Research Papers (see also Tutorials/Books)

Expected Pinball Loss For Quantile Regression and Inverse PDF Estimation,  Taman Narayan, Serena Wang, Kevin Canini, and Maya R. Gupta. TMLR 2024 (to appear).  Data for the Puzzle Club experiment.

Global Optimization Networks. Sen Zhao, Erez Louidor, and Maya R. Gupta. ICML 2022.

Bootstrapping for Batch Active Sampling. Heinrich Jiang, and Maya R. Gupta. KDD 2021.

Understanding Memory B Cell Selection. Stephen Lindsley,  Maya R. Gupta, Cooper Stansbury, and Indika Rajapakse.  Journal of Theoretical Biology 2021.

Quit When You Can: Efficient Evaluation of Ensembles by Optimized Ordering.  Serena Wang, Seungil You, Maya R. Gupta. ACM Journal on Emerging Technologies in Computing Systems 2021.

Fast Linear Interpolation.   Nathan Zhang, Kevin Canini, Sean Silva, Maya R. Gupta. ACM Journal on Emerging Technologies in Computing Systems 2021.

Robust Optimization for Fairness with Noisy Protected Groups.   Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, and Michael Jordan.   NeurIPS 2020.

Multidimensional Shape Constraints.   Maya R. Gupta,  Erez Louidor, Olexander Mangylov, Nobu Morioka, Taman Narayan, Sen Zhao. ICML 2020.

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

Optimizing Black-box Metrics With Adaptive Surrogates.   Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan, Mahdi Milani Fard, Maya R. Gupta. ICML 2020.

Deontological Ethics By Monotonicity Shape Constraints.   Serena Wang,  Maya R. Gupta. AIStats  2020.

Pairwise Fairness for Ranking and Regression.   Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Serena Wang. AAAI 2020.

Optimizing Generalized Rate Metrics through Game Equilibrium.   Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, NeurIPS 2019.

2019.

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

Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals   Andrew Cotter, Heinrich Jiang, Serena Wang, Taman Narayan, Maya Gupta, Seungil You, Karthik Sridharan, JMLR (to appear)  2019.

Metric-Optimized Example Weights   Sen Zhao, Mahdi Milani Fard, Maya Gupta, ICML  2019.

Shape Constraints for Set Functions Andrew Cotter, Maya Gupta, Heinrich Jiang, Erez Louidor, James Muller, Taman Narayan, Serena Wang, Tao Zhu, ICML  2019. 

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

Proxy Fairness   Maya Gupta, Andrew Cotter, Mahdi Milani Fard, Serena Wang (in review)  2019.

Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization   Serena Wang, Maya Gupta, Seungil You (in review)  2019.

Monotonicity Shape Constraints for Binary Classifiers Daniel Kraft, Maya Gupta (in review) 2019.

Diminishing Returns Shape Constraints for Interpretability and Regularization   Maya Gupta, Dara Bahri, Andrew Cotter, and Kevin Canini. NeurIPS  2018.

To Trust Or Not To Trust A Classifier   Heinrich Jiang, Been Kim, Melody Guan, Maya Gupta. NeurIPS  2018.

Constrained Interacting Submodular Groupings   Andrew Cotter, Mahdi Milani Fard, Seungil You, Maya Gupta, Jeff Bilmes. ICML  2018.

Deep Lattice Networks for Learning Partial Monotonic Functions   Seungil You, Kevin Canini, David Ding, Jan Pfeifer, and Maya Gupta. NIPS  2017.

Satisfying Real-world Goals with Dataset Constraints   Gabe Goh, Andrew Cotter, Maya Gupta, and Michael Friedlander. NIPS  2016.

Launch and Iterate: Reducing Prediction Churn   Quentin Cormier, Mahdi Milani Fard, Kevin Canini, and Maya Gupta. NIPS  2016.

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

A Light Touch for Heavily Constrained SGD   Andrew Cotter, Maya Gupta, and Jan Pfeifer. COLT  2016.

