C. Daniel Freeman

Publication Activity (10 Years)

Years Active: 2016-2023
Publications (10 Years): 23

Top Topics

Search Strategies

Optimization Problems

Top Venues

Publications

Xinghao Zhu, Wenzhao Lian, Bodi Yuan, C. Daniel Freeman, Masayoshi Tomizuka
Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces. ICRA (2023)
Yixin Liu, Avi Singh, C. Daniel Freeman, John D. Co-Reyes, Peter J. Liu
Improving Large Language Model Fine-tuning for Solving Math Problems. CoRR (2023)
Erik Gärtner, Luke Metz, Mykhaylo Andriluka, C. Daniel Freeman, Cristian Sminchisescu
Transformer-Based Learned Optimization. CVPR (2023)
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, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR (2023)
Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein
Practical tradeoffs between memory, compute, and performance in learned optimizers. CoRR (2022)
Xinghao Zhu, Wenzhao Lian, Bodi Yuan, C. Daniel Freeman, Masayoshi Tomizuka
Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces. CoRR (2022)
Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein
Practical Tradeoffs between Memory, Compute, and Performance in Learned Optimizers. CoLLAs (2022)
Luke Metz, James Harrison, C. Daniel Freeman, Amil Merchant, Lucas Beyer, James Bradbury, Naman Agrawal, Ben Poole, Igor Mordatch, Adam Roberts, Jascha Sohl-Dickstein
VeLO: Training Versatile Learned Optimizers by Scaling Up. CoRR (2022)
Erik Gärtner, Luke Metz, Mykhaylo Andriluka, C. Daniel Freeman, Cristian Sminchisescu
Transformer-Based Learned Optimization. CoRR (2022)
Satoshi Kataoka, Seyed Kamyar Seyed Ghasemipour, C. Daniel Freeman, Igor Mordatch
Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning. CoRR (2022)
C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem
Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation. NeurIPS Datasets and Benchmarks (2021)
Shixiang Shane Gu, Manfred Diaz, C. Daniel Freeman, Hiroki Furuta, Seyed Kamyar Seyed Ghasemipour, Anton Raichuk, Byron David, Erik Frey, Erwin Coumans, Olivier Bachem
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization. CoRR (2021)
Luke Metz, C. Daniel Freeman, Samuel S. Schoenholz, Tal Kachman
Gradients are Not All You Need. CoRR (2021)
C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem
Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation. CoRR (2021)
Luke Metz, C. Daniel Freeman, Niru Maheswaranathan, Jascha Sohl-Dickstein
Training Learned Optimizers with Randomly Initialized Learned Optimizers. CoRR (2021)
Luke Metz, Niru Maheswaranathan, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves. CoRR (2020)
Luke Metz, Niru Maheswaranathan, Ruoxi Sun, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein
Using a thousand optimization tasks to learn hyperparameter search strategies. CoRR (2020)
C. Daniel Freeman, David Ha, Luke Metz
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. NeurIPS (2019)
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein
Understanding and correcting pathologies in the training of learned optimizers. ICML (2019)
C. Daniel Freeman, Luke Metz, David Ha
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. CoRR (2019)
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein
Learned optimizers that outperform SGD on wall-clock and test loss. CoRR (2018)
C. Daniel Freeman, Joan Bruna
Topology and Geometry of Half-Rectified Network Optimization. ICLR (Poster) (2017)
C. Daniel Freeman, Joan Bruna
Topology and Geometry of Half-Rectified Network Optimization. CoRR (2016)