Philip Bachman

According to our database, Philip Bachman authored at least 35 papers between 2009 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Ignorance is Bliss: Robust Control via Information Gating.

Manan Tomar, Riashat Islam, Sergey Levine, Philip Bachman

CoRR, 2023

Ignorance is Bliss: Robust Control via Information Gating.

Manan Tomar, Riashat Islam, Matthew E. Taylor, Sergey Levine, Philip Bachman

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Decomposed Mutual Information Estimation for Contrastive Representation Learning.

Alessandro Sordoni, Nouha Dziri, Hannes Schulz, Geoffrey J. Gordon, Philip Bachman, Remi Tachet des Combes

Proceedings of the 38th International Conference on Machine Learning, 2021

Data-Efficient Reinforcement Learning with Self-Predictive Representations.

Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Representation Learning with Video Deep InfoMax.

R. Devon Hjelm, Philip Bachman

CoRR, 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

Deep Reinforcement and InfoMax Learning.

Bogdan Mazoure, Remi Tachet des Combes, Thang Doan, Philip Bachman, R. Devon Hjelm

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Learning Representations by Maximizing Mutual Information Across Views.

Philip Bachman, R. Devon Hjelm, William Buchwalter

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

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

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

VFunc: a Deep Generative Model for Functions.

Philip Bachman, Riashat Islam, Alessandro Sordoni, Zafarali Ahmed

CoRR, 2018

Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data.

Amjad Almahairi, Sai Rajeswar, Alessandro Sordoni, Philip Bachman, Aaron C. Courville

Proceedings of the 35th International Conference on Machine Learning, 2018

Learning Invariances for Policy Generalization.

Remi Tachet des Combes, Philip Bachman, Harm van Seijen

Proceedings of the 6th International Conference on Learning Representations, 2018

Deep Reinforcement Learning That Matters.

Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Machine Comprehension by Text-to-Text Neural Question Generation.

Xingdi Yuan, Tong Wang, Çaglar Gülçehre, Alessandro Sordoni, Philip Bachman, Saizheng Zhang, Sandeep Subramanian, Adam Trischler

Proceedings of the 2nd Workshop on Representation Learning for NLP, 2017

NewsQA: A Machine Comprehension Dataset.

Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman

Proceedings of the 2nd Workshop on Representation Learning for NLP, 2017

Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data.

Shikhar Sharma, Jing He, Kaheer Suleman, Hannes Schulz, Philip Bachman

Proceedings of the 5th International Conference on Learning Representations, 2017

Calibrating Energy-based Generative Adversarial Networks.

Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard H. Hovy, Aaron C. Courville

Proceedings of the 5th International Conference on Learning Representations, 2017

Learning Algorithms for Active Learning.

Philip Bachman, Alessandro Sordoni, Adam Trischler

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data.

Adam Trischler, Zheng Ye, Xingdi Yuan, Jing He, Philip Bachman, Kaheer Suleman

CoRR, 2016

Iterative Alternating Neural Attention for Machine Reading.

Alessandro Sordoni, Philip Bachman, Yoshua Bengio

CoRR, 2016

Towards Information-Seeking Agents.

Philip Bachman, Alessandro Sordoni, Adam Trischler

CoRR, 2016

An Architecture for Deep, Hierarchical Generative Models.

Philip Bachman

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Natural Language Comprehension with the EpiReader.

Adam Trischler, Zheng Ye, Xingdi Yuan, Philip Bachman, Alessandro Sordoni, Kaheer Suleman

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016

A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data.

Adam Trischler, Zheng Ye, Xingdi Yuan, Jing He, Philip Bachman

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016

2015

Testing Visual Attention in Dynamic Environments.

Philip Bachman, David Krueger, Doina Precup

CoRR, 2015

Data Generation as Sequential Decision Making.

Philip Bachman, Doina Precup

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Variational Generative Stochastic Networks with Collaborative Shaping.

Philip Bachman, Doina Precup

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Lifelong Learning of Discriminative Representations.

Ouais Alsharif, Philip Bachman, Joelle Pineau

CoRR, 2014

Learning with Pseudo-Ensembles.

Philip Bachman, Ouais Alsharif, Doina Precup

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Sample-based approximate regularization.

Philip Bachman, Amir-massoud Farahmand, Doina Precup

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Greedy Confidence Pursuit: A Pragmatic Approach to Multi-bandit Optimization.

Philip Bachman, Doina Precup

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

2012

Improved Estimation in Time Varying Models.

Doina Precup, Philip Bachman

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Learning Compact Representations of Time-Varying Processes.

Philip Bachman, Doina Precup

Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011

2009

Structure discovery in PPI networks using pattern-based network decomposition.

Philip Bachman, Ying Liu

Bioinform., 2009