Block Color Quantization: A New Method for Color Halftoning. - Maya R. Gupta; Michael J. Gormish; David G. Stork (ICIP, 2000)

Color conversions using maximum entropy estimation. - Maya R. Gupta; Robert M. Gray (ICIP (1), 2001)

Two-stage color palettization for error diffusion. - Niloy J. Mitra; Maya R. Gupta (Human Vision and Electronic Imaging, 2002)

Halftoning on the wavelet domain. - Maya R. Gupta (Color Imaging: Processing, Hardcopy, and Applications, 2003)

Analysis and classification of internal pipeline images. - Deirdre B. O'Brien; Maya R. Gupta; Robert M. Gray; Jon Kristian Hagene (ICIP (3), 2003)

Inverting color transforms. - Maya R. Gupta (Computational Imaging, 2004)

A principle of minimum expected risk. - Maya R. Gupta (ISIT, 2004)

Simulating the effect of illumination using color transformations. - Maya R. Gupta; Stephen Upton; Jayson Bowen (Computational Imaging, 2005)

Segmenting for Wavelet Compression. - Maya R. Gupta; Andrey Stroilov (DCC, 2005)

Custom color enhancements by statistical learning. - Maya R. Gupta (ICIP (3), 2005)

Design goals and solutions for display of hyperspectral images. - Nathaniel P. Jacobson; Maya R. Gupta (IEEE Trans. Geosci. Remote. Sens., 2005)

A Multiresolutional Estimated Gradient Architecture for Global Optimization. - Megan Hazen; Maya R. Gupta (IEEE Congress on Evolutionary Computation, 2006)

Wavelet Principal Component Analysis and its Application to Hyperspectral Images. - Maya R. Gupta; Nathaniel P. Jacobson (ICIP, 2006)

Information-theoretic and Set-theoretic Similarity. - Luca Cazzanti; Maya R. Gupta (ISIT, 2006)

Distribution-based Bayesian Minimum Expected Risk for Discriminant Analysis. - Santosh Srivastava; Maya R. Gupta (ISIT, 2006)

Nonparametric Supervised Learning by Linear Interpolation with Maximum Entropy. - Maya R. Gupta; Robert M. Gray; Richard A. Olshen (IEEE Trans. Pattern Anal. Mach. Intell., 2006)

An EM Technique for Multiple Transmitter Localization. - Jill K. Nelson; Maya R. Gupta (CISS, 2007)

Ranked dither for robust color printing. - Maya R. Gupta; Jayson Bowen (Color Imaging: Processing, Hardcopy, and Applications, 2007)

Joint Deconvolution and Classification for Signals with Multipath. - Maya R. Gupta; Hyrum S. Anderson; Yihua Chen 003 (ICASSP (3), 2007)

Beamforming Alternatives for Multi-Channel Transient Acoustic Event Classification. - Brandon Smith; Les E. Atlas; Maya R. Gupta (ICASSP (2), 2007)

Color Management of Printers by Regression over Enclosing Neighborhoods. - Erika M. Chin; Eric K. Garcia; Maya R. Gupta (ICIP (2), 2007)

SNR-Adaptive Linear Fusion of Hyperspectral Images for Color Display. - Nathaniel P. Jacobson; Maya R. Gupta (ICIP (3), 2007)

Local similarity discriminant analysis. - Luca Cazzanti; Maya R. Gupta (ICML, 2007)

Maximum Entropy Generative Models for Similarity-based Learning. - Maya R. Gupta; Luca Cazzanti; Anjali J. Koppal (ISIT, 2007)

Bayesian Quadratic Discriminant Analysis. - Santosh Srivastava; Maya R. Gupta; Bela A. Frigyik (J. Mach. Learn. Res., 2007)

OCR binarization and image pre-processing for searching historical documents. - Maya R. Gupta; Nathaniel P. Jacobson; Eric K. Garcia (Pattern Recognit., 2007)

Linear Fusion of Image Sets for Display. - Nathaniel P. Jacobson; Maya R. Gupta; Jeff B. Cole (IEEE Trans. Geosci. Remote. Sens., 2007)

Multiresolutional regularization of local linear regression over adaptive neighborhoods for color management. - Nasiha Hrustemovic; Maya R. Gupta (ICIP, 2008)

Cost-sensitive multi-class classification from probability estimates. - Deirdre B. O'Brien; Maya R. Gupta; Robert M. Gray (ICML, 2008)

Functional Bregman divergence. - Bela A. Frigyik; Santosh Srivastava; Maya R. Gupta (ISIT, 2008)

Bayesian estimation of the entropy of the multivariate Gaussian. - Santosh Srivastava; Maya R. Gupta (ISIT, 2008)

Learning custom color transformations with adaptive neighborhoods. - Maya R. Gupta; Eric K. Garcia; Andrey Stroilov (J. Electronic Imaging, 2008)

Generative models for similarity-based classification. - Luca Cazzanti; Maya R. Gupta; Anjali J. Koppal (Pattern Recognit., 2008)

