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2020 – today
- 2024
[i26]Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John Langford, Matthew E. Taylor, Sergey Levine:
Video Occupancy Models. CoRR abs/2407.09533 (2024)- 2023
- [c26]Manan Tomar, Riashat Islam, Matthew E. Taylor, Sergey Levine, Philip Bachman:
Ignorance is Bliss: Robust Control via Information Gating. NeurIPS 2023 - [i25]Manan Tomar, Riashat Islam
, Sergey Levine, Philip Bachman:
Ignorance is Bliss: Robust Control via Information Gating. CoRR abs/2303.06121 (2023) - 2021
- [c25]Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman:
Data-Efficient Reinforcement Learning with Self-Predictive Representations. ICLR 2021 - [c24]Alessandro Sordoni, Nouha Dziri, Hannes Schulz, Geoffrey J. Gordon, Philip Bachman, Remi Tachet des Combes:
Decomposed Mutual Information Estimation for Contrastive Representation Learning. ICML 2021: 9859-9869 - [c23]Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville:
Pretraining Representations for Data-Efficient Reinforcement Learning. NeurIPS 2021: 12686-12699 - [i24]Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville:
Pretraining Representations for Data-Efficient Reinforcement Learning. CoRR abs/2106.04799 (2021) - [i23]Alessandro Sordoni, Nouha Dziri, Hannes Schulz, Geoffrey J. Gordon, Philip Bachman, Remi Tachet des Combes:
Decomposed Mutual Information Estimation for Contrastive Representation Learning. CoRR abs/2106.13401 (2021) - 2020
- [c22]Bogdan Mazoure, Remi Tachet des Combes, Thang Doan, Philip Bachman, R. Devon Hjelm:
Deep Reinforcement and InfoMax Learning. NeurIPS 2020 - [i22]Bogdan Mazoure, Remi Tachet des Combes, Thang Doan, Philip Bachman, R. Devon Hjelm:
Deep Reinforcement and InfoMax Learning. CoRR abs/2006.07217 (2020) - [i21]Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman:
Data-Efficient Reinforcement Learning with Momentum Predictive Representations. CoRR abs/2007.05929 (2020) - [i20]R. Devon Hjelm, Philip Bachman:
Representation Learning with Video Deep InfoMax. CoRR abs/2007.13278 (2020)
2010 – 2019
- 2019
- [c21]R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, Yoshua Bengio:
Learning deep representations by mutual information estimation and maximization. ICLR 2019 - [c20]Philip Bachman, R. Devon Hjelm, William Buchwalter:
Learning Representations by Maximizing Mutual Information Across Views. NeurIPS 2019: 15509-15519 - [i19]Philip Bachman, R. Devon Hjelm, William Buchwalter:
Learning Representations by Maximizing Mutual Information Across Views. CoRR abs/1906.00910 (2019) - 2018
- [c19]Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger:
Deep Reinforcement Learning That Matters. AAAI 2018: 3207-3214 - [c18]Remi Tachet des Combes, Philip Bachman, Harm van Seijen:
Learning Invariances for Policy Generalization. ICLR (Workshop) 2018 - [c17]Amjad Almahairi, Sai Rajeswar, Alessandro Sordoni, Philip Bachman, Aaron C. Courville:
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data. ICML 2018: 195-204 - [i18]Amjad Almahairi, Sai Rajeswar, Alessandro Sordoni, Philip Bachman, Aaron C. Courville:
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data. CoRR abs/1802.10151 (2018) - [i17]Philip Bachman, Riashat Islam, Alessandro Sordoni, Zafarali Ahmed:
VFunc: a Deep Generative Model for Functions. CoRR abs/1807.04106 (2018) - [i16]Remi Tachet des Combes, Philip Bachman, Harm van Seijen:
Learning Invariances for Policy Generalization. CoRR abs/1809.02591 (2018) - 2017
- [c16]Philip Bachman, Alessandro Sordoni, Adam Trischler:
Learning Algorithms for Active Learning. ICLR (Workshop) 2017 - [c15]Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard H. Hovy, Aaron C. Courville:
Calibrating Energy-based Generative Adversarial Networks. ICLR (Poster) 2017 - [c14]Shikhar Sharma, Jing He, Kaheer Suleman, Hannes Schulz, Philip Bachman:
Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data. ICLR (Workshop) 2017 - [c13]Philip Bachman, Alessandro Sordoni, Adam Trischler:
