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Found 11 papers, 8 papers with code
Asymmetry in Low-Rank Adapters of Foundation Models
Specifically, when updating the parameter matrices of a neural network by adding a product $BA$, we observe that the $B$ and $A$ matrices have distinct functions: $A$ extracts features from the input, while $B$ uses these features to create the desired output.
Federated Wasserstein Distance
We introduce a principled way of computing the Wasserstein distance between two distributions in a federated manner.
Unbalanced Optimal Transport meets Sliced-Wasserstein
In parallel, unbalanced OT was designed to allow comparisons of more general positive measures, while being more robust to outliers.
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances
The Sliced-Wasserstein distance (SW) is a computationally efficient and theoretically grounded alternative to the Wasserstein distance.
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections
The Sliced-Wasserstein distance (SW) is being increasingly used in machine learning applications as an alternative to the Wasserstein distance and offers significant computational and statistical benefits.
Statistical and Topological Properties of Sliced Probability Divergences
The idea of slicing divergences has been proven to be successful when comparing two probability measures in various machine learning applications including generative modeling, and consists in computing the expected value of a `base divergence' between one-dimensional random projections of the two measures.
Generalized Sliced Distances for Probability Distributions
Probability metrics have become an indispensable part of modern statistics and machine learning, and they play a quintessential role in various applications, including statistical hypothesis testing and generative modeling.
Approximate Bayesian Computation with the Sliced-Wasserstein Distance
Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood.
Safe Policy Improvement with Soft Baseline Bootstrapping
Batch Reinforcement Learning (Batch RL) consists in training a policy using trajectories collected with another policy, called the behavioural policy.
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
Minimum expected distance estimation (MEDE) algorithms have been widely used for probabilistic models with intractable likelihood functions and they have become increasingly popular due to their use in implicit generative modeling (e. g. Wasserstein generative adversarial networks, Wasserstein autoencoders).
Generalized Sliced Wasserstein Distances
The SW distance, specifically, was shown to have similar properties to the Wasserstein distance, while being much simpler to compute, and is therefore used in various applications including generative modeling and general supervised/unsupervised learning.
