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.


Adaptive Gradient-Based Meta-Learning Methods. To Appear in NeurIPS 2019.

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

Differentially Private Meta-Learning.

Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar.

Recent Papers:

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

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

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

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

A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. ACL 2018.

Mikhail Khodak*, Nikunj Saunshi*, Yingyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora.
[paper] [arXiv] [slides] [code] [data] [blog] [talk]

A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs. ICLR 2018.

Sanjeev Arora, Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli.
[paper] [poster] [slides] [code] [data] [blog]