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ORCIDDoina Precup
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- affiliation: McGill University, Montreal, Canada
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2020 – today
- 2021
[i99]Arushi Jain, Gandharv Patil, Ayush Jain, Khimya Khetarpal, Doina Precup:
Variance Penalized On-Policy and Off-Policy Actor-Critic. CoRR abs/2102.01985 (2021)- [i98]Borja Balle, Clara Lacroce, Prakash Panangaden, Doina Precup, Guillaume Rabusseau:
Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata. CoRR abs/2102.06860 (2021) - [i97]Vlad Firoiu, Eser Aygün, Ankit Anand, Zafarali Ahmed, Xavier Glorot, Laurent Orseau, Lei Zhang, Doina Precup, Shibl Mourad:
Training a First-Order Theorem Prover from Synthetic Data. CoRR abs/2103.03798 (2021) - [i96]Safa Alver, Doina Precup:
What is Going on Inside Recurrent Meta Reinforcement Learning Agents? CoRR abs/2104.14644 (2021) - 2020
- [j30]Tanya Nair, Doina Precup, Douglas L. Arnold, Tal Arbel:
Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation. Medical Image Anal. 59 (2020) - [j29]André Barreto, Shaobo Hou, Diana Borsa, David Silver, Doina Precup:
Fast reinforcement learning with generalized policy updates. Proc. Natl. Acad. Sci. USA 117(48): 30079-30087 (2020) - [j28]Di Wu
, Boyu Wang, Doina Precup, Benoit Boulet:
Multiple Kernel Learning-Based Transfer Regression for Electric Load Forecasting. IEEE Trans. Smart Grid 11(2): 1183-1192 (2020) - [c191]Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare:
Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction. AAAI 2020: 4328-4336 - [c190]Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup:
Options of Interest: Temporal Abstraction with Interest Functions. AAAI 2020: 4444-4451 - [c189]Andrei Lupu, Doina Precup:
Gifting in Multi-Agent Reinforcement Learning (Student Abstract). AAAI 2020: 13871-13872 - [c188]David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman:
Value Preserving State-Action Abstractions. AISTATS 2020: 1639-1650 - [c187]Tianyu Li, Bogdan Mazoure, Doina Precup, Guillaume Rabusseau:
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning. AISTATS 2020: 2852-2862 - [c186]Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare:
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. AISTATS 2020: 4357-4366 - [c185]Andrei Lupu, Doina Precup:
Gifting in Multi-Agent Reinforcement Learning. AAMAS 2020: 789-797 - [c184]Mingde Zhao, Sitao Luan, Ian Porada, Xiao-Wen Chang, Doina Precup:
META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation. AAMAS 2020: 1647-1655 - [c183]Jhelum Chakravorty, Patrick Nadeem Ward, Julien Roy, Maxime Chevalier-Boisvert, Sumana Basu, Andrei Lupu, Doina Precup:
Option-Critic in Cooperative Multi-agent Systems. AAMAS 2020: 1792-1794 - [c182]Faizy Ahsan, Alexandre Drouin, François Laviolette, Doina Precup, Mathieu Blanchette:
Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites. BIBM 2020: 62-66 - [c181]Ivana Kajic, Eser Aygün, Doina Precup:
Learning to cooperate: Emergent communication in multi-agent navigation. CogSci 2020 - [c180]Doina Precup:
Keynote Lecture - Building Knowledge For AI AgentsWith Reinforcement Learning. ICCP 2020: 1 - [c179]Emmanuel Bengio, Joelle Pineau, Doina Precup:
Interference and Generalization in Temporal Difference Learning. ICML 2020: 767-777 - [c178]Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup:
What can I do here? A Theory of Affordances in Reinforcement Learning. ICML 2020: 5243-5253 - [c177]Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup:
Invariant Causal Prediction for Block MDPs. ICML 2020: 11214-11224 - [c176]Zilun Peng, Ahmed Touati, Pascal Vincent, Doina Precup:
