University Pierre et Marie Curie - LIP6 - UPMC
 4 Place Jussieu - 75005 PARIS
 Tel : +33 1 44 27 84 23
 Fax : +33 1 44 27 70 00
 ludovic[dot]denoyer[at]lip6[dot]fr

NOW: 

•  Research Scientist at Facebook Artificial Intelligence Research - Paris

Previously:

• Full Professor in the Machine Learning and Information Access Team
• Co-head of the Data-Science Master Data, Machine Learning and Knowledge (DAC)
• Chargé de Mission Open Data/Research auprès de la présidence de l'UPMC
• Habilitation to Drive Research: http://www-connex.lip6.fr/~denoyer/HDR.pdf
•  CV (PDF) :  cv_july_2017

Slides

• Présentation Criteo2017 -- Sequential Budgeted Learning with focus on Cost-Sensitive Features Acquisition and Learning Time-Efficient Deep Architectures -- slides_criteo
• Présentation Google 2017 -- Sequential Budgeted Learning with focus on Deep Sequential Neural Networks and Budgeted Option Discovery --   PDF file: google_2017
• Présentation Critéo 2016 -- Learning Graph Embeddings -- criteo_2017
• Séminaire Facebook FAIR 2016 -- Sequential Budgeted Learning with focus on Sequential Information Acquisition --facebook_2016

Softwares

• A RL package for PyTorch : https://github.com/ludc/rl

• A RL simple package for Torch and OpenAI Gym : https://github.com/ludc/rltorch
• Source code of the Social Network Embeddings paper : https://github.com/ludc/social_network_diffusion_embeddings
• A simple google map interface for Torch: https://github.com/ludc/googlemaps

News

• (June 2017) : Multi-view Generative Adversarial Networks  pdf  Mickael Chen, Ludovic Denoyer has been accepted at ECML/PKDD 2017

•  (June 2017) : Two new papers:
• • Fader Networks: Manipulating Images by Sliding Attributes
  Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, Marc'Aurelio Ranzato
• • Learning Time-Efficient Deep Architectures with Budgeted Super Networks
  Tom Veniat, Ludovic Denoyer

• (November 2017) Three new papers:

• Multi-view Generative Adversarial Networks  pdf  Mickael Chen, Ludovic Denoyer
  • NIPS Adversarial Learning workshop 2016
• Options Discovery with Budgeted Reinforcement Learning pdf Aurelia Leon, Ludovic Denoyer
  • NIPS Deep Reinforcement Learning workshop 2016
• Modelling Relational Time Series using Gaussian Embeddings pdf Ludovic Dos Santos, Ali Ziat, Ludovic Denoyer, Benjamin Piwowarski, Patrick Gallinari
  • NIPS Time Series workshop 2016

• ICONIP 2016 : Recurrent Neural Networks for Adaptive Feature Acquisition -- G Contardo, L Denoyer, T Artières -- Best student paper award
• ESANN 2016 : Policy-gradient methods for Decision Trees -- A Léon, L Denoyer
• ESANN 2016 : Learning Embeddings for Completion and Prediction of Relationnal Multivariate Time-Series -- A Ziat, G Contardo, N Baskiotis, L Denoyer
• May 2015 : New article "Reinforced Decision Trees"
• 23rd of december, 2014 : New article "Representation Learning for Cold Start Recommendation"
• November 2014: Focus on "Deep Sequential Neural Networks"

Current students

• Ali Ziat - Representation Learning models for heterogeneous temporal data (co-supervised with Nicolas Baskiotis) with VEDECOM - defense in October 2017
• Gabriella Contardo - Online Budgeted Learning (co-supervised with Thierry Artieres) - defense in july 2017
• Nassim Aklil - Budgeted approaches to Robot Navigation (co-supervised with Mehdi Khamassi) -- defense in September 2017
• Aurelia Leon - Budgeted Reinforcement Learning
• Mickael Chen - Multiview Learning (co-supervised with Thierry Artieres)
• Edouard Delasalles - Temporal Learning (co-supervised with Sylvain Lamprier)
• Guillaume Lample - Neural Machine Translation (co-supervised with MA Ranzato -- Facebook)
• Perrine Cribier-Delande - Intelligent Cars (CIFRE Renault)

Past Students

• Loreta Maag - Machine Learning approach to privacy in dynamic social networks (Alcatel)
• Gabriel Dulac-Arnold - A General Sequential Model for Constrained (Budgeted) Classification - Phd Obtained in 2014, now at DeepMind
• Yann Jacob - Machine Learning for mutilrelational and heterogeneous networks - PhD obtained in 2013 - now working at a data scientist at iRaiser
• Sheng Gao - Latent Factor Models for Link Prediction Problems - PhD obtained in 2013 - now working as a Lecturer atthe Beijing Unniversity of Post and Telecommunication
• Francis Maes - Reinforcement Learning for Structured Output Prediction - PhD obtained in 2009 - now CEO of D-Labs