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04/21/2023
Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single
We propose an evolution strategies-based algorithm for estimating gradie...
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12/28/2022
On Implicit Bias in Overparameterized Bilevel Optimization
Many problems in machine learning involve bilevel optimization (BLO), in...
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02/28/2022
Amortized Proximal Optimization
We propose a framework for online meta-optimization of parameters that g...
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12/29/2021
Disentanglement and Generalization Under Correlation Shifts
Correlations between factors of variation are prevalent in real-world da...
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12/27/2021
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies
Unrolled computation graphs arise in many scenarios, including training ...
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12/24/2021
Lyapunov Exponents for Diversity in Differentiable Games
Ridge Rider (RR) is an algorithm for finding diverse solutions to optimi...
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02/16/2021
Complex Momentum for Learning in Games
We generalize gradient descent with momentum for learning in differentia...
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06/16/2020
Understanding and mitigating exploding inverses in invertible neural networks
Invertible neural networks (INNs) have been used to design generative mo...
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11/06/2019
Optimizing Millions of Hyperparameters by Implicit Differentiation
We propose an algorithm for inexpensive gradient-based hyperparameter op...
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03/07/2019
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions
Hyperparameter optimization can be formulated as a bilevel optimization ...
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10/25/2018
Reversible Recurrent Neural Networks
Recurrent neural networks (RNNs) provide state-of-the-art performance in...
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06/27/2018
Adversarial Distillation of Bayesian Neural Network Posteriors
Bayesian neural networks (BNNs) allow us to reason about uncertainty in ...
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03/12/2018
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Stochastic neural net weights are used in a variety of contexts, includi...
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12/19/2017