Publications

I am grateful for all of the wonderful collaborations that have led to the publications listed here.

Preprints/submissions

• Adhyyan Narang, Vidya Muthukumar and Anant Sahai: “Classification and adversarial examples in an overparameterized linear model: A signal-processing perspective”, short version at ICML’21 Workshop on Overparameterization: Pitfalls and Opportunities.
• Yehuda Dar, Vidya Muthukumar and Richard G. Baraniuk: “A farewell to the bias-variance tradeoff? An overview of the theory of overparameterized machine learning”.

Journal papers (under revision)

• Kuo-Wei Lai and Vidya Muthukumar: “General Loss Functions Lead to (Approximate) Interpolation in High Dimensions”, major revision at Journal of Machine Learning Research.

Journal papers (accepted)

• Ashwin Pananjady, Vidya Muthukumar and Andrew Thangaraj: “Just Wing It: Optimal Estimation of Missing Mass in a Markovian Sequence”, Journal of Machine Learning Research, 2024 issue.
• Vidya Muthukumar, Soham Phade and Anant Sahai: “On the impossibility of convergence of strategies arising from no-regret learning”, Mathematics of Operations Research, 2024 issue.
• Chiraag Kaushik, Andrew McRae, Mark Davenport and Vidya Muthukumar: “New equivalences between interpolation and SVMs: Kernels and structured features”, SIAM Journal on Mathematics of Data Science, 2024 issue.
• Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer and Vidya Muthukumar: “The good, bad and ugly sides of data augmentation: An implicit spectral regularization perspective”, Journal of Machine Learning Research, 2024 issue.
• Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar and Emma Brunskill: “Estimating optimal policy value in general linear contextual bandits”, Transactions of Machine Learning Research, 2024 issue.
• Ke Wang, Vidya Muthukumar and Christos Thrampoulidis: “Benign overfitting in multiclass classification: All roads lead to interpolation”, IEEE Transactions on Information Theory, 2023 issue.
• Daniel Hsu, Vidya Muthukumar and Ji Xu: “On the proliferation of support vectors in high dimensions”, Journal of Statistical Mechanics: Theory and Experiment (special issue, Machine Learning, 2022).
• Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu and Anant Sahai: “Classification vs regression in overparameterized regimes: Does the loss function matter?”, Journal of Machine Learning Research, 2021 issue.
• Vidya Muthukumar, Kailas Vodrahalli, Vignesh Subramanian and Anant Sahai: “Harmless interpolation of noisy data in linear regression”, IEEE Journal of Selected Areas in Information Theory, inaugural special issue on “Deep Learning: Mathematical Foundations and Applications to Information Science”, 2020 issue.
• Ashwin Pananjady, Cheng Mao, Vidya Muthukumar, Martin Wainwright and Thomas Courtade: “Worst-case v.s. Average-case Design for Estimation from Fixed Pairwise Comparisons”, Annals of Statistics, 2020 issue.
• Vidya Muhukumar and Anant Sahai: “Fundamental limits on ex-post enforcement and implications for spectrum rights”, IEEE Transactions on Cognitive Communications and Networking, 2017 issue.

