Search Results for author: Marianne Clausel

Found 14 papers, 6 papers with code

Polarimetric phase retrieval: uniqueness and algorithms

no code implementations26 Jun 2022 Julien Flamant, Konstantin Usevich, Marianne Clausel, David Brie

This work introduces a novel Fourier phase retrieval model, called polarimetric phase retrieval that enables a systematic use of polarization information in Fourier phase retrieval problems.

Retrieval

Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)

1 code implementation26 Feb 2022 Aleksandra Burashnikova, Yury Maximov, Marianne Clausel, Charlotte Laclau, Franck Iutzeler, Massih-Reza Amini

This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012. 06910], where we proposed a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks.

Recommendation Systems

Recommender systems: when memory matters

no code implementations4 Dec 2021 Aleksandra Burashnikova, Marianne Clausel, Massih-Reza Amini, Yury Maximov, Nicolas Dante

In this paper, we study the effect of long memory in the learnability of a sequential recommender system including users' implicit feedback.

Recommendation Systems

Bilingual Topic Models for Comparable Corpora

no code implementations30 Nov 2021 Georgios Balikas, Massih-Reza Amini, Marianne Clausel

However, this assumption is strong for comparable corpora that consist of documents thematically similar to an extent only, which are, in turn, the most commonly available or easy to obtain.

Cross-Lingual Word Embeddings Retrieval +4

Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation

1 code implementation12 Dec 2020 Aleksandra Burashnikova, Marianne Clausel, Charlotte Laclau, Frack Iutzeller, Yury Maximov, Massih-Reza Amini

In this paper, we propose a theoretically founded sequential strategy for training large-scale Recommender Systems (RS) over implicit feedback, mainly in the form of clicks.

Recommendation Systems

Smooth And Consistent Probabilistic Regression Trees

no code implementations NeurIPS 2020 Sami Alkhoury, Emilie Devijver, Marianne Clausel, Myriam Tami, Eric Gaussier, Georges Oppenheim

We propose here a generalization of regression trees, referred to as Probabilistic Regression (PR) trees, that adapt to the smoothness of the prediction function relating input and output variables while preserving the interpretability of the prediction and being robust to noise.

regression

Nonlinear Functional Output Regression: a Dictionary Approach

no code implementations3 Mar 2020 Dimitri Bouche, Marianne Clausel, François Roueff, Florence d'Alché-Buc

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.

Dictionary Learning regression

Uncertain Trees: Dealing with Uncertain Inputs in Regression Trees

no code implementations27 Oct 2018 Myriam Tami, Marianne Clausel, Emilie Devijver, Adrien Dulac, Eric Gaussier, Stefan Janaqi, Meriam Chebre

Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies.

regression

Topical Coherence in LDA-based Models through Induced Segmentation

1 code implementation ACL 2017 Hesam Amoualian, Wei Lu, Eric Gaussier, Georgios Balikas, Massih R. Amini, Marianne Clausel

This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words.

Ad-Hoc Information Retrieval General Classification +3

Modeling topic dependencies in semantically coherent text spans with copulas

1 code implementation COLING 2016 Georgios Balikas, Hesam Amoualian, Marianne Clausel, Eric Gaussier, Massih R. Amini

The exchangeability assumption in topic models like Latent Dirichlet Allocation (LDA) often results in inferring inconsistent topics for the words of text spans like noun-phrases, which are usually expected to be topically coherent.

Topic Models

On a Topic Model for Sentences

1 code implementation1 Jun 2016 Georgios Balikas, Massih-Reza Amini, Marianne Clausel

Probabilistic topic models are generative models that describe the content of documents by discovering the latent topics underlying them.

General Classification text-classification +1

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