SVRG for Policy Evaluation with Fewer Gradient Evaluations. IJCAI 2020: 2697-2703 - [c175]Veronica Chelu, Doina Precup, Hado van Hasselt:
Forethought and Hindsight in Credit Assignment. NeurIPS 2020 - [c174]Scott Fujimoto, David Meger, Doina Precup:
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay. NeurIPS 2020 - [c173]Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess:
Value-driven Hindsight Modelling. NeurIPS 2020 - [c172]Martin Klissarov, Doina Precup:
Reward Propagation Using Graph Convolutional Networks. NeurIPS 2020 - [c171]Zheng Wen, Doina Precup, Morteza Ibrahimi, André Barreto, Benjamin Van Roy, Satinder Singh:
On Efficiency in Hierarchical Reinforcement Learning. NeurIPS 2020 - [i95]Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup:
Options of Interest: Temporal Abstraction with Interest Functions. CoRR abs/2001.00271 (2020) - [i94]Bogdan Mazoure, Thang Doan, Tianyu Li, Vladimir Makarenkov, Joelle Pineau, Doina Precup, Guillaume Rabusseau:
Provably efficient reconstruction of policy networks. CoRR abs/2002.02863 (2020) - [i93]Arthur Guez, Fabio Viola, Théophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess:
Value-driven Hindsight Modelling. CoRR abs/2002.08329 (2020) - [i92]David Venuto, Jhelum Chakravorty, Léonard Boussioux, Junhao Wang, Gavin McCracken, Doina Precup:
oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions. CoRR abs/2002.09043 (2020) - [i91]Jean Harb, Tom Schaul, Doina Precup, Pierre-Luc Bacon:
Policy Evaluation Networks. CoRR abs/2002.11833 (2020) - [i90]Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup:
Invariant Causal Prediction for Block MDPs. CoRR abs/2003.06016 (2020) - [i89]Emmanuel Bengio, Joelle Pineau, Doina Precup:
Interference and Generalization in Temporal Difference Learning. CoRR abs/2003.06350 (2020) - [i88]Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare:
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. CoRR abs/2003.12239 (2020) - [i87]Ivana Kajic, Eser Aygün, Doina Precup:
Learning to cooperate: Emergent communication in multi-agent navigation. CoRR abs/2004.01097 (2020) - [i86]Safa Alver, Doina Precup:
A Brief Look at Generalization in Visual Meta-Reinforcement Learning. CoRR abs/2006.07262 (2020) - [i85]Eser Aygün, Zafarali Ahmed, Ankit Anand, Vlad Firoiu, Xavier Glorot, Laurent Orseau, Doina Precup, Shibl Mourad:
Learning to Prove from Synthetic Theorems. CoRR abs/2006.11259 (2020) - [i84]Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup:
What can I do here? A Theory of Affordances in Reinforcement Learning. CoRR abs/2006.15085 (2020) - [i83]Scott Fujimoto, David Meger, Doina Precup:
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay. CoRR abs/2007.06049 (2020) - [i82]Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup:
Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks. CoRR abs/2008.08838 (2020) - [i81]Sitao Luan, Mingde Zhao, Chenqing Hua, Xiao-Wen Chang, Doina Precup:
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks. CoRR abs/2008.08844 (2020) - [i80]Martin Klissarov, Doina Precup:
Reward Propagation Using Graph Convolutional Networks. CoRR abs/2010.02474 (2020) - [i79]Charles C. Onu, Jacob E. Miller, Doina Precup:
A Fully Tensorized Recurrent Neural Network. CoRR abs/2010.04196 (2020) - [i78]Tianyu Li, Doina Precup, Guillaume Rabusseau:
Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning. CoRR abs/2010.10029 (2020) - [i77]Veronica Chelu, Doina Precup, Hado van Hasselt:
Forethought and Hindsight in Credit Assignment. CoRR abs/2010.13685 (2020) - [i76]Gavin McCracken, Colin Daniels, Rosie Zhao, Anna Brandenberger, Prakash Panangaden, Doina Precup:
A Study of Policy Gradient on a Class of Exactly Solvable Models. CoRR abs/2011.01859 (2020) - [i75]Anand Kamat, Doina Precup:
