Estimation from indirect supervision with linear moments ICML 2016 With Roy Frostig, John Duchi and Percy Liang
A Reinforcement Learning approach to online learning of decision trees EWRL 2015 With Abhinav Garlapati, Vaishnavh Nagarajan and Balaraman Ravindran
Probabilitistic dependency networks for prediction and diagnostics TRB Annual Meeting 2014 With Narayanan U. Edakunni, Abhishek Tripathi, John Handley and Fredric Roulland
I am primarily interested in providing provable guarantees for different Machine Learning problems and algorithms. I am currently exploring guarantees for security of ML systems. As ML systems become more powerful and widely employed, it's important to have guarantees on their performance in presence of adversaries, particularly in high risk applications like self-driving cars and medicine. How can we ensure robustness to adversarial corruptions of inputs to the deployed classifier?
On a related note, I am also looking at extrapolating properties of the unseen parts of the distribution. How much training data do we need to collect to ensure that with high probability, every element we see at deployment is already seen during training?
More broadly, I am interested in understanding other goals that ML systems should satisfy (besides prediction accuracy) like fairness, interpretability and privacy .
On the theoretical side, I am interested in non-convex optimization and understanding the conditions under which local methods can solve non-convex objectives arising in Machine Learning problems. I have previously worked on providing guarantees for learning mixture of gaussians from streaming data. I am also excited about new methods to circumvent computational challenges of non-convexity. As an undergraduate, I worked on using method of moments to perform computationally efficient estimation under indirect supervision.