Monotonic Calibrated Interpolated Look-Up Tables, Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voedvovski, Kevin Canini, Alexander Mangylov, and Wojtek Moczydlowski. Journal Machine Learning Research (JMLR) 2016.

Revisiting Stein’s Paradox: Multi-Task Averaging, Sergey Feldman, Maya Gupta, and Bela Frigyik, Journal Machine Learning Research (JMLR) 2014. Research-grade Matlab Code and Datasets

Training Highly Multi-class Classifiers, Maya Gupta, Samy Bengio, and Jason Weston, Journal Machine Learning Research (JMLR), vol. 15, 2014.

Classifying with Confidence from Incomplete Information, Nathan Parrish, Hyrum Anderson, Maya Gupta, and Dun Yu Hsaio, Journal Machine Learning Research (JMLR) 2013. Code and Data

Similarity-based Clustering by Left-Stochastic Matrix Factorization, Raman Arora, Maya Gupta, Amol Kapila, and Maryam Fazel, Journal Machine Learning Research (JMLR), vol. 14, 2013.

Bounds on the Bayes Error Given Moments, Bela A. Frigyik and Maya Gupta, IEEE Trans. on Information Theory, vol. 58, no. 6, 2012.

Dimensionality Reduction by Local Discriminative Gaussians, Nathan Parrish and Maya Gupta, ICML 2012. Research-grade code

Filtering Tandem Mass Spectra for Quality, Sergey Feldman, Barbara Frewen, Michael MacCoss, and Maya Gupta, University of Washington Dept. of Electrical Engineering Technical Report UWEETR-2012-0001, 2012. Code and Data

Multi-task Averaging, Sergey Feldman, Maya Gupta, and Bela A. Frigyik, Advances in Neural Information Processing (NIPS), 2012. See also the journal paper version of this paper. 

Optimized Regression for Efficient Function Evaluation, Eric K. Garcia, Raman Arora, and Maya Gupta, IEEE Trans. Image Processing, 2012. Code

Reliable Early Classification of Time Series, Hyrum S. Anderson, Nathan Parrish, Kristi Tsukida, and Maya Gupta, Proc. IEEE ICASSP , 2012. See also the journal paper version of this work.

Subjective Evaluations of Example-based, Total Variation, and Joint Regularization for Image Processing, Hyrum S. Anderson, Maya Gupta, and Jon Y. Hardeberg, SPIE Conf. on Computational Imaging, 2012. Research-grade Code and Data

Bayesian Transfer Learning for Noisy Channels, Nathan Parrish and Maya Gupta, IEEE Statistical Signal Processing Workshop, 2011.

Channel-Robust Classifiers, Hyrum S. Anderson, Maya Gupta, Eric Swanson, and Kevin Jamieson, IEEE Trans. on Signal Processing , vol. 59, no. 4, 1421-1434, 2011.

Clustering by Left-Stochastic Matrix Factorization, Raman Arora, Maya Gupta, Amol Kapila, and Maryam Fazel, Proc. Intl. Conf. Machine Learning (ICML), 2011. See also the journal paper version of this work. Code

Clutter Rejection by Clustering Likelihood-Based Similarities, Evan Hanusa, David Krout, and Maya Gupta, Proc. FUSION, 2011.

Color Science and Engineering for the Display of Remote Sensing Images,  Maya Gupta and Nasiha Hrustemovic, chapter 5, "Optical Remote Sensing", 65-79, 2011. Code and Images Used

Expected Kernel for Missing Features in Support Vector Machines, Hyrum S. Anderson and Maya Gupta, IEEE Statistical Signal Processing Workshop, 2011.

How to Analyze Paired Comparison Data, Kristi Tsukida and Maya Gupta, University of Washington Tech Report # UW-EE-2011-0004, 2011.

Minimizing Bearing Bias in Tracking by De-coupled Rotation and Translation Estimates, Raman Arora and Maya Gupta, Proc. FUSION, 2011.

