Frames from generalized group fourier transforms and SL2(Fq). - Matthew Thill; Vidya Muthukumar; Babak Hassibi (ICASSP, 2014)

Whitespace evaluation software (WEST) and its applications to whitespace in Canada and Australia. - Kate Harrison; Vidya Muthukumar; Anant Sahai (DySPAN, 2015)

Whitespaces after the USA's TV incentive auction: A spectrum reallocation case study. - Vidya Muthukumar; Angel Daruna; Vijay Kamble; Kate Harrison; Anant Sahai (ICC, 2015)

Commitment in regulatory spectrum games: Examining the first-player advantage. - Vidya Muthukumar; Anant Sahai (ISIT, 2017)

Worst-case vs Average-case Design for Estimation from Fixed Pairwise Comparisons. - Ashwin Pananjady; Cheng Mao; Vidya Muthukumar; Martin J. Wainwright; Thomas A. Courtade (CoRR, 2017)

Fundamental Limits on Ex-Post Enforcement and Implications for Spectrum Rights. - Vidya Muthukumar; Anant Sahai (IEEE Trans. Cogn. Commun. Netw., 2017)

Understanding Unequal Gender Classification Accuracy from Face Images. - Vidya Muthukumar; Tejaswini Pedapati; Nalini K. Ratha; Prasanna Sattigeri; Chai-Wah Wu; Brian Kingsbury; Abhishek Kumar 001; Samuel Thomas 001; Aleksandra Mojsilovic; Kush R. Varshney (CoRR, 2018)

Best of many worlds: Robust model selection for online supervised learning. - Vidya Muthukumar; Mitas Ray; Anant Sahai; Peter L. Bartlett (AISTATS, 2019)

Color-Theoretic Experiments to Understand Unequal Gender Classification Accuracy From Face Images. - Vidya Muthukumar (CVPR Workshops, 2019)

Robust Commitments and Partial Reputation. - Vidya Muthukumar; Anant Sahai (EC, 2019)

Harmless interpolation of noisy data in regression. - Vidya Muthukumar; Kailas Vodrahalli; Anant Sahai (ISIT, 2019)

OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits. - Niladri S. Chatterji; Vidya Muthukumar; Peter L. Bartlett (AISTATS, 2020)

On the Impossibility of Convergence of Mixed Strategies with No Regret Learning. - Vidya Muthukumar; Soham Phade; Anant Sahai (CoRR, 2020)

Harmless Interpolation of Noisy Data in Regression. - Vidya Muthukumar; Kailas Vodrahalli; Vignesh Subramanian; Anant Sahai (IEEE J. Sel. Areas Inf. Theory, 2020)

On the proliferation of support vectors in high dimensions. - Daniel Hsu 001; Vidya Muthukumar; Ji Xu 003 (AISTATS, 2021)

Online Model Selection for Reinforcement Learning with Function Approximation. - Jonathan N. Lee; Aldo Pacchiano; Vidya Muthukumar; Weihao Kong; Emma Brunskill (AISTATS, 2021)

A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning. - Yehuda Dar; Vidya Muthukumar; Richard G. Baraniuk (CoRR, 2021)

Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective. - Adhyyan Narang; Vidya Muthukumar; Anant Sahai (CoRR, 2021)

Classification vs regression in overparameterized regimes: Does the loss function matter? - Vidya Muthukumar; Adhyyan Narang; Vignesh Subramanian; Mikhail Belkin; Daniel Hsu 001; Anant Sahai (J. Mach. Learn. Res., 2021)

Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits. - Wenshuo Guo; Kumar Krishna Agrawal; Aditya Grover; Vidya K. Muthukumar; Ashwin Pananjady (AISTATS, 2022)

Harmless interpolation in regression and classification with structured features. - Andrew D. McRae; Santhosh Karnik; Mark A. Davenport; Vidya K. Muthukumar (AISTATS, 2022)

