Sushant Agarwal

Hello, I am a PhD 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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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.