Robust Classification of Signal Estimates Given a Channel Model, Nathan Parrish, Maya Gupta, and Hyrum S. Anderson, IEEE Statistical Signal Processing Workshop, 2011.

Bayesian and Pairwise Local Similarity Discriminant Analysis, Peter Sadowski, Luca Cazzanti and Maya Gupta, Proc. IEEE Conf. on Cognitive Information Processing (invited paper), 2010.

Completely Lazy Learning, Eric K. Garcia, Sergey Feldman, Maya Gupta and Santosh Srivastava, IEEE Trans. on Knowledge and Data Engineering, vol. 22, no. 9, 1274 - 1285, 2010. Code and Data

Estimation of Position from Multistatic Doppler Measurements, Evan Hanusa, David Krout, and Maya Gupta, Proc. FUSION , 2010.

Introduction to the Dirichlet Distribution and Related Processes, Bela A. Frigyik, Amol Kapila, and Maya Gupta, Tech Report UWEETR-2010-0006, 2010.

Optimized Construction of ICC Profiles by Lattice Regression, Eric K. Garcia and Maya Gupta, Proc. Color Imaging Conference, 2010.

Parametric Bayesian Estimation of Differential and Relative Entropy , Maya Gupta and Santosh Srivastava, Entropy , vol. 12, no. 4, 818-843, 2010.

Robust Sequential Classification of Tracks, Nathan Parrish, Hyrum S. Anderson, and Maya Gupta, Proc. FUSION , 2010.

Shadow Dirichlet for Restricted Probability Modeling, Bela A. Frigyik, Maya Gupta, and Y. Chen, Advances in Neural Information Processing Systems (NIPS) , 2010.

Theory and Use of the EM Method, Maya Gupta and Yihua (James) Chen, Foundations and Trends in Signal Processing, NOW Publishers, vol. 4, no. 3, 223-296, 2010.

Thesis: Joint Deconvolution and Classification: Classifiers for Dataset Shift Induced by Linear Systems, Hyrum S. Anderson, Univ. of Washington PhD Thesis (Advisor: Maya Gupta), 2010.

Thesis: Regression Strategies for Low-Dimensional Problems with Application to Color Management, Eric K. Garcia, Univ. of Washington PhD Thesis, 2010.

Thesis: Strategies for Similarity-based Learning, Yihua Chen (James Chen), Univ. of Washington PhD Thesis, 2010.

Training a support vector machine to classify signals in a real environment given clean training data, Kevin Jamieson, Maya Gupta, Eric Swanson and Hyrum S. Anderson, Proc. IEEE ICASSP, 2010.

A Quasi EM Method for Estimating Multiple Transmitter Locations, Jill K. Nelson, Jaime Almodovar, Maya Gupta and Will Mortensen, IEEE Signal Processing Letters, 354-357, 2009.

Building Accurate and Smooth ICC Profiles by Lattice Regression, Eric K. Garcia and Maya Gupta, Proc. Color Imaging Conference, 101-106, 2009.

Classifying linear system outputs by robust local Bayesian quadratic discriminant analysis on linear estimators, Hyrum S. Anderson and Maya Gupta, IEEE Workshop on Statistical Signal Processing, 2009.

Estimating Multiple Transmitter Locations from Power Measurements at Multiple Receivers, Jill K. Nelson, Jaime Almodovar, Maya Gupta, William Mortensen, Proc. ICASSP, 2009.

Filtering Web Text to Match Target Genres, Alex Marin, Sergey Feldman, Mari Ostendorf, and Maya Gupta, Proc. ICASSP , 2009.

Fusing Similarities and Euclidean Features with a Generative Classifier, Luca Cazzanti, Maya R Gupta, and Santosh Srivastava, Proc. IEEE Conf. on Information Fusion (FUSION) , 2009.

Fusing Similarities and Kernels for Classification, Yihua Chen and Maya Gupta, Proc. IEEE Conf. on Information Fusion (FUSION), 2009.

