A quadratically convergent iterative scheme for locating conical degeneracies in the spectra of parametric self-adjoint matrices. - Gregory Berkolaiko; Advait Parulekar (CoRR, 2020)

Stochastic Linear Bandits with Protected Subspace. - Advait Parulekar; Soumya Basu 001; Aditya Gopalan; Karthikeyan Shanmugam; Sanjay Shakkottai (CoRR, 2020)

L1 Regression with Lewis Weights Subsampling. - Aditya Parulekar; Advait Parulekar; Eric Price 001 (APPROX-RANDOM, 2021)

Locating Conical Degeneracies in the Spectra of Parametric Self-adjoint Matrices. - Gregory Berkolaiko; Advait Parulekar (SIAM J. Matrix Anal. Appl., 2021)

Improved Algorithms for Misspecified Linear Markov Decision Processes. - Daniel Vial; Advait Parulekar; Sanjay Shakkottai; R. Srikant 001 (AISTATS, 2022)

Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation. - Daniel Vial; Advait Parulekar; Sanjay Shakkottai; R. Srikant 001 (ICML, 2022)

InfoNCE Loss Provably Learns Cluster-Preserving Representations. - Advait Parulekar; Liam Collins; Karthikeyan Shanmugam; Aryan Mokhtari; Sanjay Shakkottai (COLT, 2023)

PAC Generalization via Invariant Representations. - Advait U. Parulekar; Karthikeyan Shanmugam; Sanjay Shakkottai (ICML, 2023)

A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models. - Litu Rout; Advait Parulekar; Constantine Caramanis; Sanjay Shakkottai (CoRR, 2023)

In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness. - Liam Collins; Advait Parulekar; Aryan Mokhtari; Sujay Sanghavi; Sanjay Shakkottai (NeurIPS, 2024)

Efficient Approximate Posterior Sampling with Annealed Langevin Monte Carlo. - Advait Parulekar; Litu Rout; Karthikeyan Shanmugam; Sanjay Shakkottai (CoRR, 2025)