
Forecasting elections results via the voter model with stubborn nodes
In this paper we propose a novel method to forecast the result of electi...
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MAGMA: Inference and Prediction with MultiTask Gaussian Processes
We investigate the problem of multiple time series forecasting, with the...
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PACBayesian Bound for the Conditional Value at Risk
Conditional Value at Risk (CVaR) is a family of "coherent risk measures"...
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Differentiable PACBayes Objectives with Partially Aggregated Neural Networks
We make three related contributions motivated by the challenge of traini...
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PACBayes unleashed: generalisation bounds with unbounded losses
We present new PACBayesian generalisation bounds for learning problems ...
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How opinions crystallise: an analysis of polarisation in the voter model
We address the phenomenon of sedimentation of opinions in networks. We i...
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From industrywide parameters to aircraftcentric onflight inference: improving aeronautics performance prediction with machine learning
Aircraft performance models play a key role in airline operations, espec...
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KernelBased Ensemble Learning in Python
We propose a new supervised learning algorithm, for classification and r...
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PACBayesian Contrastive Unsupervised Representation Learning
Contrastive unsupervised representation learning (CURL) is the stateof...
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Still no free lunches: the price to pay for tighter PACBayes bounds
"No free lunch" results state the impossibility of obtaining meaningful ...
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Online kmeans Clustering
We study the problem of online clustering where a clustering algorithm h...
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PACBayes UnExpected Bernstein Inequality
We present a new PACBayesian generalization bound. Standard bounds cont...
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Attributing and Referencing (Research) Software: Best Practices and Outlook from Inria
Software is a fundamental pillar of modern scientiic research, not only ...
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Dichotomize and Generalize: PACBayesian Binary Activated Deep Neural Networks
We present a comprehensive study of multilayer neural networks with bina...
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Perturbed Model Validation: A New Framework to Validate Model Relevance
This paper introduces PMV (Perturbed Model Validation), a new technique ...
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Nonlinear aggregation of filters to improve image denoising
We introduce a novel aggregation method to efficiently perform image den...
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Revisiting clustering as matrix factorisation on the Stiefel manifold
This paper studies clustering for possibly high dimensional data (e.g. i...
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A Primer on PACBayesian Learning
Generalized Bayesian learning algorithms are increasingly popular in mac...
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Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly
When confronted with massive data streams, summarizing data with dimensi...
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Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
We examine a network of learners which address the same classification t...
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Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
We introduce pycobra, a Python library devoted to ensemble learning (reg...
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Simpler PACBayesian Bounds for Hostile Data
PACBayesian learning bounds are of the utmost interest to the learning ...
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Stability revisited: new generalisation bounds for the LeaveoneOut
The present paper provides a new generic strategy leading to nonasympto...
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A QuasiBayesian Perspective to Online Clustering
When faced with high frequency streams of data, clustering raises theore...
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An Oracle Inequality for QuasiBayesian NonNegative Matrix Factorization
The aim of this paper is to provide some theoretical understanding of Ba...
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PACBayesian High Dimensional Bipartite Ranking
This paper is devoted to the bipartite ranking problem, a classical stat...
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Benjamin Guedj
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