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Found 44 papers, 18 papers with code
Improving Consistency Models with Generator-Induced Flows
The former relies on the true velocity field of the corresponding differential equation, approximated by a pre-trained neural network.
Gaussian-Smoothed Sliced Probability Divergences
We particularly focus on the Gaussian smoothed sliced Wasserstein distance and prove that it converges with a rate $O(n^{-1/2})$.
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
In this paper, we introduce a novel differentially private generative modeling approach based on a gradient flow in the space of probability measures.
Federated Wasserstein Distance
We introduce a principled way of computing the Wasserstein distance between two distributions in a federated manner.
Adversarial Sample Detection Through Neural Network Transport Dynamics
We propose a detector of adversarial samples that is based on the view of neural networks as discrete dynamic systems.
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance.
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals.
Approximating DTW with a convolutional neural network on EEG data
Dynamic Time Wrapping (DTW) is a widely used algorithm for measuring similarities between two time series.
Personalised Federated Learning On Heterogeneous Feature Spaces
Most personalised federated learning (FL) approaches assume that raw data of all clients are defined in a common subspace i. e. all clients store their data according to the same schema.
Continuous PDE Dynamics Forecasting with Implicit Neural Representations
Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations.
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.
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.
Diverse Weight Averaging for Out-of-Distribution Generalization
Standard neural networks struggle to generalize under distribution shifts in computer vision.
Generalizing to New Physical Systems via Context-Informed Dynamics Model
Data-driven approaches to modeling physical systems fail to generalize to unseen systems that share the same general dynamics with the learning domain, but correspond to different physical contexts.
Mapping conditional distributions for domain adaptation under generalized target shift
We consider the problem of unsupervised domain adaptation (UDA) between a source and a target domain under conditional and label shift a. k. a Generalized Target Shift (GeTarS).
Statistical and Topological Properties of Gaussian Smoothed Sliced Probability Divergences
In this paper, we analyze the theoretical properties of this distance as well as those of generalized versions denoted as Gaussian smoothed sliced divergences.
Unsupervised domain adaptation with non-stochastic missing data
Moreover, we compare the target error of our Adaptation-imputation framework and the "ideal" target error of a UDA classifier without missing target components.
Differentially Private Sliced Wasserstein Distance
Developing machine learning methods that are privacy preserving is today a central topic of research, with huge practical impacts.
Photonic Differential Privacy with Direct Feedback Alignment
Optical Processing Units (OPUs) -- low-power photonic chips dedicated to large scale random projections -- have been used in previous work to train deep neural networks using Direct Feedback Alignment (DFA), an effective alternative to backpropagation.
Heterogeneous Wasserstein Discrepancy for Incomparable Distributions
We provide a theoretical analysis of this new divergence, called $\textit{heterogeneous Wasserstein discrepancy (HWD)}$, and we show that it preserves several interesting properties including rotation-invariance.
Wasserstein Learning of Determinantal Point Processes
Through an evaluation on a real-world dataset, we show that our Wasserstein learning approach provides significantly improved predictive performance on a generative task compared to DPPs trained using MLE.
Partial Trace Regression and Low-Rank Kraus Decomposition
The trace regression model, a direct extension of the well-studied linear regression model, allows one to map matrices to real-valued outputs.
Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression
Owing to their statistical properties, non-convex sparse regularizers have attracted much interest for estimating a sparse linear model from high dimensional data.
Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport
The problem of domain adaptation on an unlabeled target dataset using knowledge from multiple labelled source datasets is becoming increasingly important.
Optimal Transport for Conditional Domain Matching and Label Shift
We address the problem of unsupervised domain adaptation under the setting of generalized target shift (joint class-conditional and label shifts).
Theoretical Guarantees for Bridging Metric Measure Embedding and Optimal Transport
Unlike Gromov-Wasserstein (GW) distance which compares pairwise distances of elements from each distribution, we consider a method allowing to embed the metric measure spaces in a common Euclidean space and compute an optimal transport (OT) on the embedded distributions.
Singleshot : a scalable Tucker tensor decomposition
This paper introduces a new approach for the scalable Tucker decomposition problem.
Unsupervised domain adaptation with imputation
Motivated by practical applications, we consider unsupervised domain adaptation for classification problems, in the presence of missing data in the target domain.
Screening Sinkhorn Algorithm for Regularized Optimal Transport
We introduce in this paper a novel strategy for efficiently approximating the Sinkhorn distance between two discrete measures.
Screening Rules for Lasso with Non-Convex Sparse Regularizers
Leveraging on the convexity of the Lasso problem , screening rules help in accelerating solvers by discarding irrelevant variables, during the optimization process.
Distance Measure Machines
This paper presents a distance-based discriminative framework for learning with probability distributions.
Concave losses for robust dictionary learning
Traditional dictionary learning methods are based on quadratic convex loss function and thus are sensitive to outliers.
Joint Distribution Optimal Transportation for Domain Adaptation
This paper deals with the unsupervised domain adaptation problem, where one wants to estimate a prediction function $f$ in a given target domain without any labeled sample by exploiting the knowledge available from a source domain where labels are known.
Wasserstein Discriminant Analysis
Wasserstein Discriminant Analysis (WDA) is a new supervised method that can improve classification of high-dimensional data by computing a suitable linear map onto a lower dimensional subspace.
Importance sampling strategy for non-convex randomized block-coordinate descent
As the number of samples and dimensionality of optimization problems related to statistics an machine learning explode, block coordinate descent algorithms have gained popularity since they reduce the original problem to several smaller ones.
Operator-valued Kernels for Learning from Functional Response Data
In this paper we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function.
Generalized conditional gradient: analysis of convergence and applications
The objectives of this technical report is to provide additional results on the generalized conditional gradient methods introduced by Bredies et al. [BLM05].
Histogram of gradients of Time-Frequency Representations for Audio scene detection
This paper addresses the problem of audio scenes classification and contributes to the state of the art by proposing a novel feature.
DC Proximal Newton for Non-Convex Optimization Problems
We introduce a novel algorithm for solving learning problems where both the loss function and the regularizer are non-convex but belong to the class of difference of convex (DC) functions.
Optimal Transport for Domain Adaptation
Domain adaptation from one data space (or domain) to another is one of the most challenging tasks of modern data analytics.
Mixed-norm Regularization for Brain Decoding
This framework is extended to the multi-task learning situation where several similar classification tasks related to different subjects are learned simultaneously.
Multiple Operator-valued Kernel Learning
We study this problem in the case of kernel ridge regression for functional responses with an lr-norm constraint on the combination coefficients.
Decoding finger movements from ECoG signals using switching linear models
As a witness of the BCI community increasing interest toward such a problem, the fourth BCI Competition provides a dataset which aim is to predict individual finger movements from ECoG signals.
Support Vector Machines with a Reject Option
We consider the problem of binary classification where the classifier may abstain instead of classifying each observation.
