Daniel Levy

Affiliations:
  • OpenAI, USA
  • Stanford University, Department of Computer Science, USA (PhD 2021)


According to our database1, Daniel Levy authored at least 15 papers between 2016 and 2024.

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Bibliography

2024
OpenAI o1 System Card.
CoRR, 2024

2023
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.
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Trans. Mach. Learn. Res., 2023

2021
Advancing optimization for modern machine learning.
PhD thesis, 2021

Learning with User-Level Privacy.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Adapting to function difficulty and growth conditions in private optimization.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Distributionally Robust Multilingual Machine Translation.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

2020
Large-Scale Methods for Distributionally Robust Optimization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Necessary and Sufficient Conditions for Adaptive, Mirror, and Standard Gradient Methods.
CoRR, 2019

Necessary and Sufficient Geometries for Gradient Methods.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
Bayesian optimization and attribute adjustment.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Generalizing Hamiltonian Monte Carlo with Neural Networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Data Noising as Smoothing in Neural Network Language Models.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks.
CoRR, 2016


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