Philip Bachman

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 45

Top Topics

Visual Attention

Sequential Decision Making

Reinforcement Learning

Natural Language

Top Venues

Publications

Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John Langford, Matthew E. Taylor, Sergey Levine
Video Occupancy Models. CoRR (2024)
Manan Tomar, Riashat Islam, Sergey Levine, Philip Bachman
Ignorance is Bliss: Robust Control via Information Gating. CoRR (2023)
Manan Tomar, Riashat Islam, Matthew E. Taylor, Sergey Levine, Philip Bachman
Ignorance is Bliss: Robust Control via Information Gating. NeurIPS (2023)
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)
Alessandro Sordoni, Nouha Dziri, Hannes Schulz, Geoffrey J. Gordon, Philip Bachman, Remi Tachet des Combes
Decomposed Mutual Information Estimation for Contrastive Representation Learning. CoRR (2021)
Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman
Data-Efficient Reinforcement Learning with Self-Predictive Representations. ICLR (2021)
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 (2021)
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)
Bogdan Mazoure, Remi Tachet des Combes, Thang Doan, Philip Bachman, R. Devon Hjelm
Deep Reinforcement and InfoMax Learning. NeurIPS (2020)
Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman
Data-Efficient Reinforcement Learning with Momentum Predictive Representations. CoRR (2020)
R. Devon Hjelm, Philip Bachman
Representation Learning with Video Deep InfoMax. CoRR (2020)
Bogdan Mazoure, Remi Tachet des Combes, Thang Doan, Philip Bachman, R. Devon Hjelm
Deep Reinforcement and InfoMax Learning. CoRR (2020)
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)
Philip Bachman, R. Devon Hjelm, William Buchwalter
Learning Representations by Maximizing Mutual Information Across Views. CoRR (2019)
Philip Bachman, R. Devon Hjelm, William Buchwalter
Learning Representations by Maximizing Mutual Information Across Views. NeurIPS (2019)
Remi Tachet des Combes, Philip Bachman, Harm van Seijen
Learning Invariances for Policy Generalization. ICLR (Workshop) (2018)
Philip Bachman, Riashat Islam, Alessandro Sordoni, Zafarali Ahmed
VFunc: a Deep Generative Model for Functions. CoRR (2018)
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger
Deep Reinforcement Learning That Matters. AAAI (2018)
Amjad Almahairi, Sai Rajeswar, Alessandro Sordoni, Philip Bachman, Aaron C. Courville
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data. ICML (2018)
Amjad Almahairi, Sai Rajeswar, Alessandro Sordoni, Philip Bachman, Aaron C. Courville
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data. CoRR (2018)
Remi Tachet des Combes, Philip Bachman, Harm van Seijen
Learning Invariances for Policy Generalization. CoRR (2018)
Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard H. Hovy, Aaron C. Courville
Calibrating Energy-based Generative Adversarial Networks. CoRR (2017)
Shikhar Sharma, Jing He, Kaheer Suleman, Hannes Schulz, Philip Bachman
Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data. ICLR (Workshop) (2017)
Philip Bachman, Alessandro Sordoni, Adam Trischler
Learning Algorithms for Active Learning. CoRR (2017)
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 (2017)
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)
Philip Bachman, Alessandro Sordoni, Adam Trischler
Learning Algorithms for Active Learning. ICML (2017)
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger
Deep Reinforcement Learning that Matters. CoRR (2017)
Philip Bachman, Doina Precup
Variational Generative Stochastic Networks with Collaborative Shaping. CoRR (2017)
Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard H. Hovy, Aaron C. Courville
Calibrating Energy-based Generative Adversarial Networks. ICLR (Poster) (2017)
Philip Bachman, Alessandro Sordoni, Adam Trischler
Learning Algorithms for Active Learning. ICLR (Workshop) (2017)
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman
NewsQA: A Machine Comprehension Dataset. Rep4NLP@ACL (2017)
Philip Bachman
An Architecture for Deep, Hierarchical Generative Models. CoRR (2016)
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman
NewsQA: A Machine Comprehension Dataset. CoRR (2016)
Philip Bachman, Alessandro Sordoni, Adam Trischler
Towards Information-Seeking Agents. CoRR (2016)
Philip Bachman
An Architecture for Deep, Hierarchical Generative Models. NIPS (2016)
Adam Trischler, Zheng Ye, Xingdi Yuan, Philip Bachman, Alessandro Sordoni, Kaheer Suleman
Natural Language Comprehension with the EpiReader. EMNLP (2016)
Alessandro Sordoni, Philip Bachman, Yoshua Bengio
Iterative Alternating Neural Attention for Machine Reading. CoRR (2016)
Adam Trischler, Zheng Ye, Xingdi Yuan, Jing He, Philip Bachman
A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data. ACL (1) (2016)
Shikhar Sharma, Jing He, Kaheer Suleman, Hannes Schulz, Philip Bachman
Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data. CoRR (2016)
Adam Trischler, Zheng Ye, Xingdi Yuan, Jing He, Philip Bachman, Kaheer Suleman
A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data. CoRR (2016)
Philip Bachman, David Krueger, Doina Precup
Testing Visual Attention in Dynamic Environments. CoRR (2015)
Philip Bachman, Doina Precup
Data Generation as Sequential Decision Making. NIPS (2015)
Philip Bachman, Doina Precup
Variational Generative Stochastic Networks with Collaborative Shaping. ICML (2015)
Philip Bachman, Doina Precup
Data Generation as Sequential Decision Making. CoRR (2015)
Philip Bachman, Ouais Alsharif, Doina Precup
Learning with Pseudo-Ensembles. CoRR (2014)
Philip Bachman, Amir-massoud Farahmand, Doina Precup
Sample-based approximate regularization. ICML (2014)
Philip Bachman, Ouais Alsharif, Doina Precup
Learning with Pseudo-Ensembles. NIPS (2014)
Ouais Alsharif, Philip Bachman, Joelle Pineau
Lifelong Learning of Discriminative Representations. CoRR (2014)
Philip Bachman, Doina Precup
Greedy Confidence Pursuit: A Pragmatic Approach to Multi-bandit Optimization. ECML/PKDD (1) (2013)
Doina Precup, Philip Bachman
Improved Estimation in Time Varying Models. ICML (2012)
Philip Bachman, Doina Precup
Learning Compact Representations of Time-Varying Processes. AAAI (2011)
Philip Bachman, Ying Liu
Structure discovery in PPI networks using pattern-based network decomposition. Bioinform. 25 (14) (2009)