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.

Preprints:

**Geometry-Aware Gradient Algorithms for Neural Architecture Search.**

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

[arXiv]
[code]
[blog]

**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]