Laurent Charlin
Laurent Charlin
Associate Professor, HEC Montréal & Mila, Canada CIFAR AI Chair
Verified email at - Homepage
Cited by
Cited by
How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation
CW Liu, R Lowe, IV Serban, M Noseworthy, L Charlin, J Pineau
EMNLP 2016, 2016
A hierarchical latent variable encoder-decoder model for generating dialogues
I Serban, A Sordoni, R Lowe, L Charlin, J Pineau, A Courville, Y Bengio
Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017
Online continual learning with maximally interfered retrieval
R Aljundi, L Caccia, E Belilovsky, M Caccia, M Lin, L Charlin, T Tuytelaars
Neural Information Processing Systems 2019, 2019
Exact combinatorial optimization with graph convolutional neural networks
M Gasse, D Chételat, N Ferroni, L Charlin, A Lodi
Advances in neural information processing systems 32, 2019
Session-based Social Recommendation via Dynamic Graph Attention Networks
W Song, Z Xiao, Y Wang, L Charlin, M Zhang, J Tang
WSDM 2019, 2019
A survey of available corpora for building data-driven dialogue systems
IV Serban, R Lowe, P Henderson, L Charlin, J Pineau
Dialogue & Discourse journal, 2015
Modeling user exposure in recommendation
D Liang, L Charlin, J McInerney, DM Blei
Proceedings of the 25th international conference on World Wide Web, 951-961, 2016
Towards deep conversational recommendations
R Li, S Ebrahimi Kahou, H Schulz, V Michalski, L Charlin, C Pal
Advances in neural information processing systems 31, 2018
Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence
D Liang, J Altosaar, L Charlin, DM Blei
Proceedings of the 10th ACM conference on recommender systems, 59-66, 2016
Language GANs falling short
M Caccia, L Caccia, W Fedus, H Larochelle, J Pineau, L Charlin
ICLR 2020, 2018
Content-based recommendations with Poisson factorization
PK Gopalan, L Charlin, D Blei
Advances in neural information processing systems 27, 2014
The deconfounded recommender: A causal inference approach to recommendation
Y Wang, D Liang, L Charlin, DM Blei
arXiv preprint arXiv:1808.06581, 2018
Training end-to-end dialogue systems with the ubuntu dialogue corpus
R Lowe, N Pow, IV Serban, L Charlin, CW Liu, J Pineau
Dialogue & Discourse 8 (1), 31-65, 2017
Deep exponential families
R Ranganath, L Tang, L Charlin, D Blei
Artificial intelligence and statistics, 762-771, 2015
The Toronto paper matching system: an automated paper-reviewer assignment system
L Charlin, R Zemel
Online fast adaptation and knowledge accumulation: a new approach to continual learning
M Caccia, P Rodriguez, O Ostapenko, F Normandin, M Lin, L Caccia, ...
Advances in Neural Information Processing Systems, 2020, 2020
Pretraining representations for data-efficient reinforcement learning
M Schwarzer, N Rajkumar, M Noukhovitch, A Anand, L Charlin, RD Hjelm, ...
Advances in Neural Information Processing Systems 34, 12686-12699, 2021
Dynamic poisson factorization
L Charlin, R Ranganath, J McInerney, DM Blei
Proceedings of the 9th ACM Conference on Recommender Systems, 155-162, 2015
Causal inference for recommendation
D Liang, L Charlin, DM Blei
Causation: Foundation to Application, Workshop at UAI. AUAI, 2016
Hierarchical POMDP controller optimization by likelihood maximization
M Toussaint, L Charlin, P Poupart
UAI2008, 2012
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