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2020 – today
- 2021
[b1]Daniel Levy:
Advancing optimization for modern machine learning. Stanford University, USA, 2021- [c10]Chunting Zhou, Daniel Levy, Xian Li, Marjan Ghazvininejad, Graham Neubig:
Distributionally Robust Multilingual Machine Translation. EMNLP (1) 2021: 5664-5674 - [c9]Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh:
Learning with User-Level Privacy. NeurIPS 2021: 12466-12479 - [c8]Hilal Asi, Daniel Levy, John C. Duchi:
Adapting to function difficulty and growth conditions in private optimization. NeurIPS 2021: 19069-19081 - [i10]Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh:
Learning with User-Level Privacy. CoRR abs/2102.11845 (2021) - [i9]Hilal Asi, Daniel Levy, John C. Duchi:
Adapting to Function Difficulty and Growth Conditions in Private Optimization. CoRR abs/2108.02391 (2021) - [i8]Chunting Zhou, Daniel Levy, Xian Li, Marjan Ghazvininejad, Graham Neubig:
Distributionally Robust Multilingual Machine Translation. CoRR abs/2109.04020 (2021) - 2020
- [c7]Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford:
Large-Scale Methods for Distributionally Robust Optimization. NeurIPS 2020 - [i7]Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford:
Large-Scale Methods for Distributionally Robust Optimization. CoRR abs/2010.05893 (2020)
2010 – 2019
- 2019
- [c6]Daniel Levy, John C. Duchi:
Necessary and Sufficient Geometries for Gradient Methods. NeurIPS 2019: 11491-11501 - [i6]Daniel Levy, John C. Duchi:
Necessary and Sufficient Conditions for Adaptive, Mirror, and Standard Gradient Methods. CoRR abs/1909.10455 (2019) - 2018
- [c5]Daniel Levy, Stefano Ermon:
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces. AAAI 2018: 3474-3481 - [c4]Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein:
Generalizing Hamiltonian Monte Carlo with Neural Networks. ICLR (Poster) 2018 - [c3]Stephan Eismann, Daniel Levy, Rui Shu, Stefan Bartzsch, Stefano Ermon:
Bayesian optimization and attribute adjustment. UAI 2018: 1042-1052 - [i5]Edward Chou, Josh Beal, Daniel Levy, Serena Yeung, Albert Haque, Li Fei-Fei:
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference. CoRR abs/1811.09953 (2018) - 2017
- [c2]Stephen Mussmann, Daniel Levy, Stefano Ermon:
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search. UAI 2017 - [i4]Stephen Mussmann, Daniel Levy, Stefano Ermon:
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search. CoRR abs/1707.03372 (2017) - [i3]Daniel Levy, Stefano Ermon:
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces. CoRR abs/1711.08068 (2017) - [i2]Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein:
Generalizing Hamiltonian Monte Carlo with Neural Networks. CoRR abs/1711.09268 (2017) - 2013
- [j1]Christopher J. Henry, Sheela Ramanna, Daniel Levy:
Quantifying Nearness in Visual Spaces. Cybern. Syst. 44(1): 38-56 (2013) - [c1]Elizabeth Ann Patitsas, Daniel Levy:
Dr. Horrible's fork bomb: a lab for introducing security issues in CS2. ITiCSE 2013: 318
2000 – 2009
- 2006
- [i1]Shmuel Friedland, Daniel Levy:
A polynomial-time approximation algorithm for the number of k-matchings in bipartite graphs. CoRR abs/cs/0607135 (2006)
Coauthor Index
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last updated on 2022-09-06 03:54 CEST by the dblp team
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