Vidya Muthukumar

71 papers A* 10A 7B 3Misc 1Journal 46Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	How Does the ReLU Activation Affect the Implicit Bias of Gradient Descent on High-dimensional Neural Network Regression? CoRR Kuo-Wei Lai, Guanghui Wang, Molei Tao, Vidya Muthukumar
2025	J	`jnl`	A general technique for approximating high-dimensional empirical kernel matrices. CoRR Chiraag Kaushik, Justin Romberg, Vidya Muthukumar
2025	A*	`conf`	Estimating stationary mass, frequency by frequency. COLT Milind Nakul, Vidya Muthukumar, Ashwin Pananjady
2025	J	`jnl`	Estimating stationary mass, frequency by frequency. CoRR Milind Nakul, Vidya Muthukumar, Ashwin Pananjady
2025	J	`jnl`	General Loss Functions Lead to (Approximate) Interpolation in High Dimensions. J. Mach. Learn. Res. Kuo-Wei Lai, Vidya Muthukumar
2025	J	`jnl`	Improved and Oracle-Efficient Online ℓ CoRR Rohan Ghuge, Vidya Muthukumar, Sahil Singla
2025	A*	`conf`	Improved and Oracle-Efficient Online ℓ1-Multicalibration. ICML Rohan Ghuge, Vidya Muthukumar, Sahil Singla
2025	J	`jnl`	Multi-Agent Performative Prediction Beyond the Insensitivity Assumption: A Case Study for Mortgage Competition. CoRR Guanghui Wang, Krishna Acharya, Lokranjan Lakshmikanthan, Vidya Muthukumar, Juba Ziani
2025	J	`jnl`	On the Impossibility of Convergence of Mixed Strategies with Optimal No-Regret Learning. Math. Oper. Res. Vidya Muthukumar, Soham Phade, Anant Sahai
2025	J	`jnl`	On the Unreasonable Effectiveness of Last-layer Retraining. CoRR John C. Hill, Tyler LaBonte, Xinchen Zhang, Vidya Muthukumar
2025	A	`conf`	Task Shift: From Classification to Regression in Overparameterized Linear Models. AISTATS Tyler LaBonte, Kuo-Wei Lai, Vidya Muthukumar
2025	J	`jnl`	Task Shift: From Classification to Regression in Overparameterized Linear Models. CoRR Tyler LaBonte, Kuo-Wei Lai, Vidya Muthukumar
2024	A*	`conf`	Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance. ICML Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y. Jin, Wenrui Ma, Vidya Muthukumar, Eva L. Dyer
2024	J	`jnl`	Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance. CoRR Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y. Jin, Wenrui Ma, Vidya Muthukumar, Eva L. Dyer
2024	J	`jnl`	Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity. Trans. Mach. Learn. Res. Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill
2024	J	`jnl`	Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence. J. Mach. Learn. Res. Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj
2024	J	`jnl`	Just Wing It: Optimal Estimation of Missing Mass in a Markovian Sequence. CoRR Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj
2024	J	`jnl`	New Equivalences between Interpolation and SVMs: Kernels and Structured Features. SIAM J. Math. Data Sci. Chiraag Kaushik, Andrew D. McRae, Mark A. Davenport, Vidya Muthukumar
2024	A	`conf`	One Shot Inverse Reinforcement Learning for Stochastic Linear Bandits. UAI Etash Guha, Jim James, Krishna Acharya, Vidya Muthukumar, Ashwin Pananjady
2024	A*	`conf`	Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks. NeurIPS Chiraag Kaushik, Justin Romberg, Vidya Muthukumar
2024	J	`jnl`	Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks. CoRR Chiraag Kaushik, Justin Romberg, Vidya Muthukumar
2024	B	`conf`	Sharp Analysis of Out-of-Distribution Error for "Importance-Weighted" Estimators in the Overparameterized Regime. ISIT Kuo-Wei Lai, Vidya Muthukumar
2024	J	`jnl`	Sharp analysis of out-of-distribution error for "importance-weighted" estimators in the overparameterized regime. CoRR Kuo-Wei Lai, Vidya Muthukumar
2024	A*	`conf`	The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations. NeurIPS Tyler LaBonte, John C. Hill, Xinchen Zhang, Vidya Muthukumar, Abhishek Kumar
2024	J	`jnl`	The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations. CoRR Tyler LaBonte, John C. Hill, Xinchen Zhang, Vidya Muthukumar, Abhishek Kumar
2024	J	`jnl`	The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective. J. Mach. Learn. Res. Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar
2023	J	`jnl`	Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation. IEEE Trans. Inf. Theory Ke Wang, Vidya Muthukumar, Christos Thrampoulidis
2023	J	`jnl`	Estimating Optimal Policy Value in General Linear Contextual Bandits. CoRR Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill
2023	A*	`conf`	Faster Margin Maximization Rates for Generic Optimization Methods. NeurIPS Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy
2023	J	`jnl`	Faster Margin Maximization Rates for Generic Optimization Methods. CoRR Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy
2023	J	`jnl`	General Loss Functions Lead to (Approximate) Interpolation in High Dimensions. CoRR Kuo-Wei Lai, Vidya Muthukumar
2023	J	`jnl`	New Equivalences Between Interpolation and SVMs: Kernels and Structured Features. CoRR Chiraag Kaushik, Andrew D. McRae, Mark A. Davenport, Vidya Muthukumar
2023	A	`conf`	The Complexity of Infinite-Horizon General-Sum Stochastic Games. ITCS Yujia Jin, Vidya Muthukumar, Aaron Sidford
2023	A*	`conf`	Towards Last-layer Retraining for Group Robustness with Fewer Annotations. NeurIPS Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar
