Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. - Alec Radford; Luke Metz; Soumith Chintala (ICLR (Poster), 2016)

Unrolled Generative Adversarial Networks. - Luke Metz; Ben Poole; David Pfau; Jascha Sohl-Dickstein (ICLR (Poster), 2017)

BEGAN: Boundary Equilibrium Generative Adversarial Networks. - David Berthelot; Tom Schumm; Luke Metz (CoRR, 2017)

Discrete Sequential Prediction of Continuous Actions for Deep RL. - Luke Metz; Julian Ibarz; Navdeep Jaitly; James Davidson (CoRR, 2017)

Adversarial Spheres. - Justin Gilmer; Luke Metz; Fartash Faghri; Samuel S. Schoenholz; Maithra Raghu; Martin Wattenberg; Ian J. Goodfellow (ICLR (Workshop), 2018)

Learning to Learn Without Labels. - Luke Metz; Niru Maheswaranathan; Brian Cheung; Jascha Sohl-Dickstein (ICLR (Workshop), 2018)

Learning Unsupervised Learning Rules. - Luke Metz; Niru Maheswaranathan; Brian Cheung; Jascha Sohl-Dickstein (CoRR, 2018)

Guided evolutionary strategies: escaping the curse of dimensionality in random search. - Niru Maheswaranathan; Luke Metz; George Tucker; Jascha Sohl-Dickstein (CoRR, 2018)

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)

Towards GAN Benchmarks Which Require Generalization. - Ishaan Gulrajani; Colin Raffel; Luke Metz (ICLR (Poster), 2019)

Meta-Learning Update Rules for Unsupervised Representation Learning. - Luke Metz; Niru Maheswaranathan; Brian Cheung; Jascha Sohl-Dickstein (ICLR, 2019)

Guided evolutionary strategies: augmenting random search with surrogate gradients. - Niru Maheswaranathan; Luke Metz; George Tucker; Dami Choi; Jascha Sohl-Dickstein (ICML, 2019)

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 learned optimizers to make models robust to input noise. - Luke Metz; Niru Maheswaranathan; Jonathon Shlens; Jascha Sohl-Dickstein; Ekin D. Cubuk (CoRR, 2019)

Learning an Adaptive Learning Rate Schedule. - Zhen Xu 006; Andrew M. Dai; Jonas Kemp; Luke Metz (CoRR, 2019)

Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. - Jack Parker-Holder; Luke Metz; Cinjon Resnick; Hengyuan Hu; Adam Lerer; Alistair Letcher; Alexander Peysakhovich; Aldo Pacchiano; Jakob N. Foerster (NeurIPS, 2020)

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)

On Linear Identifiability of Learned Representations. - Geoffrey Roeder; Luke Metz; Diederik P. Kingma (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)

Parallel Training of Deep Networks with Local Updates. - Michael Laskin; Luke Metz; Seth Nabarrao; Mark Saroufim; Badreddine Noune; Carlo Luschi; Jascha Sohl-Dickstein; Pieter Abbeel (CoRR, 2020)

Learn2Hop: Learned Optimization on Rough Landscapes. - Amil Merchant; Luke Metz; Samuel S. Schoenholz; Ekin D. Cubuk (ICML, 2021)

On Linear Identifiability of Learned Representations. - Geoffrey Roeder; Luke Metz; Durk Kingma (ICML, 2021)

Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies. - Paul Vicol; Luke Metz; Jascha Sohl-Dickstein (ICML, 2021)

Reverse engineering learned optimizers reveals known and novel mechanisms. - Niru Maheswaranathan; David Sussillo; Luke Metz; Ruoxi Sun 002; Jascha Sohl-Dickstein (NeurIPS, 2021)

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

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

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

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

Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies (Extended Abstract). - Paul Vicol; Luke Metz; Jascha Sohl-Dickstein (IJCAI, 2022)

Discovered Policy Optimisation. - Chris Lu 001; Jakub Grudzien Kuba; Alistair Letcher; Luke Metz; Christian Schröder de Witt; Jakob N. Foerster (NeurIPS, 2022)

A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases. - James Harrison; Luke Metz; Jascha Sohl-Dickstein (NeurIPS, 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)

General-Purpose In-Context Learning by Meta-Learning Transformers. - Louis Kirsch; James Harrison; Jascha Sohl-Dickstein; Luke Metz (CoRR, 2022)

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

Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies. - Oscar Li; James Harrison; Jascha Sohl-Dickstein; Virginia Smith; Luke Metz (NeurIPS, 2023)

Noise-Reuse in Online Evolution Strategies. - Oscar Li; James Harrison; Jascha Sohl-Dickstein; Virginia Smith; Luke Metz (CoRR, 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)