Stochastic Hyperparameter Optimization through Hypernetworks. - Jonathan Lorraine; David Duvenaud (CoRR, 2018)

Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. - Matthew MacKay; Paul Vicol; Jonathan Lorraine; David Duvenaud; Roger B. Grosse (ICLR (Poster), 2019)

Understanding Neural Architecture Search Techniques. - George Adam; Jonathan Lorraine (CoRR, 2019)

Optimizing Millions of Hyperparameters by Implicit Differentiation. - Jonathan Lorraine; Paul Vicol; David Duvenaud (AISTATS, 2020)

Meta-learning to Improve Pre-training. - Aniruddh Raghu; Jonathan Lorraine; Simon Kornblith; Matthew McDermott; David Duvenaud (NeurIPS, 2021)

Complex Momentum for Learning in Games. - Jonathan Lorraine; David Acuna; Paul Vicol; David Duvenaud (CoRR, 2021)

Input Convex Gradient Networks. - Jack Richter-Powell; Jonathan Lorraine; Brandon Amos (CoRR, 2021)

Complex Momentum for Optimization in Games. - Jonathan P. Lorraine; David Acuna; Paul Vicol; David Duvenaud (AISTATS, 2022)

Lyapunov Exponents for Diversity in Differentiable Games. - Jonathan Lorraine; Paul Vicol; Jack Parker-Holder; Tal Kachman; Luke Metz; Jakob N. Foerster (AAMAS, 2022)

On Implicit Bias in Overparameterized Bilevel Optimization. - Paul Vicol; Jonathan P. Lorraine; Fabian Pedregosa; David Duvenaud; Roger B. Grosse (ICML, 2022)

Task Selection for AutoML System Evaluation. - Jonathan Lorraine; Nihesh Anderson; Chansoo Lee; Quentin de Laroussilhe; Mehadi Hassen (CoRR, 2022)

ATT3D: Amortized Text-to-3D Object Synthesis. - Jonathan Lorraine; Kevin Xie; Xiaohui Zeng; Chen-Hsuan Lin 001; Towaki Takikawa; Nicholas Sharp; Tsung-Yi Lin; Ming-Yu Liu 001; Sanja Fidler; James Lucas (ICCV, 2023)

Using Large Language Models for Hyperparameter Optimization. - Michael R. Zhang; Nishkrit Desai; Juhan Bae; Jonathan Lorraine; Jimmy Ba (CoRR, 2023)

Improving Hyperparameter Optimization with Checkpointed Model Weights. - Nikhil Mehta 004; Jonathan Lorraine; Steve Masson; Ramanathan Arunachalam; Zaid Pervaiz Bhat; James Lucas; Arun George Zachariah (ECCV Workshops (11), 2024)

LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis. - Kevin Xie; Jonathan Lorraine; Tianshi Cao; Jun Gao 004; James Lucas; Antonio Torralba 001; Sanja Fidler; Xiaohui Zeng (ECCV (77), 2024)

Graph Metanetworks for Processing Diverse Neural Architectures. - Derek Lim; Haggai Maron; Marc T. Law; Jonathan Lorraine; James Lucas (ICLR, 2024)

Training Data Attribution via Approximate Unrolling. - Juhan Bae; Wu Lin; Jonathan Lorraine; Roger B. Grosse (NeurIPS, 2024)

Training Data Attribution via Approximate Unrolled Differentiation. - Juhan Bae; Wu Lin; Jonathan Lorraine; Roger B. Grosse (CoRR, 2024)

Scalable Nested Optimization for Deep Learning. - Jonathan Lorraine (CoRR, 2024)

JacNet: Learning Functions with Structured Jacobians. - Jonathan Lorraine; Safwan Hossain (CoRR, 2024)

Multi-student Diffusion Distillation for Better One-step Generators. - Yanke Song; Jonathan Lorraine; Weili Nie; Karsten Kreis; James Lucas (CoRR, 2024)

LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models. - Zhengyi Wang; Jonathan Lorraine; Yikai Wang; Hang Su; Jun Zhu; Sanja Fidler; Xiaohui Zeng (CoRR, 2024)

Scalable Nested Optimization For Deep Learning - Jonathan Lorraine (2024)

Score Distillation Sampling for Audio: Source Separation, Synthesis, and Beyond. - Jessie Richter-Powell; Antonio Torralba 001; Jonathan Lorraine (CoRR, 2025)