
Heinrich Jiang, Been Kim, Melody Y. Guan, Maya R. Gupta
To Trust Or Not To Trust A Classifier. NeurIPS (2018)
Andrew Cotter, Mahdi Milani Fard, Seungil You, Maya R. Gupta, Jeff A. Bilmes
Constrained Interacting Submodular Groupings. ICML (2018)
Maya R. Gupta, Dara Bahri, Andrew Cotter, Kevin Robert Canini
Diminishing Returns Shape Constraints for Interpretability and Regularization. NeurIPS (2018)
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake E. Woodworth, Seungil You
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. CoRR (2018)
Maya R. Gupta, Andrew Cotter, Mahdi Milani Fard, Serena Wang
Proxy Fairness. CoRR (2018)
Sen Zhao, Mahdi Milani Fard, Maya R. Gupta
Metric-Optimized Example Weights. CoRR (2018)
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, James Muller, Taman Narayan, Serena Wang, Tao Zhu
Interpretable Set Functions. CoRR (2018)
Andrew Cotter, Heinrich Jiang, Serena Wang, Taman Narayan, Maya R. Gupta, Seungil You, Karthik Sridharan
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. CoRR (2018)
Heinrich Jiang, Been Kim, Maya R. Gupta
To Trust Or Not To Trust A Classifier. CoRR (2018)
Serena Wang, Maya R. Gupta, Seungil You
Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization. CoRR (2018)
Seungil You, David Ding, Kevin Robert Canini, Jan Pfeifer, Maya R. Gupta
Deep Lattice Networks and Partial Monotonic Functions. NIPS (2017)
Seungil You, David Ding, Kevin Robert Canini, Jan Pfeifer, Maya R. Gupta
Deep Lattice Networks and Partial Monotonic Functions. CoRR (2017)
Andrew Cotter, Maya R. Gupta, Jan Pfeifer
A Light Touch for Heavily Constrained SGD. COLT (2016)
Andrew Cotter, Michael P. Friedlander, Gabriel Goh, Maya R. Gupta
Satisfying Real-world Goals with Dataset Constraints. CoRR (2016)
Gabriel Goh, Andrew Cotter, Maya R. Gupta, Michael P. Friedlander
Satisfying Real-world Goals with Dataset Constraints. NIPS (2016)
Mahdi Milani Fard, Quentin Cormier, Kevin Robert Canini, Maya R. Gupta
Launch and Iterate: Reducing Prediction Churn. NIPS (2016)
Maya R. Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Robert Canini, Alexander Mangylov, Wojtek Moczydlowski, Alexander Van Esbroeck
Monotonic Calibrated Interpolated Look-Up Tables. J. Mach. Learn. Res. 17 (2016)
Mahdi Milani Fard, Kevin Robert Canini, Andrew Cotter, Jan Pfeifer, Maya R. Gupta
Fast and Flexible Monotonic Functions with Ensembles of Lattices. NIPS (2016)
Maya R. Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Robert Canini, Alexander Mangylov, Wojtek Moczydlowski
Monotonic Calibrated Interpolated Look-Up Tables. CoRR (2015)
Andrew Cotter, Maya R. Gupta, Jan Pfeifer
A Light Touch for Heavily Constrained SGD. CoRR (2015)
Sergey Feldman, Maya R. Gupta, Bela A. Frigyik
Revisiting Stein's paradox: multi-task averaging. J. Mach. Learn. Res. 15 (1) (2014)
Maya R. Gupta, Samy Bengio, Jason Weston
Training highly multiclass classifiers. J. Mach. Learn. Res. 15 (1) (2014)
Nathan Parrish, Hyrum S. Anderson, Maya R. Gupta, Dun-Yu Hsiao
Classifying with confidence from incomplete information. J. Mach. Learn. Res. 14 (1) (2013)
Raman Arora, Maya R. Gupta, Amol Kapila, Maryam Fazel
Similarity-based clustering by left-stochastic matrix factorization. J. Mach. Learn. Res. 14 (1) (2013)