Conference papers

• Tyler LaBonte, Jack Hill, Xinchen Zhang, Vidya Muthukumar and Abhishek Kumar: “The Group Robustness is in the Details: Revisiting Finetuning Under Spurious Correlations”, Neural Information Processing Systems (NeurIPS), Vancouver, 2024.
• Chiraag Kaushik, Justin Romberg and Vidya Muthukumar: “Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks”, Neural Information Processing Systems (NeurIPS), Vancouver, 2024.
• Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y Jin, Wenrui Ma, Vidya Muthukumar and Eva L Dyer: “Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance”, International Conference on Machine Learning (ICML), Vienna, 2024.
• Etash Guha, Jim James, Krishna Acharya, Vidya Muthukumar and Ashwin Pananjady: “One shot inverse reinforcement learning for stochastic linear bandits”, Uncertainty in Artificial Intelligence (UAI), Barcelona, 2024.
• Kuo-Wei Lai and Vidya Muthukumar: “Sharp analysis of out-of-distribution error for “importance-weighted” estimators in the overparameterized regime”, IEEE International Symposium on Information Theory (ISIT), Athens, 2024.
• Tyler LaBonte, Vidya Muthukumar and Abhishek Kumar: “Towards last-layer retraining for group robustness with fewer annotations”, Neural Information Processing Systems (NeurIPS), New Orleans, 2023.
• Guanghui Wang, Zihao Hu, Vidya Muthukumar and Jacob Abernethy: “Faster margin maximization rates for generic optimization methods”, Neural Information Processing Systems (NeurIPS), New Orleans, 2023 (spotlight).
• Yujia Jin, Vidya Muthukumar and Aaron Sidford: “The complexity of infinite-horizon general-sum stochastic games”, Innovations in Theoretical Computer Science (ITCS) 2023.
• Guanghui Wang, Zihao Hu, Vidya Muthukumar and Jacob Abernethy: “Adaptive oracle-efficient online learning”, Neural Information Processing Systems (NeurIPS), New Orleans, 2022.
• Vidya Muthukumar and Akshay Krishnamurthy: “Universal and data-adaptive algorithms for model selection in linear contextual bandits”, International Conference on Machine Learning (ICML), Baltimore, 2022.
• Andrew D. McRae, Santhosh Karnik, Mark A. Davenport and Vidya Muthukumar: “Harmless interpolation in regression and classification with structured features”. AISTATS 2022 (virtual).
• Wenshuo Guo, Kumar Krishna Agarwal, Aditya Grover, Vidya Muthukumar and Ashwin Pananjady: “Learning from an exploring demonstrator: Optimal reward estimation for bandits”., AISTATS 2022 (virtual), short versions at ICML’21 Workshop on Human-AI Collaboration in Sequential Decision-Making (spotlight talk)  and ICML’21 Workshop on Reinforcement Learning Theory.
• Ke Wang, Vidya Muthukumar and Christos Thrampoulidis: “Benign overfitting in multiclass classification: All roads lead to interpolation”, NeurIPS 2021 (virtual), short version at ICML’21 Workshop on Overparameterization: Pitfalls and Opportunities.
• Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong and Emma Brunskill, “Online model selection for reinforcement learning with function approximation”, AISTATS 2021 (virtual).
• Daniel Hsu, Vidya Muthukumar and Ji Xu: “On the proliferation of support vectors in high dimensions”, AISTATS 2021 (virtual).
• Niladri S. Chatterji, Vidya Muthukumar and Peter L. Bartlett: “OSOM: A Simultaneously Optimal Algorithm for the Multi-Armed and the Linear Contextual Bandit Problems”, AISTATS 2020 (virtual).
• Vidya Muthukumar, Kailas Vodrahalli and Anant Sahai: “Harmless interpolation in noisy linear regression”, IEEE International Symposium on Information Theory, Paris, 2019.
• Vidya Muthukumar: “Color-theoretic Explanations for Understanding Unequal Gender Classification Accuracy from Face Images”, IEEE CVPR Workshop on Bias Estimation in Face Analytics, Los Angeles, 2019.
• Vidya Muthukumar, Mitas Ray, Anant Sahai and Peter L. Bartlett: “Best of many worlds: Robust model selection for online supervised learning”, AISTATS 2019, Naha, Okinawa.
• Vidya Muthukumar and Anant Sahai: “Robust commitments and partial reputation”, ACM Economics and Computation, Phoenix, 2019; shorter version presented at Neural Information Processing Systems Workshop on Learning in the Presence of Strategic Behavior, Los Angeles, 2017
• Vidya Muthukumar and Anant Sahai: “Commitment in regulatory spectrum games: Examining the first-player advantage”, IEEE International Symposium on Information Theory, Aachen, 2017.
• Vidya Muthukumar and Anant Sahai: “Fundamental limits on ex-post enforcement and implications for spectrum rights”, IEEE Symposium on Dynamic Spectrum Access Networks, Baltimore, 2017.
• Kate Harrison, Vidya Muthukumar and Anant Sahai: “Whitespace Evaluation SofTware (WEST) and its applications to whitespace in Canada and Australia”, IEEE Symposium on Dynamic Spectrum Access Networks, Stockholm, 2015.
• Vidya Muthukumar, Angel Daruna, Vijay Kamble, Kate Harrison and Anant Sahai: “Whitespaces after the USA’s TV incentive auction: a spectrum reallocation case study”, IEEE International Conference on Communications, London, 2015.
• Matthew Thill, Vidya Muthukumar and Babak Hassibi: “Frames from Generalized Group Fourier Transforms and SL2(Fq)”, IEEE International Conference on Acoustics, Speech and Signal Processing, Florence, 2014.

Unpublished manuscripts

• Vidya Muthukumar: “Understanding Unequal Gender Classification Accuracy From Face Images”.