| 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 | 1716 | 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 | 1369 | 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 | 799 | 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 | 792 | 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 | 587 | 2019 |
| 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 | 565 | 2018 |
| 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 | 493 | 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 | 492 | 2016 |
| 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 | 343 | 2016 |
| Language GANs falling short M Caccia, L Caccia, W Fedus, H Larochelle, J Pineau, L Charlin ICLR 2020, 2018 | 291 | 2018 |
| The deconfounded recommender: A causal inference approach to recommendation Y Wang, D Liang, L Charlin, DM Blei arXiv preprint arXiv:1808.06581, 2018 | 278* | 2018 |
| Content-based recommendations with Poisson factorization P Gopalan, L Charlin, DM Blei Advances in neural information processing systems 27, 2014 | 248 | 2014 |
| The Toronto paper matching system: an automated paper-reviewer assignment system L Charlin, R Zemel | 200 | 2013 |
| 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 | 195 | 2017 |
| 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 | 193 | 2021 |
| 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 | 188 | 2020 |
| Deep exponential families R Ranganath, L Tang, L Charlin, D Blei Artificial intelligence and statistics, 762-771, 2015 | 183 | 2015 |
| Multi-XScience: A large-scale dataset for extreme multi-document summarization of scientific articles Y Lu, Y Dong, L Charlin EMNLP 2020, 2020 | 155 | 2020 |
| Causal inference for recommendation D Liang, L Charlin, DM Blei Causation: Foundation to Application, Workshop at UAI. AUAI 6 (41), 108, 2016 | 150 | 2016 |
| Dynamic poisson factorization L Charlin, R Ranganath, J McInerney, DM Blei Proceedings of the 9th ACM Conference on Recommender Systems, 155-162, 2015 | 131 | 2015 |
Joelle PineauSchool of Computer Science, McGill University; FAIR, Meta AI; MilaVerified email at cs.mcgill.ca