Learning Algorithms for Active Learning. ICML 2017: 301-310 - [c12]Xingdi Yuan, Tong Wang, Çaglar Gülçehre, Alessandro Sordoni, Philip Bachman, Saizheng Zhang, Sandeep Subramanian, Adam Trischler:
Machine Comprehension by Text-to-Text Neural Question Generation. Rep4NLP@ACL 2017: 15-25 - [c11]Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman:
NewsQA: A Machine Comprehension Dataset. Rep4NLP@ACL 2017: 191-200 - [i15]Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard H. Hovy, Aaron C. Courville:
Calibrating Energy-based Generative Adversarial Networks. CoRR abs/1702.01691 (2017) - [i14]Xingdi Yuan, Tong Wang, Çaglar Gülçehre, Alessandro Sordoni, Philip Bachman, Sandeep Subramanian, Saizheng Zhang, Adam Trischler:
Machine Comprehension by Text-to-Text Neural Question Generation. CoRR abs/1705.02012 (2017) - [i13]Philip Bachman, Alessandro Sordoni, Adam Trischler:
Learning Algorithms for Active Learning. CoRR abs/1708.00088 (2017) - [i12]Philip Bachman, Doina Precup:
Variational Generative Stochastic Networks with Collaborative Shaping. CoRR abs/1708.00805 (2017) - [i11]Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger:
Deep Reinforcement Learning that Matters. CoRR abs/1709.06560 (2017) - 2016
- [c10]Adam Trischler, Zheng Ye, Xingdi Yuan, Jing He, Philip Bachman:
A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data. ACL (1) 2016 - [c9]Adam Trischler, Zheng Ye, Xingdi Yuan, Philip Bachman, Alessandro Sordoni, Kaheer Suleman:
Natural Language Comprehension with the EpiReader. EMNLP 2016: 128-137 - [c8]Philip Bachman:
An Architecture for Deep, Hierarchical Generative Models. NIPS 2016: 4826-4834 - [i10]Adam Trischler, Zheng Ye, Xingdi Yuan, Jing He, Philip Bachman, Kaheer Suleman:
A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data. CoRR abs/1603.08884 (2016) - [i9]Alessandro Sordoni, Philip Bachman, Yoshua Bengio:
Iterative Alternating Neural Attention for Machine Reading. CoRR abs/1606.02245 (2016) - [i8]Shikhar Sharma, Jing He, Kaheer Suleman, Hannes Schulz, Philip Bachman:
Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data. CoRR abs/1606.03632 (2016) - [i7]Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman:
NewsQA: A Machine Comprehension Dataset. CoRR abs/1611.09830 (2016) - [i6]Philip Bachman, Alessandro Sordoni, Adam Trischler:
Towards Information-Seeking Agents. CoRR abs/1612.02605 (2016) - [i5]Philip Bachman:
An Architecture for Deep, Hierarchical Generative Models. CoRR abs/1612.04739 (2016) - 2015
- [c7]Philip Bachman, Doina Precup:
Variational Generative Stochastic Networks with Collaborative Shaping. ICML 2015: 1964-1972 - [c6]Philip Bachman, Doina Precup:
Data Generation as Sequential Decision Making. NIPS 2015: 3249-3257 - [i4]Philip Bachman, Doina Precup:
Data Generation as Sequential Decision Making. CoRR abs/1506.03504 (2015) - [i3]Philip Bachman, David Krueger, Doina Precup:
Testing Visual Attention in Dynamic Environments. CoRR abs/1510.08949 (2015) - 2014
- [c5]Philip Bachman, Amir-massoud Farahmand, Doina Precup:
Sample-based approximate regularization. ICML 2014: 1926-1934 - [c4]Philip Bachman, Ouais Alsharif, Doina Precup:
Learning with Pseudo-Ensembles. NIPS 2014: 3365-3373 - [i2]Ouais Alsharif, Philip Bachman, Joelle Pineau:
Lifelong Learning of Discriminative Representations. CoRR abs/1404.4108 (2014) - [i1]Philip Bachman, Ouais Alsharif, Doina Precup:
Learning with Pseudo-Ensembles. CoRR abs/1412.4864 (2014) - 2013
- [c3]Philip Bachman, Doina Precup:
Greedy Confidence Pursuit: A Pragmatic Approach to Multi-bandit Optimization. ECML/PKDD (1) 2013: 241-256 - 2012
- [c2]Doina Precup, Philip Bachman:
Improved Estimation in Time Varying Models. ICML 2012 - 2011
- [c1]Philip Bachman, Doina Precup:
Learning Compact Representations of Time-Varying Processes. AAAI 2011: 1748-1749
2000 – 2009
- 2009
- [j1]Philip Bachman, Ying Liu:
Structure discovery in PPI networks using pattern-based network decomposition. Bioinform. 25(14): 1814-1821 (2009)
Coauthor Index
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