Aditi Raghunathan

Aditi Raghunathan



I am a PhD student at Stanford University.

Email: aditir'at'stanford'dot'edu


Publications ~ Research Interests ~

Bio

I am a first year PhD student in Computer Science at Stanford University. As part of the rotation system at Stanford, I am fortunate to work with Greg Valiant, James Zou, Percy Liang and Moses Charikar .
Previously, I obtained my B.Tech. (Hons.) in Computer Science from IIT Madras in 2016.

Learning mixture of gaussians from streaming data
Under submission.
With Prateek Jain and Ravishankar Krishnaswamy

Estimating the unseen from multiple populations
ICML 2017
With Greg Valiant and James Zou

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.