Mikhail Khodak

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 50

Top Topics

Neural Architecture

Parameter Tuning

Annotated Corpus

Top Venues

Publications

Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. ICLR (2024)
Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. CoRR (2023)
Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu
Meta-Learning Adversarial Bandit Algorithms. CoRR (2023)
Keegan Harris, Ioannis Anagnostides, Gabriele Farina, Mikhail Khodak, Steven Wu, Tuomas Sandholm
Meta-Learning in Games. ICLR (2023)
Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
AANG : Automating Auxiliary Learning. ICLR (2023)
Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
Cross-Modal Fine-Tuning: Align then Refine. ICML (2023)
Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii
Learning-augmented private algorithms for multiple quantile release. ICML (2023)
Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith
On Noisy Evaluation in Federated Hyperparameter Tuning. MLSys (2023)
Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
Cross-Modal Fine-Tuning: Align then Refine. CoRR (2023)
Kareem Amin, Travis Dick, Mikhail Khodak, Sergei Vassilvitskii
Private Algorithms with Private Predictions. CoRR (2022)
Maria-Florina Balcan, Keegan Harris, Mikhail Khodak, Zhiwei Steven Wu
Meta-Learning Adversarial Bandits. CoRR (2022)
Renbo Tu, Nicholas Roberts, Mikhail Khodak, Junhong Shen, Frederic Sala, Ameet Talwalkar
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks. NeurIPS (2022)
Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith
On Noisy Evaluation in Federated Hyperparameter Tuning. CoRR (2022)
Junhong Shen, Mikhail Khodak, Ameet Talwalkar
Efficient Architecture Search for Diverse Tasks. NeurIPS (2022)
Keegan Harris, Ioannis Anagnostides, Gabriele Farina, Mikhail Khodak, Zhiwei Steven Wu, Tuomas Sandholm
Meta-Learning in Games. CoRR (2022)
Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
AANG: Automating Auxiliary Learning. CoRR (2022)
Junhong Shen, Mikhail Khodak, Ameet Talwalkar
Efficient Architecture Search for Diverse Tasks. CoRR (2022)
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii
Learning Predictions for Algorithms with Predictions. CoRR (2022)
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
Provably tuning the ElasticNet across instances. CoRR (2022)
Mikhail Khodak, Neil A. Tenenholtz, Lester Mackey, Nicolò Fusi
Initialization and Regularization of Factorized Neural Layers. CoRR (2021)
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
Learning-to-learn non-convex piecewise-Lipschitz functions. NeurIPS (2021)
Renbo Tu, Mikhail Khodak, Nicholas Roberts, Ameet Talwalkar
NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search. CoRR (2021)
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
Learning-to-learn non-convex piecewise-Lipschitz functions. CoRR (2021)
Nicholas Roberts, Samuel Guo, Cong Xu, Ameet Talwalkar, David Lander, Lvfang Tao, Linhang Cai, Shuaicheng Niu, Jianyu Heng, Hongyang Qin, Minwen Deng, Johannes Hog, Alexander Pfefferle, Sushil Ammanaghatta Shivakumar, Arjun Krishnakumar, Yubo Wang, Rhea Sukthanker, Frank Hutter, Euxhen Hasanaj, Tien-Dung Le, Mikhail Khodak, Yuriy Nevmyvaka, Kashif Rasul, Frederic Sala, Anderson Schneider, Junhong Shen, Evan R. Sparks
AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. NeurIPS (Competition and Demos) (2021)
Mikhail Khodak, Neil A. Tenenholtz, Lester Mackey, Nicolò Fusi
Initialization and Regularization of Factorized Neural Layers. ICLR (2021)
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. NeurIPS (2021)
Liam Li, Mikhail Khodak, Nina Balcan, Ameet Talwalkar
Geometry-Aware Gradient Algorithms for Neural Architecture Search. ICLR (2021)
Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar
Rethinking Neural Operations for Diverse Tasks. CoRR (2021)
Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar
Rethinking Neural Operations for Diverse Tasks. NeurIPS (2021)
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. CoRR (2021)
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, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14 (1-2) (2021)
Liam Li, Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
Geometry-Aware Gradient Algorithms for Neural Architecture Search. CoRR (2020)
Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar
Differentially Private Meta-Learning. ICLR (2020)
Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. ICML (2020)
Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. CoRR (2020)
Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, Nikunj Saunshi
A Theoretical Analysis of Contrastive Unsupervised Representation Learning. CoRR (2019)
Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar
Provable Guarantees for Gradient-Based Meta-Learning. ICML (2019)
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
Adaptive Gradient-Based Meta-Learning Methods. CoRR (2019)
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
Provable Guarantees for Gradient-Based Meta-Learning. CoRR (2019)
Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, Hrishikesh Khandeparkar
A Theoretical Analysis of Contrastive Unsupervised Representation Learning. ICML (2019)
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, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. CoRR (2019)
Mikhail Khodak, Maria-Florina Balcan, Ameet S. Talwalkar
Adaptive Gradient-Based Meta-Learning Methods. NeurIPS (2019)
Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar
Differentially Private Meta-Learning. CoRR (2019)
Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. ACL (1) (2018)
Sanjeev Arora, Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli
A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs. ICLR (Poster) (2018)
Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli
A Large Self-Annotated Corpus for Sarcasm. LREC (2018)
Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. CoRR (2018)
Mikhail Khodak, Liang Zheng, Andrew S. Lan, Carlee Joe-Wong, Mung Chiang
Learning Cloud Dynamics to Optimize Spot Instance Bidding Strategies. INFOCOM (2018)
Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli
A Large Self-Annotated Corpus for Sarcasm. CoRR (2017)
Mikhail Khodak, Andrej Risteski, Christiane Fellbaum, Sanjeev Arora
Extending and Improving Wordnet via Unsupervised Word Embeddings. CoRR (2017)