Extending and Improving Wordnet via Unsupervised Word Embeddings. - Mikhail Khodak; Andrej Risteski; Christiane Fellbaum; Sanjeev Arora (CoRR, 2017)

A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. - Mikhail Khodak; Nikunj Saunshi; Yingyu Liang; Tengyu Ma 001; Brandon Stewart; Sanjeev Arora (ACL (1), 2018)

A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs. - Sanjeev Arora; Mikhail Khodak; Nikunj Saunshi; Kiran Vodrahalli (ICLR (Poster), 2018)

Learning Cloud Dynamics to Optimize Spot Instance Bidding Strategies. - Mikhail Khodak; Liang Zheng 002; Andrew S. Lan; Carlee Joe-Wong; Mung Chiang (INFOCOM, 2018)

A Large Self-Annotated Corpus for Sarcasm. - Mikhail Khodak; Nikunj Saunshi; Kiran Vodrahalli (LREC, 2018)

Provable Guarantees for Gradient-Based Meta-Learning. - Maria-Florina Balcan; Mikhail Khodak; Ameet Talwalkar (ICML, 2019)

A Theoretical Analysis of Contrastive Unsupervised Representation Learning. - Nikunj Saunshi; Orestis Plevrakis; Sanjeev Arora; Mikhail Khodak; Hrishikesh Khandeparkar (ICML, 2019)

Adaptive Gradient-Based Meta-Learning Methods. - Mikhail Khodak; Maria-Florina Balcan; Ameet Talwalkar (NeurIPS, 2019)

Advances and Open Problems in Federated Learning. - Peter Kairouz; H. Brendan McMahan; Brendan Avent; Aurélien Bellet; Mehdi Bennis; Arjun Nitin Bhagoji; Kallista A. Bonawitz; Zachary Charles; Graham Cormode; Rachel Cummings; Rafael G. L. D'Oliveira; Salim El Rouayheb; et al. (CoRR, 2019)

Differentially Private Meta-Learning. - Jeffrey Li; Mikhail Khodak; Sebastian Caldas; Ameet Talwalkar (ICLR, 2020)

A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. - Nikunj Saunshi; Yi Zhang 074; Mikhail Khodak; Sanjeev Arora (ICML, 2020)

Initialization and Regularization of Factorized Neural Layers. - Mikhail Khodak; Neil A. Tenenholtz; Lester Mackey; Nicolò Fusi (ICLR, 2021)

Geometry-Aware Gradient Algorithms for Neural Architecture Search. - Liam Li; Mikhail Khodak; Nina Balcan; Ameet Talwalkar (ICLR, 2021)

Learning-to-learn non-convex piecewise-Lipschitz functions. - Maria-Florina Balcan; Mikhail Khodak; Dravyansh Sharma; Ameet Talwalkar (NeurIPS, 2021)

Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. - Mikhail Khodak; Renbo Tu; Tian Li 005; Liam Li; Maria-Florina Balcan; Virginia Smith; Ameet Talwalkar (NeurIPS, 2021)

AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. - Nicholas Roberts; Samuel Guo; Cong Xu; Ameet Talwalkar; David Lander; Lvfang Tao; Linhang Cai; Shuaicheng Niu; Jianyu Heng; Hongyang Qin; Minwen Deng; Johannes Hog; et al. (NeurIPS (Competition and Demos), 2021)

Rethinking Neural Operations for Diverse Tasks. - Nicholas Roberts; Mikhail Khodak; Tri Dao; Liam Li; Christopher Ré; Ameet Talwalkar (NeurIPS, 2021)

NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search. - Renbo Tu; Mikhail Khodak; Nicholas Roberts; Ameet Talwalkar (CoRR, 2021)

Advances and Open Problems in Federated Learning. - Peter Kairouz; H. Brendan McMahan; Brendan Avent; Aurélien Bellet; Mehdi Bennis; Arjun Nitin Bhagoji; Kallista A. Bonawitz; Zachary Charles; Graham Cormode; Rachel Cummings; Rafael G. L. D'Oliveira; Hubert Eichner; et al. (Found. Trends Mach. Learn., 2021)

Efficient Architecture Search for Diverse Tasks. - Junhong Shen; Mikhail Khodak; Ameet Talwalkar (NeurIPS, 2022)

NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks. - Renbo Tu; Nicholas Roberts; Mikhail Khodak; Junhong Shen; Frederic Sala; Ameet Talwalkar (NeurIPS, 2022)

Learning Predictions for Algorithms with Predictions. - Mikhail Khodak; Maria-Florina Balcan; Ameet Talwalkar; Sergei Vassilvitskii (CoRR, 2022)

Meta-Learning Adversarial Bandits. - Maria-Florina Balcan; Keegan Harris; Mikhail Khodak; Zhiwei Steven Wu (CoRR, 2022)

Provably tuning the ElasticNet across instances. - Maria-Florina Balcan; Mikhail Khodak; Dravyansh Sharma; Ameet Talwalkar (CoRR, 2022)

Private Algorithms with Private Predictions. - Kareem Amin 002; Travis Dick; Mikhail Khodak; Sergei Vassilvitskii (CoRR, 2022)

AANG : Automating Auxiliary Learning. - Lucio M. Dery; Paul Michel; Mikhail Khodak; Graham Neubig; Ameet Talwalkar (ICLR, 2023)

Meta-Learning in Games. - Keegan Harris; Ioannis Anagnostides; Gabriele Farina; Mikhail Khodak; Steven Wu 001; Tuomas Sandholm (ICLR, 2023)

Learning-augmented private algorithms for multiple quantile release. - Mikhail Khodak; Kareem Amin 002; Travis Dick; Sergei Vassilvitskii (ICML, 2023)

Cross-Modal Fine-Tuning: Align then Refine. - Junhong Shen; Liam Li; Lucio M. Dery; Corey Staten; Mikhail Khodak; Graham Neubig; Ameet Talwalkar (ICML, 2023)

On Noisy Evaluation in Federated Hyperparameter Tuning. - Kevin Kuo; Pratiksha Thaker; Mikhail Khodak; John Nguyen; Daniel Jiang; Ameet Talwalkar; Virginia Smith (MLSys, 2023)

Meta-Learning Adversarial Bandit Algorithms. - Mikhail Khodak; Ilya Osadchiy; Keegan Harris; Maria-Florina Balcan; Kfir Y. Levy; Ron Meir; Zhiwei Steven Wu (CoRR, 2023)

Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. - Mikhail Khodak; Edmond Chow; Maria-Florina Balcan; Ameet Talwalkar (ICLR, 2024)

SureMap: Simultaneous Mean Estimation for Single-Task and Multi-Task Disaggregated Evaluation. - Mikhail Khodak; Lester Mackey; Alexandra Chouldechova; Miroslav Dudík (CoRR, 2024)

Specialized Foundation Models Struggle to Beat Supervised Baselines. - Zongzhe Xu; Ritvik Gupta; Wenduo Cheng; Alexander Shen 003; Junhong Shen; Ameet Talwalkar; Mikhail Khodak (ICLR, 2025)

L2G: Repurposing Language Models for Genomics Tasks. - Wenduo Cheng; Junhong Shen; Mikhail Khodak; Jian Ma; Ameet Talwalkar (Trans. Mach. Learn. Res., 2025)

Attention Retrieves, MLP Memorizes: Disentangling Trainable Components in the Transformer. - Yihe Dong; Lorenzo Noci; Mikhail Khodak; Mufan Bill Li (CoRR, 2025)