Jonathan Lorraine

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 23

Top Topics

Neural Architecture

Hyperparameters

Top Venues

Publications

Jonathan Lorraine
Scalable Nested Optimization for Deep Learning. CoRR (2024)
Nikhil Mehta, Jonathan Lorraine, Steve Masson, Ramanathan Arunachalam, Zaid Pervaiz Bhat, James Lucas, Arun George Zachariah
Improving Hyperparameter Optimization with Checkpointed Model Weights. CoRR (2024)
Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas
Graph Metanetworks for Processing Diverse Neural Architectures. ICLR (2024)
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 (2024)
Juhan Bae, Wu Lin, Jonathan Lorraine, Roger Grosse
Training Data Attribution via Approximate Unrolled Differentiation. CoRR (2024)
Michael R. Zhang, Nishkrit Desai, Juhan Bae, Jonathan Lorraine, Jimmy Ba
Using Large Language Models for Hyperparameter Optimization. CoRR (2023)
Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas
Graph Metanetworks for Processing Diverse Neural Architectures. CoRR (2023)
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 (2023)
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)
Jonathan Lorraine, Paul Vicol, Jack Parker-Holder, Tal Kachman, Luke Metz, Jakob N. Foerster
Lyapunov Exponents for Diversity in Differentiable Games. AAMAS (2022)
Jonathan Lorraine, Nihesh Anderson, Chansoo Lee, Quentin de Laroussilhe, Mehadi Hassen
Task Selection for AutoML System Evaluation. CoRR (2022)
Paul Vicol, Jonathan Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse
On Implicit Bias in Overparameterized Bilevel Optimization. CoRR (2022)
Jack Richter-Powell, Jonathan Lorraine, Brandon Amos
Input Convex Gradient Networks. CoRR (2021)
Jonathan Lorraine, David Acuna, Paul Vicol, David Duvenaud
Complex Momentum for Learning in Games. CoRR (2021)
Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud
Meta-Learning to Improve Pre-Training. CoRR (2021)
Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud
Meta-learning to Improve Pre-training. NeurIPS (2021)
Jonathan Lorraine, Paul Vicol, Jack Parker-Holder, Tal Kachman, Luke Metz, Jakob Foerster
Lyapunov Exponents for Diversity in Differentiable Games. CoRR (2021)
Jonathan Lorraine, Paul Vicol, David Duvenaud
Optimizing Millions of Hyperparameters by Implicit Differentiation. AISTATS (2020)
Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. CoRR (2019)
George Adam, Jonathan Lorraine
Understanding Neural Architecture Search Techniques. CoRR (2019)
Jonathan Lorraine, Paul Vicol, David Duvenaud
Optimizing Millions of Hyperparameters by Implicit Differentiation. CoRR (2019)
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)
Jonathan Lorraine, David Duvenaud
Stochastic Hyperparameter Optimization through Hypernetworks. CoRR (2018)