Search Results for author:
Found 30 papers, 10 papers with code
End-to-end Supervised Prediction of Arbitrary-size Graphs with Partially-Masked Fused Gromov-Wasserstein Matching
We present a novel end-to-end deep learning-based approach for Supervised Graph Prediction (SGP).
Anomaly component analysis
At the crossway of machine learning and data analysis, anomaly detection aims at identifying observations that exhibit abnormal behaviour.
Fast kernel half-space depth for data with non-convex supports
Data depth is a statistical function that generalizes order and quantiles to the multivariate setting and beyond, with applications spanning over descriptive and visual statistics, anomaly detection, testing, etc.
Tailoring Mixup to Data using Kernel Warping functions
Data augmentation is an essential building block for learning efficient deep learning models.
Exploiting Edge Features in Graphs with Fused Network Gromov-Wasserstein Distance
Pairwise comparison of graphs is key to many applications in Machine learning ranging from clustering, kernel-based classification/regression and more recently supervised graph prediction.
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization
This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation.
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Surrogate kernel-based methods offer a flexible solution to structured output prediction by leveraging the kernel trick in both input and output spaces.
Vector-Valued Least-Squares Regression under Output Regularity Assumptions
We propose and analyse a reduced-rank method for solving least-squares regression problems with infinite dimensional output.
Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses
The efficiency of the approach is demonstrated and contrasted with the classical squared loss setting on both synthetic and real-world benchmarks.
Fast Kernel Methods for Generic Lipschitz Losses via $p$-Sparsified Sketches
Kernel methods are learning algorithms that enjoy solid theoretical foundations while suffering from important computational limitations.
Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection
We study short-term prediction of wind speed and wind power (every 10 minutes up to 4 hours ahead).
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF
This paper tackles post-hoc interpretability for audio processing networks.
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters
This paper introduces a novel and generic framework to solve the flagship task of supervised labeled graph prediction by leveraging Optimal Transport tools.
A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions
a probability distribution or a data set.
Emotion Transfer Using Vector-Valued Infinite Task Learning
Style transfer is a significant problem of machine learning with numerous successful applications.
A Framework to Learn with Interpretation
The interpreter provides both local and global interpretability about the predictive model in terms of human-understandable high level attribute functions, with minimal loss of accuracy.
Learning Output Embeddings in Structured Prediction
A powerful and flexible approach to structured prediction consists in embedding the structured objects to be predicted into a feature space of possibly infinite dimension by means of output kernels, and then, solving a regression problem in this output space.
When OT meets MoM: Robust estimation of Wasserstein Distance
Issued from Optimal Transport, the Wasserstein distance has gained importance in Machine Learning due to its appealing geometrical properties and the increasing availability of efficient approximations.
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
Reproducibility, that is obtaining similar results as presented in a paper or talk, using the same code and data (when available), is a necessary step to verify the reliability of research findings.
Nonlinear Functional Output Regression: a Dictionary Approach
Then, in the more general setting of integral losses based on differentiable ground losses, KPL is implemented using first-order optimization for both fully and partially observed output functions.
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses
Operator-Valued Kernels (OVKs) and associated vector-valued Reproducing Kernel Hilbert Spaces provide an elegant way to extend scalar kernel methods when the output space is a Hilbert space.
From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining
The task of predicting fine grained user opinion based on spontaneous spoken language is a key problem arising in the development of Computational Agents as well as in the development of social network based opinion miners.
Functional Isolation Forest
For the purpose of monitoring the behavior of complex infrastructures (e. g. aircrafts, transport or energy networks), high-rate sensors are deployed to capture multivariate data, generally unlabeled, in quasi continuous-time to detect quickly the occurrence of anomalies that may jeopardize the smooth operation of the system of interest.
A multimodal movie review corpus for fine-grained opinion mining
We introduce specific categories in order to make the annotation of opinions easier for movie reviews.
Autoencoding any Data through Kernel Autoencoders
This paper investigates a novel algorithmic approach to data representation based on kernel methods.
Infinite-Task Learning with RKHSs
A step further consists of learning a continuum of tasks for various loss functions.
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction
Motivated by Supervised Opinion Analysis, we propose a novel framework devoted to Structured Output Learning with Abstention (SOLA).
Joint quantile regression in vector-valued RKHSs
Addressing the will to give a more complete picture than an average relationship provided by standard regression, a novel framework for estimating and predicting simultaneously several conditional quantiles is introduced.
Random Fourier Features for Operator-Valued Kernels
Devoted to multi-task learning and structured output learning, operator-valued kernels provide a flexible tool to build vector-valued functions in the context of Reproducing Kernel Hilbert Spaces.
Learning nonparametric differential equations with operator-valued kernels and gradient matching
Modeling dynamical systems with ordinary differential equations implies a mechanistic view of the process underlying the dynamics.