Gradient Estimation in Global Optimization Algorithms, Megan Hazen and Maya Gupta, Congress on Evolutionary Computation, 1841 - 1848, 2009.

Joint Deconvolution and Imaging, Hyrum Anderson and Maya Gupta, Proc. SPIE Conf. on Computational Imaging, 2009.

Lattice Regression, Eric K. Garcia and Maya Gupta, NIPS (Advances in Neural Information Processing Systems), 2009. Journal Paper Version and Code

Learning kernels from indefinite similarities, Yihua Chen, Maya Gupta, and Ben Recht, ICML (Intl. Conf. on Machine Learning) , 2009.

Part-of-Speech Histogram Features for Genre Classification of Text, Sergey Feldman, Marius Marin, Mari Ostendorf, and Maya Gupta , Proc. IEEE ICASSP , 2009.

Regularizing the Local Similarity Discriminant Analysis Classifier, Luca Cazzanti and Maya Gupta, Proc. ICMLA, 2009.

Sequential Bayesian Estimation of the Probability of Detection for Tracking, Kevin Jamieson, Maya R Gupta, and David Krout, Proc. IEEE Conf. on Information Fusion (FUSION), 2009.

Similarity-based Classification: Concepts and Algorithms, Yihua Chen, Eric K. Garcia, Maya Gupta, Luca Cazzanti, and Ali Rahimi, Journal of Machine Learning Research (JMLR), 2009.

Weighted Nearest-neighbor Learning and First-order Error, Maya Gupta and William Mortensen, Proc. Intl. Conf. on Frontiers of Interface Between Statistics and Science (invited paper), 2009.

Adaptive Local Linear Regression with Application to Printer Color Management, Maya Gupta, Eric K. Garcia, and Erika Chin, IEEE Trans. on Image Processing , vol. 17, no. 6, 936-945, 2008.

An Introduction to Functional Derivatives, Bela Frigyik, Santosh Srivastava, and Maya Gupta, UW Dept. of Electrical Engineering Technical Report 2008-0001, 2008.

Bayesian estimation of the entropy of the multivariate Gaussian (link goes to journal paper version), Santosh Srivastava and Maya Gupta, Proc. IEEE Intl. Symp. on Information Theory, 2008.

Cost-sensitive multi-class classification from probability estimates, D. B. OBrien and M. R. Gupta and R. M. Gray, Intl. Conf. Machine Learning (ICML), 2008.

Functional Bregman Divergence and Bayesian Estimation of Distributions, Bela A. Frigyik, Santosh Srivastava, and Maya Gupta, IEEE Trans. on Information Theory, vol. 54, no. 11, 5130-5139, 2008.

Generative Models for Similarity-based Classification, Luca Cazzanti, Maya Gupta, and Anjali Koppal, Pattern Recognition , vol. 41, no. 7, 2289-2297, 2008.

Joint Deconvolution and Classification with Applications to Passive Acoustic Underwater Multipath, Hyrum Anderson and Maya Gupta, Journal of the Acoustical Society of America , vol. 124, no. 5, 2973-2983, 2008.

Learning custom color transformations with adaptive neighborhoods, Maya Gupta, Eric K. Garcia, and Andrey Stroilov, Journal of Electronic Imaging (with Open Access), vol. 17, no. 3, 2008. (Experimental Data)

Multiresolutional Regularization of Local Linear Regression Over Adaptive Neighborhoods for Color Management, N. Hrustemovic and M. R. Gupta, Proc. of the Intl. Conf. on Image Processing , 2008.

QDA classification of multipath-corrupted observations using uncorrupted training features, Hyrum Anderson and Maya Gupta, Proc. Intl. Symp. on Underwater Reverberation and Clutter, 2008.

Ranked Dither for High-Quality Robust Printing, M. R. Gupta and J. J. Bowen, Journal of the Optical Society of America, A , vol. 25, no. 6, 1454-1458, 2008. Ranked Dither Code

Thesis: Search Strategies for Global Optimization, Megan Hazen, Univ. of Washington PhD Thesis (Advisor: Maya Gupta), 2008.