Adaptive Local Linear Regression With Application to Printer Color Management. - Maya R. Gupta; Eric K. Garcia; E. Chin (IEEE Trans. Image Process., 2008)

Functional Bregman Divergence and Bayesian Estimation of Distributions. - Bela A. Frigyik; Santosh Srivastava; Maya R. Gupta (IEEE Trans. Inf. Theory, 2008)

Gradient estimation in global optimization algorithms. - Megan Hazen; Maya R. Gupta (IEEE Congress on Evolutionary Computation, 2009)

Joint deconvolution and imaging. - Hyrum S. Anderson; Maya R. Gupta (Computational Imaging, 2009)

Fusing similarities and Euclidean features with generative classifiers. - Luca Cazzanti; Maya R. Gupta; Santosh Srivastava (FUSION, 2009)

Fusing similarities and kernels for classification. - Yihua Chen 003; Maya R. Gupta (FUSION, 2009)

Sequential Bayesian estimation of the probability of detection for tracking. - Kevin G. Jamieson; Maya R. Gupta; David W. Krout (FUSION, 2009)

Part-of-speech histograms for genre classification of text. - Sergey Feldman; Marius A. Marin; Mari Ostendorf; Maya R. Gupta (ICASSP, 2009)

Filtering web text to match target genres. - Marius A. Marin; Sergey Feldman; Mari Ostendorf; Maya R. Gupta (ICASSP, 2009)

Estimating multiple transmitter locations from power measurements at multiple receivers. - Jill K. Nelson; Jaime E. Almodovar; Maya R. Gupta; William H. Mortensen (ICASSP, 2009)

Learning kernels from indefinite similarities. - Yihua Chen 003; Maya R. Gupta; Benjamin Recht (ICML, 2009)

Regularizing the Local Similarity Discriminant Analysis Classifier. - Luca Cazzanti; Maya R. Gupta (ICMLA, 2009)

Building Accurate and Smooth ICC Profiles by Lattice Regression. - Eric K. Garcia; Maya R. Gupta (CIC, 2009)

Lattice Regression. - Eric K. Garcia; Maya R. Gupta (NIPS, 2009)

Similarity-based Classification: Concepts and Algorithms. - Yihua Chen 003; Eric K. Garcia; Maya R. Gupta; Ali Rahimi; Luca Cazzanti (J. Mach. Learn. Res., 2009)

A Quasi EM Method for Estimating Multiple Transmitter Locations. - Jill K. Nelson; Maya R. Gupta; Jaime E. Almodovar; William H. Mortensen (IEEE Signal Process. Lett., 2009)

Bayesian and pairwise local similarity discriminant analysis. - Peter J. Sadowski; Luca Cazzanti; Maya R. Gupta (CIP, 2010)

Estimation of position from multistatic Doppler measurements. - Evan Hanusa; David W. Krout; Maya R. Gupta (FUSION, 2010)

Robust sequential classification of tracks. - Nathan Parrish; Hyrum S. Anderson; Maya R. Gupta (FUSION, 2010)

Training a support vector machine to classify signals in a real environment given clean training data. - Kevin G. Jamieson; Maya R. Gupta; Eric Swanson; Hyrum S. Anderson (ICASSP, 2010)

Optimized Construction of ICC Profiles by Lattice Regression. - Eric K. Garcia; Maya R. Gupta (CIC, 2010)

Shadow Dirichlet for Restricted Probability Modeling. - Bela A. Frigyik; Maya R. Gupta; Yihua Chen 003 (NIPS, 2010)

Parametric Bayesian Estimation of Differential Entropy and Relative Entropy. - Maya R. Gupta; Santosh Srivastava (Entropy, 2010)

Theory and Use of the EM Algorithm. - Maya R. Gupta; Yihua Chen 003 (Found. Trends Signal Process., 2010)

Completely Lazy Learning. - Eric K. Garcia; Sergey Feldman; Maya R. Gupta; Santosh Srivastava (IEEE Trans. Knowl. Data Eng., 2010)

Minimizing bearing bias in tracking by de-coupled rotation and translation estimates. - Raman Arora; Maya R. Gupta (FUSION, 2011)

Clutter rejection by clustering likelihood-based similarities. - Evan Hanusa; David W. Krout; Maya R. Gupta (FUSION, 2011)

Clustering by Left-Stochastic Matrix Factorization. - Raman Arora; Maya R. Gupta; Amol Kapila; Maryam Fazel (ICML, 2011)

Multi-task Regularization of Generative Similarity Models. - Luca Cazzanti; Sergey Feldman; Maya R. Gupta; Michael Gabbay 002 (SIMBAD, 2011)

Bounds on the Maximum Bayes Error Given Moments - Bela A. Frigyik; Maya R. Gupta (CoRR, 2011)

Channel-Robust Classifiers. - Hyrum S. Anderson; Maya R. Gupta; Eric Swanson; Kevin G. Jamieson (IEEE Trans. Signal Process., 2011)