Evan Hanusa, David W. Krout, Maya R. Gupta
Contact clustering and fusion for preprocessing multistatic active sonar data. FUSION (2013)
Hyrum S. Anderson, Nathan Parrish, Kristi Tsukida, Maya R. Gupta
Reliable early classification of time series. ICASSP (2012)
Bela A. Frigyik, Maya R. Gupta
Bounds on the Bayes Error Given Moments. IEEE Trans. Inf. Theory 58 (6) (2012)
Nathan Parrish, Maya R. Gupta
Dimensionality Reduction by Local Discriminative Gaussians CoRR (2012)
Eric K. Garcia, Raman Arora, Maya R. Gupta
Optimized Regression for Efficient Function Evaluation. IEEE Trans. Image Process. 21 (9) (2012)
Hyrum S. Anderson, Maya R. Gupta, Jon Yngve Hardeberg
Subjective evaluations of example-based, total variation, and joint regularization for image processing. Computational Imaging (2012)
Sergey Feldman, Maya R. Gupta, Bela A. Frigyik
Multi-Task Averaging. NIPS (2012)
Nathan Parrish, Maya R. Gupta
Dimensionality Reduction by Local Discriminative Gaussians. ICML (2012)
Luca Cazzanti, Sergey Feldman, Maya R. Gupta, Michael Gabbay
Multi-task Regularization of Generative Similarity Models. SIMBAD (2011)
Evan Hanusa, David W. Krout, Maya R. Gupta
Clutter rejection by clustering likelihood-based similarities. FUSION (2011)
Raman Arora, Maya R. Gupta
Minimizing bearing bias in tracking by de-coupled rotation and translation estimates. FUSION (2011)
Hyrum S. Anderson, Maya R. Gupta, Eric Swanson, Kevin G. Jamieson
Channel-Robust Classifiers. IEEE Trans. Signal Process. 59 (4) (2011)
Raman Arora, Maya R. Gupta, Amol Kapila, Maryam Fazel
Clustering by Left-Stochastic Matrix Factorization. ICML (2011)
Bela A. Frigyik, Maya R. Gupta
Bounds on the Maximum Bayes Error Given Moments CoRR (2011)
Nathan Parrish, Hyrum S. Anderson, Maya R. Gupta
Robust sequential classification of tracks. FUSION (2010)
Eric K. Garcia, Maya R. Gupta
Optimized Construction of ICC Profiles by Lattice Regression. Color Imaging Conference (2010)
Maya R. Gupta, Yihua Chen
Theory and Use of the EM Algorithm. Found. Trends Signal Process. 4 (3) (2010)
Peter J. Sadowski, Luca Cazzanti, Maya R. Gupta
Bayesian and pairwise local similarity discriminant analysis. CIP (2010)
Eric K. Garcia, Sergey Feldman, Maya R. Gupta, Santosh Srivastava
Completely Lazy Learning. IEEE Trans. Knowl. Data Eng. 22 (9) (2010)
Bela A. Frigyik, Maya R. Gupta, Yihua Chen
Shadow Dirichlet for Restricted Probability Modeling. NIPS (2010)
Kevin G. Jamieson, Maya R. Gupta, Eric Swanson, Hyrum S. Anderson
Training a support vector machine to classify signals in a real environment given clean training data. ICASSP (2010)
Maya R. Gupta, Santosh Srivastava
Parametric Bayesian Estimation of Differential Entropy and Relative Entropy. Entropy 12 (4) (2010)
Evan Hanusa, David W. Krout, Maya R. Gupta
Estimation of position from multistatic Doppler measurements. FUSION (2010)
Jill K. Nelson, Maya R. Gupta, Jaime E. Almodovar, William H. Mortensen
A Quasi EM Method for Estimating Multiple Transmitter Locations. IEEE Signal Process. Lett. 16 (5) (2009)
Yihua Chen, Maya R. Gupta
Fusing similarities and kernels for classification. FUSION (2009)
Luca Cazzanti, Maya R. Gupta
Regularizing the Local Similarity Discriminant Analysis Classifier. ICMLA (2009)
