Sushant Agarwal

I am a graduate student in the Computer Science department at Northeastern University, where I am advised by Jonathan Ullman. Prior to this, I graduated with a Master's degree in Computer Science from the University of Waterloo, where I was advised by Shai Ben-David. Previously, I did my Bachelor's in Mathematics and Theoretical Computer Science from Chennai Mathematical Institute.

I am broadly interested in theoretical machine learning, and have recently been focusing on ethical issues that arise in machine learning models, such as fairness, interpretability, privacy, and robustness. I am motivated by problems that are theoretically challenging, while also being practically relevant.

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Private Mean Estimation with Person-Level Differential Privacy
Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis, Rose Silver, Jon Ullman

On the Power of Randomization in Fair Classification and Representation
Sushant Agarwal, Amit Deshpande
FAccT 2022 [pdf]

Towards the Unification and Robustness of Post-hoc Explanations
Sushant Agarwal, Shahin Jabbari, C. Agarwal*, S. Upadhyay*, Hima Lakkaraju, Steven Wu (CO)
ICML 2021 [pdf]
FORC 2022 (non-archival track) [pdf]

Open Problem: Are all VC-classes CPAC learnable?
Sushant Agarwal, Nivasini A., Shai Ben-David, Tosca Lechner, Ruth Urner
Open Problems @ COLT 2021 [pdf]

On Learnability with Computable Learners
Sushant Agarwal, Nivasini A., Shai Ben-David, Tosca Lechner, Ruth Urner
ALT 2020 [pdf]

On Trade-offs between Fairness, Interpretability, and Privacy in Classification
Sushant Agarwal
Master's Thesis [pdf]
AAAI 2021 workshop on Explainable Agency in AI [pdf]
IJCAI 2021 workshop on AI for Social Good [pdf 1][pdf 2]

Impossibility Results for Fair Representations
Tosca Lechner, Shai Ben-David, Sushant Agarwal, Nivasini A. (CO)

Authorships are in alphabetical order, unless indicated. CO denotes contributional order. * indicates equal contribution.
Thanks to Jon Barron for the template.