| Fast and flexible monotonic functions with ensembles of lattices M Milani Fard, K Canini, A Cotter, J Pfeifer, M Gupta Advances in neural information processing systems 29, 2016 | 85 | 2016 |
| Launch and iterate: Reducing prediction churn M Milani Fard, Q Cormier, K Canini, M Gupta Advances in Neural Information Processing Systems 29, 2016 | 62 | 2016 |
| Efficient Learning and Planning with Compressed Predictive States W Hamilton, MM Fard, J Pineau | 54* | |
| PAC-Bayesian model selection for reinforcement learning M Fard, J Pineau Advances in Neural Information Processing Systems 23, 2010 | 45 | 2010 |
| Modelling Sparse Dynamical Systems with Compressed Predictive State Representations WL Hamilton, MM Fard, J Pineau International Conference on Machine Learning, 2013 | 43 | 2013 |
| Compressed least-squares regression on sparse spaces MM Fard, Y Grinberg, J Pineau, D Precup Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 1054-1060, 2012 | 37 | 2012 |
| Non-deterministic policies in markovian decision processes MM Fard, J Pineau Journal of Artificial Intelligence Research 40, 1-24, 2011 | 35 | 2011 |
| Bellman error based feature generation using random projections on sparse spaces M Milani Fard, Y Grinberg, A Farahmand, J Pineau, D Precup Advances in Neural Information Processing Systems 26, 2013 | 22 | 2013 |
| Metric-optimized example weights S Zhao, MM Fard, H Narasimhan, M Gupta International Conference on Machine Learning, 7533-7542, 2019 | 21 | 2019 |
| PAC-Bayesian Policy Evaluation for Reinforcement Learning MM Fard, J Pineau, C Szepesvári Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, 2011 | 20 | 2011 |
| A variance analysis for POMDP policy evaluation MM Fard, J Pineau, P Sun Proceedings of the 23rd national conference on Artificial intelligence 2 …, 2008 | 16 | 2008 |
| Optimizing black-box metrics with adaptive surrogates Q Jiang, O Adigun, H Narasimhan, MM Fard, M Gupta International Conference on Machine Learning, 4784-4793, 2020 | 15 | 2020 |
| MDPs with non-deterministic policies M Fard, J Pineau Advances in neural information processing systems 21, 2008 | 10 | 2008 |
| Optimizing black-box metrics with iterative example weighting G Hiranandani, J Mathur, H Narasimhan, MM Fard, S Koyejo International Conference on Machine Learning, 4239-4249, 2021 | 7 | 2021 |
| Constrained interacting submodular groupings A Cotter, MM Fard, S You, M Gupta, J Bilmes International Conference on Machine Learning, 1068-1077, 2018 | 6 | 2018 |
| Distribution embedding networks for generalization from a diverse set of classification tasks L Liu, MM Fard, S Zhao arXiv preprint arXiv:2202.01940, 2022 | 2 | 2022 |
| A Co-evolutionary Competitive Multi-expert Approach to Image Compression with Neural Networks MM Fard Engineering of Intelligent Systems, 2006 IEEE International Conference on, 1-5, 2006 | 2 | 2006 |
| Ensemble Learning with Local Experts MM Fard IEEE Computer Society, 2006 | 2 | 2006 |
| Distribution embedding network for meta-learning with variable-length input L Liu, MM Fard, S Zhao | 1 | 2020 |
| Regularized reinforcement learning with performance guarantees M Milani Fard McGill University, 2014 | 1 | 2014 |
Joelle PineauSchool of Computer Science, McGill University; FAIR, Meta AI; MilaVerifisert e-postadresse på cs.mcgill.ca