2023	J	`jnl`	Towards Last-layer Retraining for Group Robustness with Fewer Annotations. CoRR Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar
2022	A*	`conf`	Adaptive Oracle-Efficient Online Learning. NeurIPS Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy
2022	J	`jnl`	Adaptive Oracle-Efficient Online Learning. CoRR Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy
2022	J	`jnl`	The Complexity of Infinite-Horizon General-Sum Stochastic Games. CoRR Yujia Jin, Vidya Muthukumar, Aaron Sidford
2022	J	`jnl`	The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective. CoRR Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar
2021	J	`jnl`	A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning. CoRR Yehuda Dar, Vidya Muthukumar, Richard G. Baraniuk
2021	A*	`conf`	Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation. NeurIPS Ke Wang, Vidya Muthukumar, Christos Thrampoulidis
2021	J	`jnl`	Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation. CoRR Ke Wang, Vidya Muthukumar, Christos Thrampoulidis
2021	J	`jnl`	Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective. CoRR Adhyyan Narang, Vidya Muthukumar, Anant Sahai
2021	J	`jnl`	Classification vs regression in overparameterized regimes: Does the loss function matter? J. Mach. Learn. Res. Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, Anant Sahai
2021	J	`jnl`	Harmless interpolation in regression and classification with structured features. CoRR Andrew D. McRae, Santhosh Karnik, Mark A. Davenport, Vidya Muthukumar
2021	J	`jnl`	Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits. CoRR Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya Muthukumar, Ashwin Pananjady
2021	A	`conf`	On the proliferation of support vectors in high dimensions. AISTATS Daniel Hsu, Vidya Muthukumar, Ji Xu
2021	A	`conf`	Online Model Selection for Reinforcement Learning with Function Approximation. AISTATS Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill
2021	J	`jnl`	Universal and data-adaptive algorithms for model selection in linear contextual bandits. CoRR Vidya Muthukumar, Akshay Krishnamurthy
2020	J	`jnl`	Classification vs regression in overparameterized regimes: Does the loss function matter? CoRR Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel J. Hsu, Anant Sahai
2020	J	`jnl`	Harmless Interpolation of Noisy Data in Regression. IEEE J. Sel. Areas Inf. Theory Vidya Muthukumar, Kailas Vodrahalli, Vignesh Subramanian, Anant Sahai
2020	A	`conf`	OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits. AISTATS Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett
2020	J	`jnl`	On the Impossibility of Convergence of Mixed Strategies with No Regret Learning. CoRR Vidya Muthukumar, Soham Phade, Anant Sahai
2020	J	`jnl`	On the proliferation of support vectors in high dimensions. CoRR Daniel Hsu, Vidya Muthukumar, Ji Xu
2020	J	`jnl`	Online Model Selection for Reinforcement Learning with Function Approximation. CoRR Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill
2019	A	`conf`	Best of many worlds: Robust model selection for online supervised learning. AISTATS Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter L. Bartlett
2019	—	`conf`	Color-Theoretic Experiments to Understand Unequal Gender Classification Accuracy From Face Images. CVPR Workshops Vidya Muthukumar
2019	B	`conf`	Harmless interpolation of noisy data in regression. ISIT Vidya Muthukumar, Kailas Vodrahalli, Anant Sahai
2019	J	`jnl`	Harmless interpolation of noisy data in regression. CoRR Vidya Muthukumar, Kailas Vodrahalli, Anant Sahai
2019	J	`jnl`	OSOM: A Simultaneously Optimal Algorithm for Multi-Armed and Linear Contextual Bandits. CoRR Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett
2019	A*	`conf`	Robust Commitments and Partial Reputation. EC Vidya Muthukumar, Anant Sahai
2019	J	`jnl`	Robust Commitments and Partial Reputation. CoRR Vidya Muthukumar, Anant Sahai
2018	J	`jnl`	Best of many worlds: Robust model selection for online supervised learning. CoRR Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter L. Bartlett
2018	J	`jnl`	Understanding Unequal Gender Classification Accuracy from Face Images. CoRR Vidya Muthukumar, Tejaswini Pedapati, Nalini K. Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney
2017	B	`conf`	Commitment in regulatory spectrum games: Examining the first-player advantage. ISIT Vidya Muthukumar, Anant Sahai
2017	J	`jnl`	Fundamental Limits on Ex-Post Enforcement and Implications for Spectrum Rights. IEEE Trans. Cogn. Commun. Netw. Vidya Muthukumar, Anant Sahai
2017	—	`conf`	Fundamental limits on ex-post enforcement and implications for spectrum rights. DySPAN Vidya Muthukumar, Anant Sahai
2017	J	`jnl`	Worst-case vs Average-case Design for Estimation from Fixed Pairwise Comparisons. CoRR Ashwin Pananjady, Cheng Mao, Vidya Muthukumar, Martin J. Wainwright, Thomas A. Courtade
2015	—	`conf`	Whitespace evaluation software (WEST) and its applications to whitespace in Canada and Australia. DySPAN Kate Harrison, Vidya Muthukumar, Anant Sahai
2015	—	`conf`	Whitespaces after the USA's TV incentive auction: A spectrum reallocation case study. ICC Vidya Muthukumar, Angel Daruna, Vijay Kamble, Kate Harrison, Anant Sahai
2014	Misc	`conf`	Frames from generalized group fourier transforms and SL2(Fq). ICASSP Matthew Thill, Vidya Muthukumar, Babak Hassibi