I am a PhD student in Carnegie Mellon University's Computer Science Department advised by Nina Balcan and Ameet Talwalkar. My research focuses on foundations and applications of machine learning, most recently meta-learning, unsupervised representation learning, and natural language processing. Previously, I received an AB in Mathematics and an MSE in Computer Science from Princeton University, where I worked with Sanjeev Arora.


Geometry-Aware Gradient Algorithms for Neural Architecture Search.

Liam Li*, Mikhail Khodak*, Maria-Florina Balcan, Ameet Talwalkar.

A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. To Appear in ICML 2020.

Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora.
[arXiv] [talk]

Recent Papers:

Differentially Private Meta-Learning. ICLR 2020.

Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar.
[paper] [arXiv] [slides]

Adaptive Gradient-Based Meta-Learning Methods. NeurIPS 2019.

Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar.
[paper] [arXiv] [poster] [slides] [code] [blog] [talk]

A Theoretical Analysis of Contrastive Unsupervised Representation Learning. ICML 2019.

Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, Nikunj Saunshi.
[paper] [arXiv] [poster] [slides] [data] [blog] [talk]

Provable Guarantees for Gradient-Based Meta-Learning. ICML 2019.

Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar.
[paper] [arXiv] [poster] [code] [data]