research
∙
09/08/2023
Generalization Bounds: Perspectives from Information Theory and PAC-Bayes
A fundamental question in theoretical machine learning is generalization...
share
research
∙
06/07/2023
Learning via Wasserstein-Based High Probability Generalisation Bounds
Minimising upper bounds on the population risk or the generalisation gap...
research
∙
04/14/2023
Wasserstein PAC-Bayes Learning: A Bridge Between Generalisation and Optimisation
PAC-Bayes learning is an established framework to assess the generalisat...
research
∙
01/18/2023
Optimistic Dynamic Regret Bounds
Online Learning (OL) algorithms have originally been developed to guaran...
research
∙
10/20/2022
Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty
We introduce a modified version of the excess risk, which can be used to...
research
∙
10/03/2022
PAC-Bayes with Unbounded Losses through Supermartingales
While PAC-Bayes is now an established learning framework for bounded los...
research
∙
09/06/2022
A PAC-Bayes bound for deterministic classifiers
We establish a disintegrated PAC-Bayesian bound, for classifiers that ar...
research
∙
06/18/2022
Efficient Aggregated Kernel Tests using Incomplete U-statistics
We propose a series of computationally efficient, nonparametric tests fo...
research
∙
06/09/2022
On Margins and Generalisation for Voting Classifiers
We study the generalisation properties of majority voting on finite ense...
research
∙
05/31/2022
Online PAC-Bayes Learning
Most PAC-Bayesian bounds hold in the batch learning setting where data i...
research
∙
04/26/2022
Reprint: a randomized extrapolation based on principal components for data augmentation
Data scarcity and data imbalance have attracted a lot of attention in ma...
research
∙
02/23/2022
On PAC-Bayesian reconstruction guarantees for VAEs
Despite its wide use and empirical successes, the theoretical understand...
research
∙
02/11/2022
Measuring dissimilarity with diffeomorphism invariance
Measures of similarity (or dissimilarity) are a key ingredient to many m...
research
∙
02/11/2022
On change of measure inequalities for f-divergences
We propose new change of measure inequalities based on f-divergences (of...
research
∙
02/11/2022
Controlling Confusion via Generalisation Bounds
We establish new generalisation bounds for multiclass classification by ...
research
∙
02/03/2022
Non-Vacuous Generalisation Bounds for Shallow Neural Networks
We focus on a specific class of shallow neural networks with a single hi...
research
∙
02/02/2022
KSD Aggregated Goodness-of-fit Test
We investigate properties of goodness-of-fit tests based on the Kernel S...
research
∙
11/15/2021
Progress in Self-Certified Neural Networks
A learning method is self-certified if it uses all available data to sim...
research
∙
10/28/2021
MMD Aggregated Two-Sample Test
We propose a novel nonparametric two-sample test based on the Maximum Me...
research
∙
09/21/2021
Learning PAC-Bayes Priors for Probabilistic Neural Networks
Recent works have investigated deep learning models trained by optimisin...
research
∙
07/08/2021
On Margins and Derandomisation in PAC-Bayes
We develop a framework for derandomising PAC-Bayesian generalisation bou...
research
∙
06/23/2021
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
We investigate a stochastic counterpart of majority votes over finite en...
research
∙
12/18/2020
Upper and Lower Bounds on the Performance of Kernel PCA
Principal Component Analysis (PCA) is a popular method for dimension red...
research
∙
12/07/2020
A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings
Many practical machine learning tasks can be framed as Structured predic...
research
∙
11/24/2020
An end-to-end data-driven optimisation framework for constrained trajectories
Many real-world problems require to optimise trajectories under constrai...
research
∙
11/16/2020
Cluster-Specific Predictions with Multi-Task Gaussian Processes
A model involving Gaussian processes (GPs) is introduced to simultaneous...
research
∙
09/22/2020
Forecasting elections results via the voter model with stubborn nodes
In this paper we propose a novel method to forecast the result of electi...
research
∙
07/21/2020
MAGMA: Inference and Prediction with Multi-Task Gaussian Processes
We investigate the problem of multiple time series forecasting, with the...
research
∙
06/26/2020
PAC-Bayesian Bound for the Conditional Value at Risk
Conditional Value at Risk (CVaR) is a family of "coherent risk measures"...
research
∙
06/22/2020
Differentiable PAC-Bayes Objectives with Partially Aggregated Neural Networks
We make three related contributions motivated by the challenge of traini...
research
∙
06/12/2020
PAC-Bayes unleashed: generalisation bounds with unbounded losses
We present new PAC-Bayesian generalisation bounds for learning problems ...
research
∙
06/12/2020
How opinions crystallise: an analysis of polarisation in the voter model
We address the phenomenon of sedimentation of opinions in networks. We i...
research
∙
05/11/2020
From industry-wide parameters to aircraft-centric on-flight inference: improving aeronautics performance prediction with machine learning
Aircraft performance models play a key role in airline operations, espec...
research
∙
12/17/2019
Kernel-Based Ensemble Learning in Python
We propose a new supervised learning algorithm, for classification and r...
research
∙
10/10/2019
PAC-Bayesian Contrastive Unsupervised Representation Learning
Contrastive unsupervised representation learning (CURL) is the state-of-...
research
∙
10/10/2019
Still no free lunches: the price to pay for tighter PAC-Bayes bounds
"No free lunch" results state the impossibility of obtaining meaningful ...
research
∙
09/15/2019
Online k-means Clustering
We study the problem of online clustering where a clustering algorithm h...
research
∙
05/31/2019
PAC-Bayes Un-Expected Bernstein Inequality
We present a new PAC-Bayesian generalization bound. Standard bounds cont...
research
∙
05/27/2019
Attributing and Referencing (Research) Software: Best Practices and Outlook from Inria
Software is a fundamental pillar of modern scientiic research, not only ...
research
∙
05/24/2019
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
We present a comprehensive study of multilayer neural networks with bina...
research
∙
05/24/2019
Perturbed Model Validation: A New Framework to Validate Model Relevance
This paper introduces PMV (Perturbed Model Validation), a new technique ...
research
∙
04/01/2019
Non-linear aggregation of filters to improve image denoising
We introduce a novel aggregation method to efficiently perform image den...
research
∙
03/11/2019
Revisiting clustering as matrix factorisation on the Stiefel manifold
This paper studies clustering for possibly high dimensional data (e.g. i...
research
∙
01/16/2019
A Primer on PAC-Bayesian Learning
Generalized Bayesian learning algorithms are increasingly popular in mac...
research
∙
05/18/2018
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly
When confronted with massive data streams, summarizing data with dimensi...
research
∙
04/26/2018
Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
We examine a network of learners which address the same classification t...
research
∙
04/25/2017
Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
We introduce pycobra, a Python library devoted to ensemble learning (reg...
research
∙
10/23/2016
Simpler PAC-Bayesian Bounds for Hostile Data
PAC-Bayesian learning bounds are of the utmost interest to the learning ...
research
∙
08/23/2016
Stability revisited: new generalisation bounds for the Leave-one-Out
The present paper provides a new generic strategy leading to non-asympto...
research
∙
02/01/2016