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06/16/2023
SLACK: Stable Learning of Augmentations with Cold-start and KL regularization
Data augmentation is known to improve the generalization capabilities of...
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02/06/2023
Rethinking Gauss-Newton for learning over-parameterized models
Compared to gradient descent, Gauss-Newton's method (GN) and variants ar...
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10/26/2022
Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference
We introduce two synthetic likelihood methods for Simulation-Based Infer...
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01/31/2022
Continual Repeated Annealed Flow Transport Monte Carlo
We propose Continual Repeated Annealed Flow Transport Monte Carlo (CRAFT...
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11/29/2021
Amortized Implicit Differentiation for Stochastic Bilevel Optimization
We study a class of algorithms for solving bilevel optimization problems...
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11/04/2021
Towards an Understanding of Default Policies in Multitask Policy Optimization
Much of the recent success of deep reinforcement learning has been drive...
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06/16/2021
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support
We study the gradient flow for a relaxed approximation to the Kullback-L...
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02/15/2021
Annealed Flow Transport Monte Carlo
Annealed Importance Sampling (AIS) and its Sequential Monte Carlo (SMC) ...
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02/07/2021
Deep Reinforcement Learning with Dynamic Optimism
In recent years, deep off-policy actor-critic algorithms have become a d...
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01/19/2021
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods
A recent line of work showed that various forms of convolutional kernel ...
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10/12/2020
Efficient Wasserstein Natural Gradients for Reinforcement Learning
A novel optimization approach is proposed for application to policy grad...
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07/14/2020
Estimating Barycenters of Measures in High Dimensions
Barycentric averaging is a principled way of summarizing populations of ...
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06/17/2020
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
We study the Stein Variational Gradient Descent (SVGD) algorithm, which ...
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04/01/2020
Synchronizing Probability Measures on Rotations via Optimal Transport
We introduce a new paradigm, measure synchronization, for synchronizing ...
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03/10/2020
KALE: When Energy-Based Learning Meets Adversarial Training
Legendre duality provides a variational lower-bound for the Kullback-Lei...
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10/21/2019
Kernelized Wasserstein Natural Gradient
Many machine learning problems can be expressed as the optimization of s...
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06/11/2019
Maximum Mean Discrepancy Gradient Flow
We construct a Wasserstein gradient flow of the maximum mean discrepancy...
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05/29/2018
On gradient regularizers for MMD GANs
We propose a principled method for gradient-based regularization of the ...
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01/04/2018
Demystifying MMD GANs
We investigate the training and performance of generative adversarial ne...
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11/15/2017
Kernel Conditional Exponential Family
A nonparametric family of conditional distributions is introduced, which...
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05/23/2017