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2020 – today
- 2024
[j6]Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar:
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective. J. Mach. Learn. Res. 25: 91:1-91:85 (2024)- [j5]Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill:
Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity. Trans. Mach. Learn. Res. 2024 (2024) - [c22]Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y. Jin, Wenrui Ma, Vidya Muthukumar, Eva L. Dyer:
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance. ICML 2024 - [c21]Kuo-Wei Lai, Vidya Muthukumar:
Sharp Analysis of Out-of-Distribution Error for "Importance-Weighted" Estimators in the Overparameterized Regime. ISIT 2024: 3701-3706 - [i29]Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y. Jin, Wenrui Ma, Vidya Muthukumar, Eva L. Dyer:
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance. CoRR abs/2402.11742 (2024) - [i28]Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj:
Just Wing It: Optimal Estimation of Missing Mass in a Markovian Sequence. CoRR abs/2404.05819 (2024) - [i27]Kuo-Wei Lai, Vidya Muthukumar:
Sharp analysis of out-of-distribution error for "importance-weighted" estimators in the overparameterized regime. CoRR abs/2405.06546 (2024) - [i26]Chiraag Kaushik, Justin Romberg, Vidya Muthukumar:
Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks. CoRR abs/2406.02769 (2024) - [i25]Tyler LaBonte, John C. Hill, Xinchen Zhang, Vidya Muthukumar, Abhishek Kumar:
The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations. CoRR abs/2407.13957 (2024) - 2023
- [j4]Ke Wang
, Vidya Muthukumar, Christos Thrampoulidis:
Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation. IEEE Trans. Inf. Theory 69(12): 7909-7952 (2023) - [c20]Yujia Jin, Vidya Muthukumar, Aaron Sidford:
The Complexity of Infinite-Horizon General-Sum Stochastic Games. ITCS 2023: 76:1-76:20 - [c19]Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar:
Towards Last-layer Retraining for Group Robustness with Fewer Annotations. NeurIPS 2023 - [c18]Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy:
Faster Margin Maximization Rates for Generic Optimization Methods. NeurIPS 2023 - [i24]Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill:
Estimating Optimal Policy Value in General Linear Contextual Bandits. CoRR abs/2302.09451 (2023) - [i23]Kuo-Wei Lai, Vidya Muthukumar:
General Loss Functions Lead to (Approximate) Interpolation in High Dimensions. CoRR abs/2303.07475 (2023) - [i22]Chiraag Kaushik, Andrew D. McRae, Mark A. Davenport, Vidya Muthukumar:
New Equivalences Between Interpolation and SVMs: Kernels and Structured Features. CoRR abs/2305.02304 (2023) - [i21]Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy:
Faster Margin Maximization Rates for Generic Optimization Methods. CoRR abs/2305.17544 (2023) - [i20]Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar:
Towards Last-layer Retraining for Group Robustness with Fewer Annotations. CoRR abs/2309.08534 (2023) - 2022
- [c17]Andrew D. McRae, Santhosh Karnik, Mark A. Davenport, Vidya K. Muthukumar:
Harmless interpolation in regression and classification with structured features. AISTATS 2022: 5853-5875 - [c16]Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya K. Muthukumar, Ashwin Pananjady:
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits. AISTATS 2022: 6357-6386 - [c15]Vidya K. Muthukumar, Akshay Krishnamurthy:
Universal and data-adaptive algorithms for model selection in linear contextual bandits. ICML 2022: 16197-16222 - [c14]Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy:
Adaptive Oracle-Efficient Online Learning. NeurIPS 2022 - [i19]Yujia Jin, Vidya Muthukumar, Aaron Sidford:
The Complexity of Infinite-Horizon General-Sum Stochastic Games. CoRR abs/2204.04186 (2022) - [i18]Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar:
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective. CoRR abs/2210.05021 (2022) - [i17]Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy:
Adaptive Oracle-Efficient Online Learning. CoRR abs/2210.09385 (2022) - 2021
- [j3]Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, Anant Sahai:
Classification vs regression in overparameterized regimes: Does the loss function matter? J. Mach. Learn. Res. 22: 222:1-222:69 (2021) - [c13]Daniel Hsu, Vidya Muthukumar, Ji Xu:
On the proliferation of support vectors in high dimensions. AISTATS 2021: 91-99 - [c12]Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill:
Online Model Selection for Reinforcement Learning with Function Approximation. AISTATS 2021: 3340-3348 - [c11]Ke Wang, Vidya Muthukumar, Christos Thrampoulidis:
Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation. NeurIPS 2021: 24164-24179 - [i16]Ke Wang, Vidya Muthukumar, Christos Thrampoulidis:
Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation. CoRR abs/2106.10865 (2021) - [i15]Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya Muthukumar, Ashwin Pananjady:
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits. CoRR abs/2106.14866 (2021) - [i14]Yehuda Dar, Vidya Muthukumar, Richard G. Baraniuk:
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning. CoRR abs/2109.02355 (2021) - [i13]Adhyyan Narang, Vidya Muthukumar, Anant Sahai:
Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective. CoRR abs/2109.13215 (2021) - [i12]Vidya Muthukumar, Akshay Krishnamurthy:
Universal and data-adaptive algorithms for model selection in linear contextual bandits. CoRR abs/2111.04688 (2021) - [i11]Andrew D. McRae, Santhosh Karnik, Mark A. Davenport, Vidya Muthukumar:
Harmless interpolation in regression and classification with structured features. CoRR abs/2111.05198 (2021) - 2020
- [j2]Vidya Muthukumar, Kailas Vodrahalli, Vignesh Subramanian, Anant Sahai:
Harmless Interpolation of Noisy Data in Regression. IEEE J. Sel. Areas Inf. Theory 1(1): 67-83 (2020) - [c10]Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett:
OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits. AISTATS 2020: 1844-1854 - [i10]Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel J. Hsu, Anant Sahai:
Classification vs regression in overparameterized regimes: Does the loss function matter? CoRR abs/2005.08054 (2020) - [i9]Daniel Hsu, Vidya Muthukumar, Ji Xu:
On the proliferation of support vectors in high dimensions. CoRR abs/2009.10670 (2020) - [i8]Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill:
Online Model Selection for Reinforcement Learning with Function Approximation. CoRR abs/2011.09750 (2020) - [i7]Vidya Muthukumar, Soham Phade, Anant Sahai:
On the Impossibility of Convergence of Mixed Strategies with No Regret Learning. CoRR abs/2012.02125 (2020)
2010 – 2019
- 2019
- [c9]Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter L. Bartlett:
Best of many worlds: Robust model selection for online supervised learning. AISTATS 2019: 3177-3186 - [c8]Vidya Muthukumar:
Color-Theoretic Experiments to Understand Unequal Gender Classification Accuracy From Face Images. CVPR Workshops 2019: 2286-2295 - [c7]Vidya Muthukumar, Anant Sahai:
Robust Commitments and Partial Reputation. EC 2019: 637-638 - [c6]Vidya Muthukumar, Kailas Vodrahalli, Anant Sahai:
Harmless interpolation of noisy data in regression. ISIT 2019: 2299-2303 - [i6]Vidya Muthukumar, Kailas Vodrahalli, Anant Sahai:
Harmless interpolation of noisy data in regression. CoRR abs/1903.09139 (2019) - [i5]Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett:
OSOM: A Simultaneously Optimal Algorithm for Multi-Armed and Linear Contextual Bandits. CoRR abs/1905.10040 (2019) - [i4]Vidya Muthukumar, Anant Sahai:
Robust Commitments and Partial Reputation. CoRR abs/1905.11555 (2019) - 2018
- [i3]Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter L. Bartlett:
Best of many worlds: Robust model selection for online supervised learning. CoRR abs/1805.08562 (2018) - [i2]Vidya Muthukumar, Tejaswini Pedapati, Nalini K. Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney:
Understanding Unequal Gender Classification Accuracy from Face Images. CoRR abs/1812.00099 (2018) - 2017
- [j1]Vidya Muthukumar, Anant Sahai:
Fundamental Limits on Ex-Post Enforcement and Implications for Spectrum Rights. IEEE Trans. Cogn. Commun. Netw. 3(3): 491-504 (2017) - [c5]Vidya Muthukumar, Anant Sahai:
Fundamental limits on ex-post enforcement and implications for spectrum rights. DySPAN 2017: 1-10 - [c4]Vidya Muthukumar, Anant Sahai:
Commitment in regulatory spectrum games: Examining the first-player advantage. ISIT 2017: 2498-2502 - [i1]Ashwin Pananjady, Cheng Mao, Vidya Muthukumar, Martin J. Wainwright, Thomas A. Courtade:
Worst-case vs Average-case Design for Estimation from Fixed Pairwise Comparisons. CoRR abs/1707.06217 (2017) - 2015
- [c3]Kate Harrison, Vidya Muthukumar, Anant Sahai:
Whitespace evaluation software (WEST) and its applications to whitespace in Canada and Australia. DySPAN 2015: 47-58 - [c2]Vidya Muthukumar, Angel Daruna, Vijay Kamble, Kate Harrison, Anant Sahai:
Whitespaces after the USA's TV incentive auction: A spectrum reallocation case study. ICC 2015: 7582-7588 - 2014
- [c1]Matthew Thill, Vidya Muthukumar, Babak Hassibi:
Frames from generalized group fourier transforms and SL2(Fq). ICASSP 2014: 4923-4927
Coauthor Index
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