Portrait de Jian Tang

Jian Tang

Membre académique principal

tangjian@mila.quebec

Chaire en IA Canada-CIFAR

Professeur agrégé, HEC Montréal, Département de sciences de la décision

Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle (DIRO)

Fondateur, BioGeometry

Sujets de recherche

Apprentissage profond

Biologie computationnelle

Modèles génératifs

Modélisation moléculaire

Réseaux de neurones en graphes

Biographie

Jian Tang est professeur agrégé au département de sciences de la décision de HEC. Il est aussi professeur associé au département informatique et recherche opérationnelle (DIRO) de l'Université de Montréal et un membre académique principal à Mila – Institut québécois d’intelligence artificielle. Il est titulaire d'une chaire de recherche en IA Canada-CIFAR et le fondateur de BioGeometry, une entreprise en démarrage spécialisée dans l'IA générative pour la découverte d'anticorps. Ses principaux domaines de recherche sont les modèles génératifs profonds, l'apprentissage automatique des graphes et leurs applications à la découverte de médicaments. Il est un leader international dans le domaine de l'apprentissage automatique des graphes, et son travail représentatif sur l'apprentissage de la représentation des nœuds, LINE, a été largement reconnu et cité plus de 5 000 fois. Il a également réalisé de nombreux travaux pionniers sur l'IA pour la découverte de médicaments, notamment le premier cadre d'apprentissage automatique à source ouverte pour la découverte de médicaments, TorchDrug et TorchProtein.

Étudiants actuels

Doctorat - UdeM

huiyu.cai@mila.quebec

Collaborateur·rice de recherche

Farzaneh Heidari

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Rabusseau

Stagiaire de recherche - McGill

stephen.lu@mila.quebec

Doctorat - UdeM

Collaborateur·rice de recherche - Carnegie Mellon University

Doctorat - UdeM

Sophie Xhonneux

Doctorat - UdeM

Superviseur⋅e principal⋅e :

lpxhonneux@gmail.com

Collaborateur·rice de recherche

Doctorat - UdeM

xinyu.yuan@mila.quebec

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

zhaocheng.zhu@umontreal.ca

Publications

Learning Powerful Policies by Using Consistent Dynamics Model

Shagun Sodhani

Anirudh Goyal

Tristan Deleu

Sergey Levine

Model-based Reinforcement Learning approaches have the promise of being sample efficient. Much of the progress in learning dynamics models i… (voir plus)n RL has been made by learning models via supervised learning. But traditional model-based approaches lead to `compounding errors' when the model is unrolled step by step. Essentially, the state transitions that the learner predicts (by unrolling the model for multiple steps) and the state transitions that the learner experiences (by acting in the environment) may not be consistent. There is enough evidence that humans build a model of the environment, not only by observing the environment but also by interacting with the environment. Interaction with the environment allows humans to carry out experiments: taking actions that help uncover true causal relationships which can be used for building better dynamics models. Analogously, we would expect such interactions to be helpful for a learning agent while learning to model the environment dynamics. In this paper, we build upon this intuition by using an auxiliary cost function to ensure consistency between what the agent observes (by acting in the real world) and what it imagines (by acting in the `learned' world). We consider several tasks - Mujoco based control tasks and Atari games - and show that the proposed approach helps to train powerful policies and better dynamics models.

2019-06-11

ArXiv (prépublication)

GMNN: Graph Markov Neural Networks

Meng Qu

This paper studies semi-supervised object classification in relational data, which is a fundamental problem in relational data modeling. The… (voir plus) problem has been extensively studied in the literature of both statistical relational learning (e.g. relational Markov networks) and graph neural networks (e.g. graph convolutional networks). Statistical relational learning methods can effectively model the dependency of object labels through conditional random fields for collective classification, whereas graph neural networks learn effective object representations for classification through end-to-end training. In this paper, we propose the Graph Markov Neural Network (GMNN) that combines the advantages of both worlds. A GMNN models the joint distribution of object labels with a conditional random field, which can be effectively trained with the variational EM algorithm. In the E-step, one graph neural network learns effective object representations for approximating the posterior distributions of object labels. In the M-step, another graph neural network is used to model the local label dependency. Experiments on object classification, link classification, and unsupervised node representation learning show that GMNN achieves state-of-the-art results.

2019-05-24

Proceedings of the 36th International Conference on Machine Learning (publié)

proceedings.mlr.press

Session-Based Social Recommendation via Dynamic Graph Attention Networks

Weiping Song

Zhiping Xiao

Yifan Wang

Laurent Charlin

Ming Zhang

Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their … (voir plus)users. Through these platforms, users can discover and create information that others will then consume. In that context, recommending relevant information to users becomes critical for viability. However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends. Moreover, the influencers may be context-dependent. That is, different friends may be relied upon for different topics. Modeling both signals is therefore essential for recommendations. We propose a recommender system for online communities based on a dynamic-graph-attention neural network. We model dynamic user behaviors with a recurrent neural network, and context-dependent social influence with a graph-attention neural network, which dynamically infers the influencers based on users' current interests. The whole model can be efficiently fit on large-scale data. Experimental results on several real-world data sets demonstrate the effectiveness of our proposed approach over several competitive baselines including state-of-the-art models.

2019-01-30

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (publié)