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2020 – today
- 2022
[c17]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- [c16]Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein:
Practical Tradeoffs between Memory, Compute, and Performance in Learned Optimizers. CoLLAs 2022: 142-164 - [c15]Paul Vicol, Luke Metz, Jascha Sohl-Dickstein:
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies (Extended Abstract). IJCAI 2022: 5354-5358 - [i28]Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein:
Practical tradeoffs between memory, compute, and performance in learned optimizers. CoRR abs/2203.11860 (2022) - [i27]James Harrison, Luke Metz, Jascha Sohl-Dickstein:
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases. CoRR abs/2209.11208 (2022) - [i26]Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schröder de Witt, Jakob N. Foerster:
Discovered Policy Optimisation. CoRR abs/2210.05639 (2022) - [i25]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 abs/2211.09760 (2022) - [i24]Erik Gärtner, Luke Metz, Mykhaylo Andriluka, C. Daniel Freeman, Cristian Sminchisescu:
Transformer-Based Learned Optimization. CoRR abs/2212.01055 (2022) - [i23]Louis Kirsch, James Harrison, Jascha Sohl-Dickstein, Luke Metz:
General-Purpose In-Context Learning by Meta-Learning Transformers. CoRR abs/2212.04458 (2022) - 2021
- [c14]Amil Merchant, Luke Metz, Samuel S. Schoenholz, Ekin D. Cubuk:
Learn2Hop: Learned Optimization on Rough Landscapes. ICML 2021: 7643-7653 - [c13]Geoffrey Roeder, Luke Metz, Durk Kingma:
On Linear Identifiability of Learned Representations. ICML 2021: 9030-9039 - [c12]Paul Vicol, Luke Metz, Jascha Sohl-Dickstein:
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies. ICML 2021: 10553-10563 - [c11]Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein:
Reverse engineering learned optimizers reveals known and novel mechanisms. NeurIPS 2021: 19910-19922 - [i22]Luke Metz, C. Daniel Freeman, Niru Maheswaranathan, Jascha Sohl-Dickstein:
Training Learned Optimizers with Randomly Initialized Learned Optimizers. CoRR abs/2101.07367 (2021) - [i21]Amil Merchant, Luke Metz, Samuel S. Schoenholz, Ekin Dogus Cubuk:
Learn2Hop: Learned Optimization on Rough Landscapes. CoRR abs/2107.09661 (2021) - [i20]Luke Metz, C. Daniel Freeman, Samuel S. Schoenholz, Tal Kachman:
Gradients are Not All You Need. CoRR abs/2111.05803 (2021) - [i19]Paul Vicol, Luke Metz, Jascha Sohl-Dickstein:
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies. CoRR abs/2112.13835 (2021) - [i18]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) - 2020
- [c10]Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alexander Peysakhovich, Aldo Pacchiano, Jakob N. Foerster:
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. NeurIPS 2020 - [i17]Ishaan Gulrajani, Colin Raffel, Luke Metz:
Towards GAN Benchmarks Which Require Generalization. CoRR abs/2001.03653 (2020) - [i16]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 abs/2002.11887 (2020) - [i15]Geoffrey Roeder, Luke Metz, Diederik P. Kingma:
On Linear Identifiability of Learned Representations. CoRR abs/2007.00810 (2020) - [i14]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 abs/2009.11243 (2020) - [i13]Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein:
Reverse engineering learned optimizers reveals known and novel mechanisms. CoRR abs/2011.02159 (2020) - [i12]Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alex Peysakhovich, Aldo Pacchiano, Jakob N. Foerster:
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. CoRR abs/2011.06505 (2020) - [i11]Michael Laskin, Luke Metz, Seth Nabarrao, Mark Saroufim, Badreddine Noune, Carlo Luschi, Jascha Sohl-Dickstein, Pieter Abbeel:
Parallel Training of Deep Networks with Local Updates. CoRR abs/2012.03837 (2020)
2010 – 2019
- 2019
- [c9]Ishaan Gulrajani, Colin Raffel, Luke Metz:
Towards GAN Benchmarks Which Require Generalization. ICLR (Poster) 2019 - [c8]Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Meta-Learning Update Rules for Unsupervised Representation Learning. ICLR 2019 - [c7]Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein:
Guided evolutionary strategies: augmenting random search with surrogate gradients. ICML 2019: 4264-4273 - [c6]Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein:
Understanding and correcting pathologies in the training of learned optimizers. ICML 2019: 4556-4565 - [c5]C. Daniel Freeman, David Ha, Luke Metz:
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. NeurIPS 2019: 5380-5391 - [i10]Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, Ekin D. Cubuk:
Using learned optimizers to make models robust to input noise. CoRR abs/1906.03367 (2019) - [i9]Zhen Xu, Andrew M. Dai, Jonas Kemp, Luke Metz:
Learning an Adaptive Learning Rate Schedule. CoRR abs/1909.09712 (2019) - [i8]C. Daniel Freeman, Luke Metz, David Ha:
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. CoRR abs/1910.13038 (2019) - 2018
- [c4]Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow:
Adversarial Spheres. ICLR (Workshop) 2018 - [c3]Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Learning to Learn Without Labels. ICLR (Workshop) 2018 - [i7]Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow:
Adversarial Spheres. CoRR abs/1801.02774 (2018) - [i6]Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Learning Unsupervised Learning Rules. CoRR abs/1804.00222 (2018) - [i5]Niru Maheswaranathan, Luke Metz, George Tucker, Jascha Sohl-Dickstein:
Guided evolutionary strategies: escaping the curse of dimensionality in random search. CoRR abs/1806.10230 (2018) - [i4]Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein:
Learned optimizers that outperform SGD on wall-clock and test loss. CoRR abs/1810.10180 (2018) - 2017
- [c2]Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein:
Unrolled Generative Adversarial Networks. ICLR (Poster) 2017 - [i3]David Berthelot, Tom Schumm, Luke Metz:
BEGAN: Boundary Equilibrium Generative Adversarial Networks. CoRR abs/1703.10717 (2017) - [i2]Luke Metz, Julian Ibarz, Navdeep Jaitly, James Davidson:
Discrete Sequential Prediction of Continuous Actions for Deep RL. CoRR abs/1705.05035 (2017) - 2016
- [c1]Alec Radford, Luke Metz, Soumith Chintala:
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. ICLR (Poster) 2016 - [i1]Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein:
Unrolled Generative Adversarial Networks. CoRR abs/1611.02163 (2016)
Coauthor Index
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