Muthukumar, Vidya

23 publications

COLT 2025 Estimating Stationary Mass, Frequency by Frequency Milind Nakul, Vidya Muthukumar, Ashwin Pananjady

JMLR 2025 General Loss Functions Lead to (Approximate) Interpolation in High Dimensions Kuo-Wei Lai, Vidya Muthukumar

ICML 2025 Improved and Oracle-Efficient Online $\ell_1$-Multicalibration Rohan Ghuge, Vidya Muthukumar, Sahil Singla

ICLRW 2025 On the Unreasonable Effectiveness of Last-Layer Retraining John Collins Hill, Tyler LaBonte, Xinchen Zhang, Vidya Muthukumar

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

ICML 2024 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

TMLR 2024 Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill

JMLR 2024 Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj

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

NeurIPS 2024 Precise Asymptotics of Reweighted Least-Squares Algorithms for Linear Diagonal Networks Chiraag Kaushik, Justin Romberg, Vidya Muthukumar

JMLR 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

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

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

NeurIPS 2023 Towards Last-Layer Retraining for Group Robustness with Fewer Annotations Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar

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

NeurIPSW 2022 Dropout Disagreement: A Recipe for Group Robustness with Fewer Annotations Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar

AISTATS 2021 On the Proliferation of Support Vectors in High Dimensions Daniel Hsu, Vidya Muthukumar, Ji Xu

AISTATS 2021 Online Model Selection for Reinforcement Learning with Function Approximation Jonathan Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill

NeurIPS 2021 Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation Ke Wang, Vidya Muthukumar, Christos Thrampoulidis

JMLR 2021 Classification vs Regression in Overparameterized Regimes: Does the Loss Function Matter? Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, Anant Sahai

AISTATS 2020 OSOM: A Simultaneously Optimal Algorithm for Multi-Armed and Linear Contextual Bandits Niladri Chatterji, Vidya Muthukumar, Peter Bartlett

AISTATS 2019 Best of Many Worlds: Robust Model Selection for Online Supervised Learning Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter Bartlett

CVPRW 2019 Color-Theoretic Experiments to Understand Unequal Gender Classification Accuracy from Face Images Vidya Muthukumar