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]
[blog]
[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.
[post]
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]