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ORCIDMikhail Khodak
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2020 – today
- 2024
[c23]Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar:
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. ICLR 2024- [i28]Zongzhe Xu, Ritvik Gupta, Wenduo Cheng, Alexander Shen, Junhong Shen, Ameet Talwalkar, Mikhail Khodak:
Specialized Foundation Models Struggle to Beat Supervised Baselines. CoRR abs/2411.02796 (2024) - [i27]Mikhail Khodak, Lester Mackey, Alexandra Chouldechova, Miroslav Dudík:
SureMap: Simultaneous Mean Estimation for Single-Task and Multi-Task Disaggregated Evaluation. CoRR abs/2411.09730 (2024) - 2023
- [c22]Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar:
AANG : Automating Auxiliary Learning. ICLR 2023 - [c21]Keegan Harris, Ioannis Anagnostides, Gabriele Farina, Mikhail Khodak, Steven Wu, Tuomas Sandholm:
Meta-Learning in Games. ICLR 2023 - [c20]Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii:
Learning-augmented private algorithms for multiple quantile release. ICML 2023: 16344-16376 - [c19]Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar:
Cross-Modal Fine-Tuning: Align then Refine. ICML 2023: 31030-31056 - [c18]Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith:
On Noisy Evaluation in Federated Hyperparameter Tuning. MLSys 2023 - [i26]Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar:
Cross-Modal Fine-Tuning: Align then Refine. CoRR abs/2302.05738 (2023) - [i25]Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu:
Meta-Learning Adversarial Bandit Algorithms. CoRR abs/2307.02295 (2023) - [i24]Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar:
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. CoRR abs/2310.02246 (2023) - 2022
- [c17]Junhong Shen, Mikhail Khodak, Ameet Talwalkar:
Efficient Architecture Search for Diverse Tasks. NeurIPS 2022 - [c16]Renbo Tu, Nicholas Roberts, Mikhail Khodak, Junhong Shen, Frederic Sala, Ameet Talwalkar:
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks. NeurIPS 2022 - [i23]Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii:
Learning Predictions for Algorithms with Predictions. CoRR abs/2202.09312 (2022) - [i22]Junhong Shen, Mikhail Khodak, Ameet Talwalkar:
Efficient Architecture Search for Diverse Tasks. CoRR abs/2204.07554 (2022) - [i21]Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar:
AANG: Automating Auxiliary Learning. CoRR abs/2205.14082 (2022) - [i20]Maria-Florina Balcan, Keegan Harris, Mikhail Khodak, Zhiwei Steven Wu:
Meta-Learning Adversarial Bandits. CoRR abs/2205.14128 (2022) - [i19]Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar:
Provably tuning the ElasticNet across instances. CoRR abs/2207.10199 (2022) - [i18]Keegan Harris, Ioannis Anagnostides, Gabriele Farina, Mikhail Khodak, Zhiwei Steven Wu, Tuomas Sandholm:
Meta-Learning in Games. CoRR abs/2209.14110 (2022) - [i17]Kareem Amin, Travis Dick, Mikhail Khodak, Sergei Vassilvitskii:
Private Algorithms with Private Predictions. CoRR abs/2210.11222 (2022) - [i16]Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith:
On Noisy Evaluation in Federated Hyperparameter Tuning. CoRR abs/2212.08930 (2022) - 2021
- [j1]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): 1-210 (2021) - [c15]Mikhail Khodak, Neil A. Tenenholtz, Lester Mackey, Nicolò Fusi:
Initialization and Regularization of Factorized Neural Layers. ICLR 2021 - [c14]Liam Li, Mikhail Khodak, Nina Balcan, Ameet Talwalkar:
Geometry-Aware Gradient Algorithms for Neural Architecture Search. ICLR 2021 - [c13]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 Randall Sparks:
AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. NeurIPS (Competition and Demos) 2021: 151-170 - [c12]Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar:
Learning-to-learn non-convex piecewise-Lipschitz functions. NeurIPS 2021: 15056-15069 - [c11]Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar:
Rethinking Neural Operations for Diverse Tasks. NeurIPS 2021: 15855-15869 - [c10]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: 19184-19197 - [i15]Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar:
Rethinking Neural Operations for Diverse Tasks. CoRR abs/2103.15798 (2021) - [i14]Mikhail Khodak, Neil A. Tenenholtz, Lester Mackey, Nicolò Fusi:
Initialization and Regularization of Factorized Neural Layers. CoRR abs/2105.01029 (2021) - [i13]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 abs/2106.04502 (2021) - [i12]Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar:
Learning-to-learn non-convex piecewise-Lipschitz functions. CoRR abs/2108.08770 (2021) - [i11]Renbo Tu, Mikhail Khodak, Nicholas Roberts, Ameet Talwalkar:
NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search. CoRR abs/2110.05668 (2021) - 2020
- [c9]Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar:
Differentially Private Meta-Learning. ICLR 2020 - [c8]Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora:
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. ICML 2020: 8512-8521 - [i10]Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora:
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. CoRR abs/2002.11172 (2020) - [i9]Liam Li, Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Geometry-Aware Gradient Algorithms for Neural Architecture Search. CoRR abs/2004.07802 (2020)
2010 – 2019
- 2019
- [c7]Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar:
Provable Guarantees for Gradient-Based Meta-Learning. ICML 2019: 424-433 - [c6]Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, Hrishikesh Khandeparkar:
A Theoretical Analysis of Contrastive Unsupervised Representation Learning. ICML 2019: 5628-5637 - [c5]Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Adaptive Gradient-Based Meta-Learning Methods. NeurIPS 2019: 5915-5926 - [i8]Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, Nikunj Saunshi:
A Theoretical Analysis of Contrastive Unsupervised Representation Learning. CoRR abs/1902.09229 (2019) - [i7]Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Provable Guarantees for Gradient-Based Meta-Learning. CoRR abs/1902.10644 (2019) - [i6]Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Adaptive Gradient-Based Meta-Learning Methods. CoRR abs/1906.02717 (2019) - [i5]Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar:
Differentially Private Meta-Learning. CoRR abs/1909.05830 (2019) - [i4]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 abs/1912.04977 (2019) - 2018
- [c4]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: 12-22 - [c3]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 - [c2]Mikhail Khodak, Liang Zheng, Andrew S. Lan, Carlee Joe-Wong, Mung Chiang:
Learning Cloud Dynamics to Optimize Spot Instance Bidding Strategies. INFOCOM 2018: 2762-2770 - [c1]Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli:
A Large Self-Annotated Corpus for Sarcasm. LREC 2018 - [i3]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 abs/1805.05388 (2018) - 2017
- [i2]Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli:
A Large Self-Annotated Corpus for Sarcasm. CoRR abs/1704.05579 (2017) - [i1]Mikhail Khodak, Andrej Risteski, Christiane Fellbaum, Sanjeev Arora:
Extending and Improving Wordnet via Unsupervised Word Embeddings. CoRR abs/1705.00217 (2017)
Coauthor Index
aka: Nina Balcan
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