An EM technique for multiple transmitter localization, Jill K. Nelson and Maya Gupta, 41st Conf. on Information Science and Systems, 610-615, 2007.

Bayesian Quadratic Discriminant Analysis, Santosh Srivastava, Maya Gupta, and Bela Frigyik, Journal of Machine Learning Research (JMLR), vol. 8, pp. 1277-1305, 2007. Complete Simulation Code

Beamforming alternatives for multi-channel transient acoustic event classification, Brandon Smith, Les Atlas, and Maya Gupta, Proc. of the IEEE ICASSP Conf., 2007.

CIELab color values of in vivo normal and grasped porcine liver, Smita De, Aylon Dagan, Phil Roan, Jacob Rosen, Mika Sinanan, Maya Gupta, and Blake Hannaford, Proc. of Medicine Meets Virtual Reality (MMVR), 2007.

Color Management of Printers by Regression over Enclosing Neighborhoods (link goes to journal paper version of this work), Erika M. Chin, Eric K. Garcia and Maya Gupta , IEEE Intl. Conf. on Image Processing, 2007.

Gamut Expansion for Video and Image Sets, Hyrum Anderson, Eric K. Garcia, and Maya Gupta, Computational Color Imaging Workshop, 2007.

Joint deconvolution and classification for signals with multipath, Maya Gupta, Hyrum S. Anderson, and Yihua Chen, Proc. of the IEEE ICASSP Conf. , 2007.

Linear Fusion of Image Sets for Display, N. P. Jacobson and M. R. Gupta and J. B. Cole, IEEE Trans. on Geosciences and Remote Sensing , vol. 45, no. 10, 3277-3288, 2007. Code for basis functions, and passive radar videos

Local Similarity Discriminant Analysis, Luca Cazzanti and Maya Gupta, Intl. Conf. Machine Learning (ICML), 2007.

Maximum likelihood signal classification using second-order blind deconvolution probability model, Maya Gupta and Hyrum S. Anderson, Proc. of the IEEE Statistical Signal Processing Workshop, 2007.

OCR binarization and image pre-processing for searching historical documents, Maya Gupta, Nathaniel P. Jacobson, and Eric K. Garcia., Pattern Recognition, vol. 40, no. 2, 389-397, 2007.

Ranked Dither for Robust Color Printing, Maya Gupta and Jayson Bowen, Proc. of IS&T Color Imaging XII: Processing, Hardcopy, and Applications, 2007. Ranked Dither Code

SNR-adaptive Linear Fusion of Hyperspectral Images for Color Display, Nathaniel P. Jacobson and Maya Gupta, IEEE Intl. Conf. on Image Processing, 2007.

Thesis: Bayesian Minimum Expected Risk Estimation of Distributions for Statistical Learning, Santosh Srivastava, Univ. of Washington PhD Thesis, 2007.

Thesis: Generative Models for Similarity-based Classification, Luca Cazzanti, Univ. of Washington PhD Thesis, 2007.

A Measure Theory Tutorial (Measure Theory for Dummies), Maya Gupta, UW EE Technical Report Series 2006-0008, 2006.

A multiresolutional estimated gradient architecture for global optimization, Megan Hazen and Maya Gupta, Congress on Evolutionary Computing , 2006.

Global Optimization for Multiple Transmitter Localization, J. K. Nelson, M. U. Hazen and M. R. Gupta, MILCOM, 2006.

Information-theoretic and set-theoretic similarity, Luca Cazzanti and Maya Gupta, Proc. of the IEEE Intl. Symposium on Information Theory, 2006.

Minimum Expected Risk Estimation for Near-neighbor Classification, Maya Gupta, S. Srivastava and L. Cazzanti, Univ. of Washington Dept. of Electrical Engineering Technical Report 2006-0006, 2006.

