Topology and Geometry of Half-Rectified Network Optimization. - C. Daniel Freeman; Joan Bruna (ICLR (Poster), 2017)

Learned optimizers that outperform SGD on wall-clock and test loss. - Luke Metz; Niru Maheswaranathan; Jeremy Nixon; C. Daniel Freeman; Jascha Sohl-Dickstein (CoRR, 2018)

Understanding and correcting pathologies in the training of learned optimizers. - Luke Metz; Niru Maheswaranathan; Jeremy Nixon; C. Daniel Freeman; Jascha Sohl-Dickstein (ICML, 2019)

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. - C. Daniel Freeman; David Ha; Luke Metz (NeurIPS, 2019)

Using a thousand optimization tasks to learn hyperparameter search strategies. - Luke Metz; Niru Maheswaranathan; Ruoxi Sun 002; C. Daniel Freeman; Ben Poole; Jascha Sohl-Dickstein (CoRR, 2020)

Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves. - Luke Metz; Niru Maheswaranathan; C. Daniel Freeman; Ben Poole; Jascha Sohl-Dickstein (CoRR, 2020)

Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation. - C. Daniel Freeman; Erik Frey; Anton Raichuk; Sertan Girgin; Igor Mordatch; Olivier Bachem (NeurIPS Datasets and Benchmarks, 2021)

Training Learned Optimizers with Randomly Initialized Learned Optimizers. - Luke Metz; C. Daniel Freeman; Niru Maheswaranathan; Jascha Sohl-Dickstein (CoRR, 2021)

Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization. - Shixiang Shane Gu; Manfred Diaz; C. Daniel Freeman; Hiroki Furuta; Seyed Kamyar Seyed Ghasemipour; Anton Raichuk; Byron David; Erik Frey; Erwin Coumans; Olivier Bachem (CoRR, 2021)

Gradients are Not All You Need. - Luke Metz; C. Daniel Freeman; Samuel S. Schoenholz; Tal Kachman (CoRR, 2021)

Practical Tradeoffs between Memory, Compute, and Performance in Learned Optimizers. - Luke Metz; C. Daniel Freeman; James Harrison; Niru Maheswaranathan; Jascha Sohl-Dickstein (CoLLAs, 2022)

Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning. - Satoshi Kataoka; Seyed Kamyar Seyed Ghasemipour; C. Daniel Freeman; Igor Mordatch (CoRR, 2022)

VeLO: Training Versatile Learned Optimizers by Scaling Up. - Luke Metz; James Harrison; C. Daniel Freeman; Amil Merchant; Lucas Beyer; James Bradbury; Naman Agrawal; Ben Poole; Igor Mordatch; Adam Roberts; Jascha Sohl-Dickstein (CoRR, 2022)

Transformer-Based Learned Optimization. - Erik Gärtner; Luke Metz; Mykhaylo Andriluka; C. Daniel Freeman; Cristian Sminchisescu (CVPR, 2023)

Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces. - Xinghao Zhu; Wenzhao Lian; Bodi Yuan; C. Daniel Freeman; Masayoshi Tomizuka (ICRA, 2023)

Improving Large Language Model Fine-tuning for Solving Math Problems. - Yixin Liu; Avi Singh; C. Daniel Freeman; John D. Co-Reyes; Peter J. Liu (CoRR, 2023)

Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? - C. Daniel Freeman; Laura Culp; Aaron Parisi; Maxwell L. Bileschi; Gamaleldin F. Elsayed; Alex Rizkowsky; Isabelle Simpson; Alex Alemi; Azade Nova; Ben Adlam; Bernd Bohnet; Gaurav Mishra; et al. (CoRR, 2023)

Learned Neural Physics Simulation for Articulated 3D Human Pose Reconstruction. - Mykhaylo Andriluka; Baruch Tabanpour; C. Daniel Freeman; Cristian Sminchisescu (ECCV (84), 2024)

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. - Jiri Hron; Laura Culp; Gamaleldin F. Elsayed; Rosanne Liu; Ben Adlam; Maxwell L. Bileschi; Bernd Bohnet; JD Co-Reyes; Noah Fiedel; C. Daniel Freeman; Izzeddin Gur; Kathleen Kenealy; et al. (CoRR, 2024)