Eric K. Garcia, Maya R. Gupta
Building Accurate and Smooth ICC Profiles by Lattice Regression. Color Imaging Conference (2009)
Marius A. Marin, Sergey Feldman, Mari Ostendorf, Maya R. Gupta
Filtering web text to match target genres. ICASSP (2009)
Eric K. Garcia, Maya R. Gupta
Lattice Regression. NIPS (2009)
Kevin G. Jamieson, Maya R. Gupta, David W. Krout
Sequential Bayesian estimation of the probability of detection for tracking. FUSION (2009)
Hyrum S. Anderson, Maya R. Gupta
Joint deconvolution and imaging. Computational Imaging (2009)
Yihua Chen, Eric K. Garcia, Maya R. Gupta, Ali Rahimi, Luca Cazzanti
Similarity-based Classification: Concepts and Algorithms. J. Mach. Learn. Res. 10 (2009)
Yihua Chen, Maya R. Gupta, Benjamin Recht
Learning kernels from indefinite similarities. ICML (2009)
Sergey Feldman, Marius A. Marin, Mari Ostendorf, Maya R. Gupta
Part-of-speech histograms for genre classification of text. ICASSP (2009)
Luca Cazzanti, Maya R. Gupta, Santosh Srivastava
Fusing similarities and Euclidean features with generative classifiers. FUSION (2009)
Megan Hazen, Maya R. Gupta
Gradient estimation in global optimization algorithms. IEEE Congress on Evolutionary Computation (2009)
Jill K. Nelson, Jaime E. Almodovar, Maya R. Gupta, William H. Mortensen
Estimating multiple transmitter locations from power measurements at multiple receivers. ICASSP (2009)
Bela A. Frigyik, Santosh Srivastava, Maya R. Gupta
Functional Bregman divergence. ISIT (2008)
Luca Cazzanti, Maya R. Gupta, Anjali J. Koppal
Generative models for similarity-based classification. Pattern Recognit. 41 (7) (2008)
Nasiha Hrustemovic, Maya R. Gupta
Multiresolutional regularization of local linear regression over adaptive neighborhoods for color management. ICIP (2008)
Deirdre B. O'Brien, Maya R. Gupta, Robert M. Gray
Cost-sensitive multi-class classification from probability estimates. ICML (2008)
Bela A. Frigyik, Santosh Srivastava, Maya R. Gupta
Functional Bregman Divergence and Bayesian Estimation of Distributions. IEEE Trans. Inf. Theory 54 (11) (2008)
Santosh Srivastava, Maya R. Gupta
Bayesian estimation of the entropy of the multivariate Gaussian. ISIT (2008)
Maya R. Gupta, Eric K. Garcia, E. Chin
Adaptive Local Linear Regression With Application to Printer Color Management. IEEE Trans. Image Process. 17 (6) (2008)
Maya R. Gupta, Eric K. Garcia, Andrey Stroilov
Learning custom color transformations with adaptive neighborhoods. J. Electronic Imaging 17 (3) (2008)
Luca Cazzanti, Maya R. Gupta
Local similarity discriminant analysis. ICML (2007)
Jill K. Nelson, Maya R. Gupta
An EM Technique for Multiple Transmitter Localization. CISS (2007)
Nathaniel P. Jacobson, Maya R. Gupta, Jeff B. Cole
Linear Fusion of Image Sets for Display. IEEE Trans. Geosci. Remote. Sens. 45 (10) (2007)
Maya R. Gupta, Jayson Bowen
Ranked dither for robust color printing. Color Imaging: Processing, Hardcopy, and Applications (2007)
Nathaniel P. Jacobson, Maya R. Gupta
SNR-Adaptive Linear Fusion of Hyperspectral Images for Color Display. ICIP (3) (2007)

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Publication Activity (10 Years)

Top Topics

Top Venues

Maya R. Gupta