Structure discovery in PPI networks using pattern-based network decomposition. - Philip Bachman; Ying Liu 007 (Bioinform., 2009)

Learning Compact Representations of Time-Varying Processes. - Philip Bachman; Doina Precup (AAAI, 2011)

Improved Estimation in Time Varying Models. - Doina Precup; Philip Bachman (ICML, 2012)

Greedy Confidence Pursuit: A Pragmatic Approach to Multi-bandit Optimization. - Philip Bachman; Doina Precup (ECML/PKDD (1), 2013)

Sample-based approximate regularization. - Philip Bachman; Amir-massoud Farahmand; Doina Precup (ICML, 2014)

Learning with Pseudo-Ensembles. - Philip Bachman; Ouais Alsharif; Doina Precup (NIPS, 2014)

Lifelong Learning of Discriminative Representations. - Ouais Alsharif; Philip Bachman; Joelle Pineau (CoRR, 2014)

Variational Generative Stochastic Networks with Collaborative Shaping. - Philip Bachman; Doina Precup (ICML, 2015)

Data Generation as Sequential Decision Making. - Philip Bachman; Doina Precup (NIPS, 2015)

Testing Visual Attention in Dynamic Environments. - Philip Bachman; David Krueger 001; Doina Precup (CoRR, 2015)

A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data. - Adam Trischler; Zheng Ye; Xingdi Yuan; Jing He; Philip Bachman (ACL (1), 2016)

Natural Language Comprehension with the EpiReader. - Adam Trischler; Zheng Ye; Xingdi Yuan; Philip Bachman; Alessandro Sordoni; Kaheer Suleman (EMNLP, 2016)

An Architecture for Deep, Hierarchical Generative Models. - Philip Bachman (NIPS, 2016)

Towards Information-Seeking Agents. - Philip Bachman; Alessandro Sordoni; Adam Trischler (CoRR, 2016)

Iterative Alternating Neural Attention for Machine Reading. - Alessandro Sordoni; Philip Bachman; Yoshua Bengio (CoRR, 2016)

A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data. - Adam Trischler; Zheng Ye; Xingdi Yuan; Jing He 010; Philip Bachman; Kaheer Suleman (CoRR, 2016)

Calibrating Energy-based Generative Adversarial Networks. - Zihang Dai; Amjad Almahairi; Philip Bachman; Eduard H. Hovy; Aaron C. Courville (ICLR (Poster), 2017)

Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data. - Shikhar Sharma 001; Jing He 010; Kaheer Suleman; Hannes Schulz; Philip Bachman (ICLR (Workshop), 2017)

Learning Algorithms for Active Learning. - Philip Bachman; Alessandro Sordoni; Adam Trischler (ICML, 2017)

NewsQA: A Machine Comprehension Dataset. - Adam Trischler; Tong Wang 012; Xingdi Yuan; Justin Harris; Alessandro Sordoni; Philip Bachman; Kaheer Suleman (Rep4NLP@ACL, 2017)

Machine Comprehension by Text-to-Text Neural Question Generation. - Xingdi Yuan; Tong Wang 012; Çaglar Gülçehre; Alessandro Sordoni; Philip Bachman; Saizheng Zhang; Sandeep Subramanian; Adam Trischler (Rep4NLP@ACL, 2017)

Deep Reinforcement Learning That Matters. - Peter Henderson 002; Riashat Islam; Philip Bachman; Joelle Pineau; Doina Precup; David Meger (AAAI, 2018)

Learning Invariances for Policy Generalization. - Remi Tachet des Combes; Philip Bachman; Harm van Seijen (ICLR (Workshop), 2018)

Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data. - Amjad Almahairi; Sai Rajeswar; Alessandro Sordoni; Philip Bachman; Aaron C. Courville (ICML, 2018)

VFunc: a Deep Generative Model for Functions. - Philip Bachman; Riashat Islam; Alessandro Sordoni; Zafarali Ahmed (CoRR, 2018)

Learning deep representations by mutual information estimation and maximization. - R. Devon Hjelm; Alex Fedorov; Samuel Lavoie-Marchildon; Karan Grewal; Philip Bachman; Adam Trischler; Yoshua Bengio (ICLR, 2019)

Learning Representations by Maximizing Mutual Information Across Views. - Philip Bachman; R. Devon Hjelm; William Buchwalter (NeurIPS, 2019)

Deep Reinforcement and InfoMax Learning. - Bogdan Mazoure; Remi Tachet des Combes; Thang Doan; Philip Bachman; R. Devon Hjelm (NeurIPS, 2020)

Data-Efficient Reinforcement Learning with Momentum Predictive Representations. - Max Schwarzer; Ankesh Anand; Rishab Goel; R. Devon Hjelm; Aaron C. Courville; Philip Bachman (CoRR, 2020)

Representation Learning with Video Deep InfoMax. - R. Devon Hjelm; Philip Bachman (CoRR, 2020)

Data-Efficient Reinforcement Learning with Self-Predictive Representations. - Max Schwarzer; Ankesh Anand; Rishab Goel; R. Devon Hjelm; Aaron C. Courville; Philip Bachman (ICLR, 2021)

Decomposed Mutual Information Estimation for Contrastive Representation Learning. - Alessandro Sordoni; Nouha Dziri; Hannes Schulz; Geoffrey J. Gordon; Philip Bachman; Remi Tachet des Combes (ICML, 2021)

Pretraining Representations for Data-Efficient Reinforcement Learning. - Max Schwarzer; Nitarshan Rajkumar; Michael Noukhovitch; Ankesh Anand; Laurent Charlin; R. Devon Hjelm; Philip Bachman; Aaron C. Courville (NeurIPS, 2021)

Ignorance is Bliss: Robust Control via Information Gating. - Manan Tomar; Riashat Islam; Matthew E. Taylor; Sergey Levine; Philip Bachman (NeurIPS, 2023)

Ignorance is Bliss: Robust Control via Information Gating. - Manan Tomar; Riashat Islam; Sergey Levine; Philip Bachman (CoRR, 2023)

Video Occupancy Models. - Manan Tomar; Philippe Hansen-Estruch; Philip Bachman; Alex Lamb; John Langford 001; Matthew E. Taylor; Sergey Levine (CoRR, 2024)