Sushrut Karmalkar

According to our database, Sushrut Karmalkar authored at least 24 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds.

David Jin, Sushrut Karmalkar, Harry Zhang, Luca Carlone

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination.

Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Sum-of-Squares Lower Bounds for Non-Gaussian Component Analysis.

Ilias Diakonikolas, Sushrut Karmalkar, Shuo Pang, Aaron Potechin

Proceedings of the 65th IEEE Annual Symposium on Foundations of Computer Science, 2024

2023

First Order Stochastic Optimization with Oblivious Noise.

Ilias Diakonikolas, Sushrut Karmalkar, Jongho Park, Christos Tzamos

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions.

Ilias Diakonikolas, Sushrut Karmalkar, Jongho Park, Christos Tzamos

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering.

Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Robust Sparse Mean Estimation via Sum of Squares.

Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Fairness for Image Generation with Uncertain Sensitive Attributes.

Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann, Alex Dimakis, Eric Price

Proceedings of the 38th International Conference on Machine Learning, 2021

Instance-Optimal Compressed Sensing via Posterior Sampling.

Ajil Jalal, Sushrut Karmalkar, Alex Dimakis, Eric Price

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning.

Aravind Gollakota, Sushrut Karmalkar, Adam R. Klivans

CoRR, 2020

Robustly Learning any Clusterable Mixture of Gaussians.

Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar

CoRR, 2020

On the Power of Compressed Sensing with Generative Models.

Akshay Kamath, Eric Price, Sushrut Karmalkar

Proceedings of the 37th International Conference on Machine Learning, 2020

Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent.

Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans

Proceedings of the 37th International Conference on Machine Learning, 2020

Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures.

Ainesh Bakshi, Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar, Pravesh K. Kothari

Proceedings of the 61st IEEE Annual Symposium on Foundations of Computer Science, 2020

Approximation Schemes for ReLU Regression.

Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi

Proceedings of the Conference on Learning Theory, 2020

2019

Lower Bounds for Compressed Sensing with Generative Models.

Akshay Kamath, Sushrut Karmalkar, Eric Price

CoRR, 2019

Compressed Sensing with Adversarial Sparse Noise via L1 Regression.

Sushrut Karmalkar, Eric Price

Proceedings of the 2nd Symposium on Simplicity in Algorithms, 2019

List-decodable Linear Regression.

Sushrut Karmalkar, Adam R. Klivans, Pravesh Kothari

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals.

Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering.

Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Eric Price, Alistair Stewart

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Fourier Entropy-Influence Conjecture for Random Linear Threshold Functions.

Sourav Chakraborty, Sushrut Karmalkar, Srijita Kundu, Satyanarayana V. Lokam, Nitin Saurabh

Proceedings of the LATIN 2018: Theoretical Informatics, 2018

Depth separation and weight-width trade-offs for sigmoidal neural networks.

Amit Deshpande, Navin Goyal, Sushrut Karmalkar

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

On Robust Concepts and Small Neural Nets.

Amit Deshpande, Sushrut Karmalkar

Proceedings of the 5th International Conference on Learning Representations, 2017

Robust Polynomial Regression up to the Information Theoretic Limit.

Daniel Kane, Sushrut Karmalkar, Eric Price

Proceedings of the 58th IEEE Annual Symposium on Foundations of Computer Science, 2017