All alphabetical author orderings were determined alphabetically. "*" denotes equal contribution.

Meta-Learning Adversarial Bandit Algorithms. NeurIPS 2023.

Mikhail Khodak*, Ilya Osadchiy*, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu.
[paper] [arXiv]

Learning-Augmented Private Algorithms for Multiple Quantile Release. ICML 2023.

Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii.
[paper] [arXiv] [code]

Cross-Modal Fine-Tuning: Align then Refine. ICML 2023.

Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar.
[paper] [arXiv] [code] [slides]

On Noisy Evaluation in Federated Hyperparameter Tuning. MLSys 2023.

Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith.
[paper] [arXiv] [blog]

Meta-Learning in Games. ICLR 2023.

Keegan Harris*, Ioannis Anagnostides*, Gabriele Farina, Mikhail Khodak, Zhiwei Steven Wu, Tuomas Sandholm.
[paper] [arXiv]

AANG: Automating Auxiliary Learning. ICLR 2023.

Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar.
[paper] [arXiv] [code]

Provably Tuning the ElasticNet Across Instances. NeurIPS 2022.

Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar.
[paper] [arXiv] [talk]

Efficient Architecture Search for Diverse Tasks. NeurIPS 2022.

Junhong Shen*, Mikhail Khodak*, Ameet Talwalkar.
[paper] [arXiv] [slides] [code] [blog]

Learning Predictions for Algorithms with Predictions. NeurIPS 2022.

Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii.
[paper] [arXiv] [poster] [talk]

NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks. NeurIPS 2022 (Datasets and Benchmarks Track).

Renbo Tu*, Nicholas Roberts*, Mikhail Khodak, Junhong Shen, Frederic Sala, Ameet Talwalkar.
[paper] [arXiv] [code] [website]

A Deeper Look at Zero-Cost Proxies for Lightweight NAS. ICLR 2022 (Blog Track).

Colin White, Mikhail Khodak, Renbo Tu, Shital Shah, Sébastien Bubeck, Debadeepta Dey.

Rethinking Neural Operations for Diverse Tasks. NeurIPS 2021.

Nicholas Roberts*, Mikhail Khodak*, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar.
[paper] [arXiv] [code] [slides] [talk] [Python package]

Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. NeurIPS 2021.

Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar.
[paper] [arXiv] [code] [poster] [slides] [talk]

Learning-to-Learn Non-Convex Piecewise-Lipschitz Functions. NeurIPS 2021.

Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar.
[paper] [arXiv]

Geometry-Aware Gradient Algorithms for Neural Architecture Search. ICLR 2021.

Liam Li*, Mikhail Khodak*, Maria-Florina Balcan, Ameet Talwalkar.
[paper] [arXiv] [slides] [code] [blog] [talk] [Determined]

Initialization and Regularization of Factorized Neural Layers. ICLR 2021.

Mikhail Khodak, Neil Tenenholtz, Lester Mackey, Nicolò Fusi.
[paper] [arXiv] [code] [blog] [talk]

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

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

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]

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] [R package]

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]

A Large Self-Annotated Corpus for Sarcasm. LREC 2018.

Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli.
[paper] [arXiv] [code] [data] [CBC] [Quartz] [The Register]

Learning Cloud Dynamics to Optimize Spot Instance Bidding Strategies. INFOCOM 2018.

Mikhail Khodak, Liang Zheng, Andrew S. Lan, Carlee Joe-Wong, Mung Chiang.
[paper] [supplement] [slides]