no code implementations • 18 Mar 2024 • Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi, Laurent Caraffa, Flavian vasile, Jeremie Mary, Andrew Comport, Valérie Gouet-Brunet
We present a method enabling the scaling of NeRFs to learn a large number of semantically-similar scenes.
no code implementations • 13 Dec 2023 • Antoine Schnepf, Flavian vasile, Ugo Tanielian
The recent advances in text and image synthesis show a great promise for the future of generative models in creative fields.
no code implementations • 8 Sep 2023 • Veronika Shilova, Ludovic Dos Santos, Flavian vasile, Gaëtan Racic, Ugo Tanielian
In digital advertising, the selection of the optimal item (recommendation) and its best creative presentation (creative optimization) have traditionally been considered separate disciplines.
no code implementations • 18 Sep 2022 • Imad Aouali, Amine Benhalloum, Martin Bompaire, Benjamin Heymann, Olivier Jeunen, David Rohde, Otmane Sakhi, Flavian vasile
Naturally, the reason for this is that we can directly measure utility metrics that rely on interventions, being the recommendations that are being shown to users.
no code implementations • 10 Aug 2022 • Imad Aouali, Achraf Ait Sidi Hammou, Sergey Ivanov, Otmane Sakhi, David Rohde, Flavian vasile
We introduce Probabilistic Rank and Reward (PRR), a scalable probabilistic model for personalized slate recommendation.
no code implementations • 2 Sep 2021 • Jules Samaran, Ugo Tanielian, Romain Beaumont, Flavian vasile
Current recommendation approaches help online merchants predict, for each visiting user, which subset of their existing products is the most relevant.
no code implementations • 26 Jul 2021 • Imad Aouali, Sergey Ivanov, Mike Gartrell, David Rohde, Flavian vasile, Victor Zaytsev, Diego Legrand
In this paper, we formulate several Bayesian models that incorporate the reward signal (Reward model), the rank signal (Rank model), or both (Full model), for non-personalized slate recommendation.
no code implementations • 13 Nov 2020 • Otmane Sakhi, Louis Faury, Flavian vasile
Our approach relies on the construction of asymptotic confidence intervals for offline contextual bandits through the DRO framework.
no code implementations • 1 Sep 2020 • Philomène Chagniot, Flavian vasile, David Rohde
Recommender systems are often optimised for short-term reward: a recommendation is considered successful if a reward (e. g. a click) can be observed immediately after the recommendation.
no code implementations • 28 Aug 2020 • Otmane Sakhi, Stephen Bonner, David Rohde, Flavian vasile
In contrast, the organic signal is typically strong and covers most items, but is not always relevant to the recommendation task.
no code implementations • 2 Oct 2019 • Otmane Sakhi, Stephen Bonner, David Rohde, Flavian vasile
The combination of the re-parameterization trick with the use of variational auto-encoders has caused a sensation in Bayesian deep learning, allowing the training of realistic generative models of images and has considerably increased our ability to use scalable latent variable models.
no code implementations • 18 Sep 2019 • Olivier Jeunen, Dmytro Mykhaylov, David Rohde, Flavian vasile, Alexandre Gilotte, Martin Bompaire
In order to handle this "bandit-feedback" setting, several Counterfactual Risk Minimisation (CRM) methods have been proposed in recent years, that attempt to estimate the performance of different policies on historical data.
no code implementations • 17 Sep 2019 • Ugo Tanielian, Flavian vasile
In recent years, the softmax model and its fast approximations have become the de-facto loss functions for deep neural networks when dealing with multi-class prediction.
no code implementations • 9 Sep 2019 • Nhan Nguyen-Thanh, Dana Marinca, Kinda Khawam, David Rohde, Flavian vasile, Elena Simona Lohan, Steven Martin, Dominique Quadri
The proposed method has been targeted to the problem of the product recommendation in the online advertising.
no code implementations • 26 Jul 2019 • Olivier Jeunen, David Rohde, Flavian vasile
The procedure aims to give an answer to the question: "Given the natural sequence of user-item interactions up to time t, can we predict which item the user will interact with at time t+1?".
no code implementations • 14 Jun 2019 • Louis Faury, Ugo Tanielian, Flavian vasile, Elena Smirnova, Elvis Dohmatob
This manuscript introduces the idea of using Distributionally Robust Optimization (DRO) for the Counterfactual Risk Minimization (CRM) problem.
no code implementations • ICLR 2019 • Ugo Tanielian, Flavian vasile, Mike Gartrell
This is often the case for applications such as language modeling, next event prediction and matrix factorization, where many of the potential outcomes are not mutually exclusive, but are more likely to be independent conditionally on the state.
no code implementations • 24 Apr 2019 • Dmytro Mykhaylov, David Rohde, Flavian vasile, Martin Bompaire, Olivier Jeunen
There are three quite distinct ways to train a machine learning model on recommender system logs.
no code implementations • 10 Apr 2019 • Stephen Bonner, Flavian vasile
Recommendations are commonly used to modify user's natural behavior, for example, increasing product sales or the time spent on a website.
1 code implementation • 2 Aug 2018 • David Rohde, Stephen Bonner, Travis Dunlop, Flavian vasile, Alexandros Karatzoglou
Recommender Systems are becoming ubiquitous in many settings and take many forms, from product recommendation in e-commerce stores, to query suggestions in search engines, to friend recommendation in social networks.
no code implementations • 22 May 2018 • Louis Faury, Flavian vasile, Clément Calauzènes, Olivier Fercoq
The aim of global optimization is to find the global optimum of arbitrary classes of functions, possibly highly multimodal ones.
no code implementations • 22 May 2018 • Ugo Tanielian, Mike Gartrell, Flavian vasile
In recent years, the Word2Vec model trained with the Negative Sampling loss function has shown state-of-the-art results in a number of machine learning tasks, including language modeling tasks, such as word analogy and word similarity, and in recommendation tasks, through Prod2Vec, an extension that applies to modeling user shopping activity and user preferences.
no code implementations • 22 Jan 2018 • Louis Faury, Flavian vasile
Learning to optimize - the idea that we can learn from data algorithms that optimize a numerical criterion - has recently been at the heart of a growing number of research efforts.
1 code implementation • 23 Jun 2017 • Stephen Bonner, Flavian vasile
Many current applications use recommendations in order to modify the natural user behavior, such as to increase the number of sales or the time spent on a website.
1 code implementation • 23 Jun 2017 • Elena Smirnova, Flavian vasile
Recommendations can greatly benefit from good representations of the user state at recommendation time.
2 code implementations • 25 Jul 2016 • Flavian Vasile, Elena Smirnova, Alexis Conneau
We propose Meta-Prod2vec, a novel method to compute item similarities for recommendation that leverages existing item metadata.
no code implementations • 11 Mar 2016 • Flavian Vasile, Damien Lefortier, Olivier Chapelle
One of the most challenging problems in computational advertising is the prediction of click-through and conversion rates for bidding in online advertising auctions.