Universal and data-adaptive algorithms for model selection in linear contextual bandits. - Vidya K. Muthukumar; Akshay Krishnamurthy (ICML, 2022)

Adaptive Oracle-Efficient Online Learning. - Guanghui Wang; Zihao Hu; Vidya Muthukumar; Jacob D. Abernethy (NeurIPS, 2022)

The Complexity of Infinite-Horizon General-Sum Stochastic Games. - Yujia Jin; Vidya Muthukumar; Aaron Sidford (ITCS, 2023)

Towards Last-layer Retraining for Group Robustness with Fewer Annotations. - Tyler LaBonte; Vidya Muthukumar; Abhishek Kumar 001 (NeurIPS, 2023)

Faster Margin Maximization Rates for Generic Optimization Methods. - Guanghui Wang; Zihao Hu; Vidya Muthukumar; Jacob D. Abernethy (NeurIPS, 2023)

Estimating Optimal Policy Value in General Linear Contextual Bandits. - Jonathan N. Lee; Weihao Kong; Aldo Pacchiano; Vidya Muthukumar; Emma Brunskill (CoRR, 2023)

General Loss Functions Lead to (Approximate) Interpolation in High Dimensions. - Kuo-Wei Lai; Vidya Muthukumar (CoRR, 2023)

Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation. - Ke Wang 035; Vidya Muthukumar; Christos Thrampoulidis (IEEE Trans. Inf. Theory, 2023)

Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance. - Chiraag Kaushik; Ran Liu; Chi-Heng Lin; Amrit Khera; Matthew Y. Jin; Wenrui Ma; Vidya Muthukumar; Eva L. Dyer (ICML, 2024)

Sharp Analysis of Out-of-Distribution Error for "Importance-Weighted" Estimators in the Overparameterized Regime. - Kuo-Wei Lai; Vidya Muthukumar (ISIT, 2024)

Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks. - Chiraag Kaushik; Justin Romberg; Vidya Muthukumar (NeurIPS, 2024)

The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations. - Tyler LaBonte; John C. Hill; Xinchen Zhang; Vidya Muthukumar; Abhishek Kumar 001 (NeurIPS, 2024)

One Shot Inverse Reinforcement Learning for Stochastic Linear Bandits. - Etash Guha; Jim James; Krishna Acharya; Vidya Muthukumar; Ashwin Pananjady (UAI, 2024)

Just Wing It: Optimal Estimation of Missing Mass in a Markovian Sequence. - Ashwin Pananjady; Vidya Muthukumar; Andrew Thangaraj (CoRR, 2024)

The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective. - Chi-Heng Lin; Chiraag Kaushik; Eva L. Dyer; Vidya Muthukumar (J. Mach. Learn. Res., 2024)

New Equivalences between Interpolation and SVMs: Kernels and Structured Features. - Chiraag Kaushik; Andrew D. McRae; Mark A. Davenport; Vidya Muthukumar (SIAM J. Math. Data Sci., 2024)

Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity. - Jonathan Lee 002; Weihao Kong; Aldo Pacchiano; Vidya Muthukumar; Emma Brunskill (Trans. Mach. Learn. Res., 2024)

Task Shift: From Classification to Regression in Overparameterized Linear Models. - Tyler LaBonte; Kuo-Wei Lai; Vidya Muthukumar (AISTATS, 2025)

Estimating stationary mass, frequency by frequency. - Milind Nakul; Vidya Muthukumar; Ashwin Pananjady (COLT, 2025)

Multi-Agent Performative Prediction Beyond the Insensitivity Assumption: A Case Study for Mortgage Competition. - Guanghui Wang; Krishna Acharya; Lokranjan Lakshmikanthan; Vidya Muthukumar; Juba Ziani (CoRR, 2025)

Improved and Oracle-Efficient Online ℓ1-Multicalibration. - Rohan Ghuge; Vidya Muthukumar; Sahil Singla 001 (CoRR, 2025)