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ORCIDJonathan Lorraine
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Conference and Workshop Papers
- 2024
[c8]Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas:
Graph Metanetworks for Processing Diverse Neural Architectures. ICLR 2024- 2023
- [c7]Jonathan Lorraine, Kevin Xie, Xiaohui Zeng, Chen-Hsuan Lin, Towaki Takikawa, Nicholas Sharp, Tsung-Yi Lin, Ming-Yu Liu, Sanja Fidler, James Lucas:
ATT3D: Amortized Text-to-3D Object Synthesis. ICCV 2023: 17900-17910 - 2022
- [c6]Jonathan P. Lorraine, David Acuna, Paul Vicol, David Duvenaud:
Complex Momentum for Optimization in Games. AISTATS 2022: 7742-7765 - [c5]Jonathan Lorraine, Paul Vicol, Jack Parker-Holder, Tal Kachman, Luke Metz, Jakob N. Foerster:
Lyapunov Exponents for Diversity in Differentiable Games. AAMAS 2022: 842-852 - [c4]Paul Vicol, Jonathan P. Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse:
On Implicit Bias in Overparameterized Bilevel Optimization. ICML 2022: 22234-22259 - 2021
- [c3]Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud:
Meta-learning to Improve Pre-training. NeurIPS 2021: 23231-23244 - 2020
- [c2]Jonathan Lorraine, Paul Vicol, David Duvenaud:
Optimizing Millions of Hyperparameters by Implicit Differentiation. AISTATS 2020: 1540-1552 - 2019
- [c1]Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse:
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. ICLR (Poster) 2019
Informal and Other Publications
- 2024
- [i18]Kevin Xie, Jonathan Lorraine, Tianshi Cao, Jun Gao, James Lucas, Antonio Torralba, Sanja Fidler, Xiaohui Zeng:
LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis. CoRR abs/2403.15385 (2024) - [i17]Juhan Bae, Wu Lin, Jonathan Lorraine, Roger Grosse:
Training Data Attribution via Approximate Unrolled Differentiation. CoRR abs/2405.12186 (2024) - [i16]Nikhil Mehta, Jonathan Lorraine, Steve Masson, Ramanathan Arunachalam, Zaid Pervaiz Bhat, James Lucas, Arun George Zachariah:
Improving Hyperparameter Optimization with Checkpointed Model Weights. CoRR abs/2406.18630 (2024) - [i15]Jonathan Lorraine:
Scalable Nested Optimization for Deep Learning. CoRR abs/2407.01526 (2024) - [i14]Jonathan Lorraine, Safwan Hossain:
JacNet: Learning Functions with Structured Jacobians. CoRR abs/2408.13237 (2024) - 2023
- [i13]Jonathan Lorraine, Kevin Xie, Xiaohui Zeng, Chen-Hsuan Lin, Towaki Takikawa, Nicholas Sharp, Tsung-Yi Lin, Ming-Yu Liu, Sanja Fidler, James Lucas:
ATT3D: Amortized Text-to-3D Object Synthesis. CoRR abs/2306.07349 (2023) - [i12]Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas:
Graph Metanetworks for Processing Diverse Neural Architectures. CoRR abs/2312.04501 (2023) - [i11]Michael R. Zhang, Nishkrit Desai, Juhan Bae, Jonathan Lorraine, Jimmy Ba:
Using Large Language Models for Hyperparameter Optimization. CoRR abs/2312.04528 (2023) - 2022
- [i10]Jonathan Lorraine, Nihesh Anderson, Chansoo Lee, Quentin de Laroussilhe, Mehadi Hassen:
Task Selection for AutoML System Evaluation. CoRR abs/2208.12754 (2022) - [i9]Paul Vicol, Jonathan Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse:
On Implicit Bias in Overparameterized Bilevel Optimization. CoRR abs/2212.14032 (2022) - 2021
- [i8]Jonathan Lorraine
, David Acuna, Paul Vicol, David Duvenaud:
Complex Momentum for Learning in Games. CoRR abs/2102.08431 (2021) - [i7]Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud:
Meta-Learning to Improve Pre-Training. CoRR abs/2111.01754 (2021) - [i6]Jack Richter-Powell, Jonathan Lorraine, Brandon Amos:
Input Convex Gradient Networks. CoRR abs/2111.12187 (2021) - [i5]Jonathan Lorraine, Paul Vicol, Jack Parker-Holder, Tal Kachman, Luke Metz, Jakob N. Foerster:
Lyapunov Exponents for Diversity in Differentiable Games. CoRR abs/2112.14570 (2021) - 2019
- [i4]Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse:
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. CoRR abs/1903.03088 (2019) - [i3]George Adam, Jonathan Lorraine:
Understanding Neural Architecture Search Techniques. CoRR abs/1904.00438 (2019) - [i2]Jonathan Lorraine, Paul Vicol, David Duvenaud:
Optimizing Millions of Hyperparameters by Implicit Differentiation. CoRR abs/1911.02590 (2019) - 2018
- [i1]Jonathan Lorraine, David Duvenaud:
Stochastic Hyperparameter Optimization through Hypernetworks. CoRR abs/1802.09419 (2018)
Coauthor Index
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