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ORCIDSushrut Karmalkar
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2020 – today
- 2024
[c21]Ilias Diakonikolas, Sushrut Karmalkar, Shuo Pang, Aaron Potechin:
Sum-of-Squares Lower Bounds for Non-Gaussian Component Analysis. FOCS 2024: 949-958- [c20]Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination. ICML 2024 - [c19]David Jin, Sushrut Karmalkar, Harry Zhang, Luca Carlone:
Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds. ICRA 2024: 4990-4997 - [i20]David Jin, Sushrut Karmalkar, Harry Zhang, Luca Carlone:
Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds. CoRR abs/2402.10865 (2024) - [i19]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination. CoRR abs/2403.10416 (2024) - [i18]Ilias Diakonikolas, Sushrut Karmalkar, Jongho Park, Christos Tzamos:
First Order Stochastic Optimization with Oblivious Noise. CoRR abs/2408.02090 (2024) - [i17]Ilias Diakonikolas, Sushrut Karmalkar, Shuo Pang, Aaron Potechin:
Sum-of-squares lower bounds for Non-Gaussian Component Analysis. CoRR abs/2410.21426 (2024) - 2023
- [c18]Ilias Diakonikolas, Sushrut Karmalkar, Jongho Park, Christos Tzamos:
Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions. COLT 2023: 5453-5475 - [c17]Ilias Diakonikolas, Sushrut Karmalkar, Jongho Park, Christos Tzamos:
First Order Stochastic Optimization with Oblivious Noise. NeurIPS 2023 - [i16]Ilias Diakonikolas, Sushrut Karmalkar, Jongho Park, Christos Tzamos:
Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions. CoRR abs/2309.11657 (2023) - 2022
- [c16]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Mean Estimation via Sum of Squares. COLT 2022: 4703-4763 - [c15]Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering. NeurIPS 2022 - [i15]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Mean Estimation via Sum of Squares. CoRR abs/2206.03441 (2022) - [i14]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering. CoRR abs/2206.05245 (2022) - 2021
- [c14]Ajil Jalal, Sushrut Karmalkar, Alex Dimakis, Eric Price:
Instance-Optimal Compressed Sensing via Posterior Sampling. ICML 2021: 4709-4720 - [c13]Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann, Alex Dimakis, Eric Price:
Fairness for Image Generation with Uncertain Sensitive Attributes. ICML 2021: 4721-4732 - [i13]Ajil Jalal, Sushrut Karmalkar, Alexandros G. Dimakis, Eric Price:
Instance-Optimal Compressed Sensing via Posterior Sampling. CoRR abs/2106.11438 (2021) - [i12]Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann, Alexandros G. Dimakis, Eric Price:
Fairness for Image Generation with Uncertain Sensitive Attributes. CoRR abs/2106.12182 (2021) - 2020
- [c12]Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi
:
Approximation Schemes for ReLU Regression. COLT 2020: 1452-1485 - [c11]Ainesh Bakshi, Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar, Pravesh K. Kothari:
Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures. FOCS 2020: 149-159 - [c10]Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans:
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. ICML 2020: 3587-3596 - [c9]Akshay Kamath, Eric Price, Sushrut Karmalkar:
On the Power of Compressed Sensing with Generative Models. ICML 2020: 5101-5109 - [i11]Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar:
Robustly Learning any Clusterable Mixture of Gaussians. CoRR abs/2005.06417 (2020) - [i10]Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi:
Approximation Schemes for ReLU Regression. CoRR abs/2005.12844 (2020) - [i9]Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans:
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. CoRR abs/2006.12011 (2020) - [i8]Aravind Gollakota, Sushrut Karmalkar, Adam R. Klivans:
The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning. CoRR abs/2010.11925 (2020)
2010 – 2019
- 2019
- [c8]Sushrut Karmalkar, Adam R. Klivans, Pravesh Kothari:
List-decodable Linear Regression. NeurIPS 2019: 7423-7432 - [c7]Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans:
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. NeurIPS 2019: 8582-8591 - [c6]Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Eric Price, Alistair Stewart:
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering. NeurIPS 2019: 10688-10699 - [c5]Sushrut Karmalkar, Eric Price:
Compressed Sensing with Adversarial Sparse Noise via L1 Regression. SOSA 2019: 19:1-19:19 - [i7]Sourav Chakraborty, Sushrut Karmalkar, Srijita Kundu, Satyanarayana V. Lokam, Nitin Saurabh:
Fourier Entropy-Influence Conjecture for Random Linear Threshold Functions. CoRR abs/1903.11635 (2019) - [i6]Sushrut Karmalkar, Adam R. Klivans, Pravesh K. Kothari:
List-Decodable Linear Regression. CoRR abs/1905.05679 (2019) - [i5]Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans:
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. CoRR abs/1911.01462 (2019) - [i4]Ilias Diakonikolas, Sushrut Karmalkar, Daniel Kane, Eric Price, Alistair Stewart:
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering. CoRR abs/1911.08085 (2019) - [i3]Akshay Kamath, Sushrut Karmalkar, Eric Price:
Lower Bounds for Compressed Sensing with Generative Models. CoRR abs/1912.02938 (2019) - 2018
- [c4]Amit Deshpande, Navin Goyal, Sushrut Karmalkar:
Depth separation and weight-width trade-offs for sigmoidal neural networks. ICLR (Workshop) 2018 - [c3]Sourav Chakraborty, Sushrut Karmalkar, Srijita Kundu, Satyanarayana V. Lokam, Nitin Saurabh:
Fourier Entropy-Influence Conjecture for Random Linear Threshold Functions. LATIN 2018: 275-289 - [i2]Sushrut Karmalkar, Eric Price:
Compressed Sensing with Adversarial Sparse Noise via L1 Regression. CoRR abs/1809.08055 (2018) - 2017
- [c2]Daniel Kane, Sushrut Karmalkar, Eric Price:
Robust Polynomial Regression up to the Information Theoretic Limit. FOCS 2017: 391-402 - [c1]Amit Deshpande, Sushrut Karmalkar:
On Robust Concepts and Small Neural Nets. ICLR (Workshop) 2017 - [i1]Daniel M. Kane, Sushrut Karmalkar, Eric Price:
Robust polynomial regression up to the information theoretic limit. CoRR abs/1708.03257 (2017)
Coauthor Index
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