Nonparametric supervised learning by linear interpolation with maximum entropy, Maya Gupta, Robert M. Gray, and Richard A. Olshen, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 766-781, 2006.

On minimizing distortion and relative entropy, Michael P. Friedlander and Maya Gupta, IEEE Trans. on Information Theory, vol. 52, no. 1, pp. 238-245, 2006.

Reverse Engineering the Sound of Jazz, L. Cazzanti, V. Hasbrook, M. R. Gupta, UW EE Technical Report Series 2006- Design goals and solutions… 0011, 2006.

Wavelet principal component analysis and its application to hyperspectral imagery, Maya Gupta and Nathaniel P. Jacobson, IEEE Intl. Conf. on Image Processing , 2006.

Custom Color Enhancements by Statistical Learning (link goes to journal paper version of this work), Maya Gupta, Proceedings of the IEEE Intl. Conf. on Image Processing, pp. 968-971, 2005.

Design goals and solutions for display of hyperspectral images, Jacobson N.P. and Gupta M.R., IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 11, pp. 2684-2692, 2005. See also follow-on journal paper.  AVIRIS image rendering code

Display of Hyperspectral Imagery by Spectral Weighting Envelopes, Nathaniel P. Jacobson and Maya Gupta, Proceedings of the IEEE Intl. Conf. on Image Processing, pp. 622-625, 2005. See journal paper version.

Quality assessment of low free-energy protein structure predictions, Luca Cazzanti, Maya Gupta, Lars Malmstrom, and David Baker, Proceedings of the IEEE Workshop on Machine Learning for Signal Processing, 375-380, 2005.

Segmenting for wavelet compression, Maya Gupta and Andrey Stroilov, Proceedings of the Data Compression Conference, p. 462, 2005.

Simulating the effect of illumination using color transformations, Maya Gupta, Steve Upton, and Jayson Bowen, Proceedings of the SPIE Conference on Computational Imaging III, vol. 5674, pp. 248-258, 2005.

Inverting color transforms, Maya Gupta, Proceedings of the SPIE Electronic Imaging Conference on Computational Imaging II, vol. 5299, pp. 83-93, 2004.

Thesis: An information theory approach to supervised learning, Maya Gupta, (Dept. of Electrical Engineering, Stanford), 2003.

Analysis and classification of internal pipeline images, Deirdre O'Brien, Maya Gupta, Robert M. Gray, and Jon Kristian Hagene, Proceedings of the International Conference on Image Processing, pp. 577-580, 2003.

Automatic classification of images from internal optical inspection of gas pipelines, Deirdre O'Brien, Maya Gupta, Robert M. Gray, and Jon Kristian Hagene, Proceedings of the International Chemical and Petroleum Industry Inspection Technology VIII Conference, 2003.

Halftoning on the wavelet domain, Maya Gupta, Proceedings of the SPIE Electronic Imaging Conference, vol. 5008, pp. 431-442, 2003.

A two-stage color palettization algorithm for error diffusion, Niloy Mitra and Maya Gupta, Proceedings of the SPIE Electronic Imaging Conference, vol. 4662, pp. 207-217, 2002.

Robust speech recognition using wavelet coefficient features, Maya Gupta and Anna Gilbert, Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding, 2001.

Vector color filter array demosaicing, Maya Gupta and Ting Chen, Proceedings of the SPIE Electronic Imaging Conference, vol. 4306, pp. 374-382, 2001.

Block color quantization: a new method for halftoning, Maya Gupta, Michael Gormish, and David Stork, Proceedings of the International Conference on Image Processing, 2000.

Nonlinear vector multiresolutional analysis, Maya Gupta and Anna Gilbert, Proceedings of the Asilomar Conference on Systems and Signals, 2000.

Recent advances in terahertz imaging, D. M. Mittleman, M. Gupta, R. Neelamani, R. G. Baraniuk, J. V. Rudd, and M. Koch, Applied Physics B, vol. 68, pp. 1085-1094, 1999.