Search Results for author:
Found 25 papers, 15 papers with code
Efficient and principled score estimation with Nyström kernel exponential families
We propose a fast method with statistical guarantees for learning an exponential family density model where the natural parameter is in a reproducing kernel Hilbert space, and may be infinite-dimensional.
Kernel Conditional Exponential Family
A nonparametric family of conditional distributions is introduced, which generalizes conditional exponential families using functional parameters in a suitable RKHS.
Demystifying MMD GANs
We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs.
On gradient regularizers for MMD GANs
We propose a principled method for gradient-based regularization of the critic of GAN-like models trained by adversarially optimizing the kernel of a Maximum Mean Discrepancy (MMD).
Ranked #128 on
Image Generation
on CIFAR-10
Maximum Mean Discrepancy Gradient Flow
We construct a Wasserstein gradient flow of the maximum mean discrepancy (MMD) and study its convergence properties.
Kernelized Wasserstein Natural Gradient
Many machine learning problems can be expressed as the optimization of some cost functional over a parametric family of probability distributions.
Generalized Energy Based Models
We show that both training stages are well-defined: the energy is learned by maximising a generalized likelihood, and the resulting energy-based loss provides informative gradients for learning the base.
Synchronizing Probability Measures on Rotations via Optimal Transport
We introduce a new paradigm, $\textit{measure synchronization}$, for synchronizing graphs with measure-valued edges.
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
We study the Stein Variational Gradient Descent (SVGD) algorithm, which optimises a set of particles to approximate a target probability distribution $\pi\propto e^{-V}$ on $\mathbb{R}^d$.
Estimating Barycenters of Measures in High Dimensions
Barycentric averaging is a principled way of summarizing populations of measures.
Efficient Wasserstein Natural Gradients for Reinforcement Learning
A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning (RL).
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods.
A recent line of work showed that various forms of convolutional kernel methods can be competitive with standard supervised deep convolutional networks on datasets like CIFAR-10, obtaining accuracies in the range of 87-90% while being more amenable to theoretical analysis.
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods
A recent line of work showed that various forms of convolutional kernel methods can be competitive with standard supervised deep convolutional networks on datasets like CIFAR-10, obtaining accuracies in the range of 87-90% while being more amenable to theoretical analysis.
Tactical Optimism and Pessimism for Deep Reinforcement Learning
In recent years, deep off-policy actor-critic algorithms have become a dominant approach to reinforcement learning for continuous control.
Annealed Flow Transport Monte Carlo
Annealed Importance Sampling (AIS) and its Sequential Monte Carlo (SMC) extensions are state-of-the-art methods for estimating normalizing constants of probability distributions.
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support
We study the gradient flow for a relaxed approximation to the Kullback-Leibler (KL) divergence between a moving source and a fixed target distribution.
Towards an Understanding of Default Policies in Multitask Policy Optimization
Much of the recent success of deep reinforcement learning has been driven by regularized policy optimization (RPO) algorithms with strong performance across multiple domains.
Amortized Implicit Differentiation for Stochastic Bilevel Optimization
We study a class of algorithms for solving bilevel optimization problems in both stochastic and deterministic settings when the inner-level objective is strongly convex.
Continual Repeated Annealed Flow Transport Monte Carlo
We propose Continual Repeated Annealed Flow Transport Monte Carlo (CRAFT), a method that combines a sequential Monte Carlo (SMC) sampler (itself a generalization of Annealed Importance Sampling) with variational inference using normalizing flows.
Maximum Likelihood Learning of Unnormalized Models for Simulation-Based Inference
We introduce two synthetic likelihood methods for Simulation-Based Inference (SBI), to conduct either amortized or targeted inference from experimental observations when a high-fidelity simulator is available.
Rethinking Gauss-Newton for learning over-parameterized models
This work studies the global convergence and implicit bias of Gauss Newton's (GN) when optimizing over-parameterized one-hidden layer networks in the mean-field regime.
SLACK: Stable Learning of Augmentations with Cold-start and KL regularization
Data augmentation is known to improve the generalization capabilities of neural networks, provided that the set of transformations is chosen with care, a selection often performed manually.
On Good Practices for Task-Specific Distillation of Large Pretrained Models
Large pretrained visual models exhibit remarkable generalization across diverse recognition tasks.
MLXP: A framework for conducting replicable Machine Learning eXperiments in Python
Replicability in machine learning (ML) research is increasingly concerning due to the utilization of complex non-deterministic algorithms and the dependence on numerous hyper-parameter choices, such as model architecture and training datasets.
Functional Bilevel Optimization for Machine Learning
In this paper, we introduce a new functional point of view on bilevel optimization problems for machine learning, where the inner objective is minimized over a function space.