Subjective evaluations of example-based, total variation, and joint regularization for image processing. - Hyrum S. Anderson; Maya R. Gupta; Jon Yngve Hardeberg (Computational Imaging, 2012)

Reliable early classification of time series. - Hyrum S. Anderson; Nathan Parrish; Kristi Tsukida; Maya R. Gupta (ICASSP, 2012)

Dimensionality Reduction by Local Discriminative Gaussians. - Nathan Parrish; Maya R. Gupta (ICML, 2012)

Multi-Task Averaging. - Sergey Feldman; Maya R. Gupta; Bela A. Frigyik (NIPS, 2012)

Optimized Regression for Efficient Function Evaluation. - Eric K. Garcia; Raman Arora; Maya R. Gupta (IEEE Trans. Image Process., 2012)

Bounds on the Bayes Error Given Moments. - Bela A. Frigyik; Maya R. Gupta (IEEE Trans. Inf. Theory, 2012)

Contact clustering and fusion for preprocessing multistatic active sonar data. - Evan Hanusa; David W. Krout; Maya R. Gupta (FUSION, 2013)

Similarity-based clustering by left-stochastic matrix factorization. - Raman Arora; Maya R. Gupta; Amol Kapila; Maryam Fazel (J. Mach. Learn. Res., 2013)

Classifying with confidence from incomplete information. - Nathan Parrish; Hyrum S. Anderson; Maya R. Gupta; Dun-Yu Hsiao (J. Mach. Learn. Res., 2013)

Revisiting Stein's paradox: multi-task averaging. - Sergey Feldman; Maya R. Gupta; Bela A. Frigyik (J. Mach. Learn. Res., 2014)

Training highly multiclass classifiers. - Maya R. Gupta; Samy Bengio; Jason Weston (J. Mach. Learn. Res., 2014)

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)

Launch and Iterate: Reducing Prediction Churn. - Mahdi Milani Fard; Quentin Cormier; Kevin Robert Canini; Maya R. Gupta (NIPS, 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)

Deep Lattice Networks and Partial Monotonic Functions. - Seungil You; David Ding; Kevin Robert Canini; Jan Pfeifer; Maya R. Gupta (NIPS, 2017)

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)

To Trust Or Not To Trust A Classifier. - Heinrich Jiang; Been Kim; Melody Y. Guan; Maya R. Gupta (NeurIPS, 2018)

Metric-Optimized Example Weights. - Sen Zhao; Mahdi Milani Fard; Maya R. Gupta (CoRR, 2018)

To Trust Or Not To Trust A Classifier. - Heinrich Jiang; Been Kim; Maya R. Gupta (CoRR, 2018)

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

Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization. - Serena Lutong Wang; Maya R. Gupta; Seungil You (CoRR, 2018)

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

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)

Metric-Optimized Example Weights. - Sen Zhao; Mahdi Milani Fard; Harikrishna Narasimhan; Maya R. Gupta (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)

Minimum-Margin Active Learning. - Heinrich Jiang; Maya R. Gupta (CoRR, 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)

Deontological Ethics By Monotonicity Shape Constraints. - Serena Lutong Wang; Maya R. Gupta (AISTATS, 2020)

Deep k-NN for Noisy Labels. - Dara Bahri; Heinrich Jiang; Maya R. Gupta (ICML, 2020)

Multidimensional Shape Constraints. - Maya R. Gupta; Erez Louidor; Oleksandr Mangylov; Nobu Morioka; Taman Narayan; Sen Zhao (ICML, 2020)

Optimizing Black-box Metrics with Adaptive Surrogates. - Qijia Jiang; Olaoluwa Adigun; Harikrishna Narasimhan; Mahdi Milani Fard; Maya R. Gupta (ICML, 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)

Bootstrapping for Batch Active Sampling. - Heinrich Jiang; Maya R. Gupta (KDD, 2021)

Regularization Strategies for Quantile Regression. - Taman Narayan; Serena Lutong Wang; Kevin Robert Canini; Maya R. Gupta (CoRR, 2021)

Quit When You Can: Efficient Evaluation of Ensembles by Optimized Ordering. - Serena Lutong Wang; Maya R. Gupta; Seungil You (ACM J. Emerg. Technol. Comput. Syst., 2021)

Fast Linear Interpolation. - Nathan Zhang; Kevin Robert Canini; Sean Silva; Maya R. Gupta (ACM J. Emerg. Technol. Comput. Syst., 2021)

Global Optimization Networks. - Sen Zhao; Erez Louidor; Maya R. Gupta (ICML, 2022)

Global Optimization Networks. - Sen Zhao; Erez Louidor; Oleksandr Mangylov; Maya R. Gupta (CoRR, 2022)

Expected Pinball Loss For Quantile Regression And Inverse CDF Estimation. - Taman Narayan; Serena Lutong Wang; Kevin Robert Canini; Maya R. Gupta (Trans. Mach. Learn. Res., 2024)