Diversity-Enriched Option-Critic. CoRR abs/2011.02565 (2020) - [i74]Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie:
Gradient Starvation: A Learning Proclivity in Neural Networks. CoRR abs/2011.09468 (2020) - [i73]Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup:
Towards Continual Reinforcement Learning: A Review and Perspectives. CoRR abs/2012.13490 (2020) - [i72]Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi, Harsh Satija, Doina Precup:
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards. CoRR abs/2012.13658 (2020)
2010 – 2019
- 2019
- [j27]Borja Balle, Prakash Panangaden, Doina Precup:
Singular value automata and approximate minimization. Math. Struct. Comput. Sci. 29(9): 1444-1478 (2019) - [c170]Philip Amortila, Marc G. Bellemare, Prakash Panangaden, Doina Precup:
Temporally Extended Metrics for Markov Decision Processes. SafeAI@AAAI 2019 - [c169]Vincent François-Lavet, Yoshua Bengio, Doina Precup, Joelle Pineau:
Combined Reinforcement Learning via Abstract Representations. AAAI 2019: 3582-3589 - [c168]Andrei Lupu, Audrey Durand, Doina Precup:
Leveraging Observations in Bandits: Between Risks and Benefits. AAAI 2019: 6112-6119 - [c167]Khimya Khetarpal, Doina Precup:
Learning Options with Interest Functions. AAAI 2019: 9955-9956 - [c166]Guillaume Rabusseau, Tianyu Li, Doina Precup:
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning. AISTATS 2019: 1630-1639 - [c165]Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup:
The Termination Critic. AISTATS 2019: 2231-2240 - [c164]Doina Precup:
Building Knowledge for AI Agents with Reinforcement Learning. AAMAS 2019: 6 - [c163]Martin Weiss, Simon Chamorro, Roger Girgis, Margaux Luck, Samira Ebrahimi Kahou, Joseph Paul Cohen, Derek Nowrouzezahrai, Doina Precup, Florian Golemo, Chris Pal:
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments. CoRL 2019: 1314-1327 - [c162]Zafarali Ahmed, Arjun Karuvally, Doina Precup, Simon Gravel:
Learning proposals for sequential importance samplers using reinforced variational inference. DeepRLStructPred@ICLR 2019 - [c161]Scott Fujimoto, David Meger, Doina Precup:
Off-Policy Deep Reinforcement Learning without Exploration. ICML 2019: 2052-2062 - [c160]Anna Harutyunyan, Peter Vrancx, Philippe Hamel, Ann Nowé, Doina Precup:
Per-Decision Option Discounting. ICML 2019: 2644-2652 - [c159]Sanjay Thakur, Herke van Hoof, Juan Camilo Gamboa Higuera, Doina Precup, David Meger:
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks. ICRA 2019: 768-774 - [c158]Hossein Aboutalebi, Doina Precup, Tibor Schuster:
Learning Modular Safe Policies in the Bandit Setting with Application to Adaptive Clinical Trials. AISafety@IJCAI 2019 - [c157]Hossein Aboutalebi, Doina Precup, Tibor Schuster:
Learning Reliable Policies in the Bandit Setting with Application to Adaptive Clinical Trials. KHD@IJCAI 2019: 43-49 - [c156]Charles C. Onu, Jonathan Lebensold, William L. Hamilton, Doina Precup:
Neural Transfer Learning for Cry-Based Diagnosis of Perinatal Asphyxia. INTERSPEECH 2019: 3053-3057 - [c155]Barleen Kaur, Paul Lemaître, Raghav Mehta, Nazanin Mohammadi Sepahvand, Doina Precup, Douglas L. Arnold, Tal Arbel:
Improving Pathological Structure Segmentation via Transfer Learning Across Diseases. DART/MIL3ID@MICCAI 2019: 90-98 - [c154]Sumana Basu, Konrad Wagstyl, Azar Zandifar, D. Louis Collins, Adriana Romero, Doina Precup:
Early Prediction of Alzheimer's Disease Progression Using Variational Autoencoders. MICCAI (4) 2019: 205-213 - [c153]Adrian Tousignant, Paul Lemaître, Doina Precup, Douglas L. Arnold, Tal Arbel:
Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data. MIDL 2019: 483-492 - [c152]Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup:
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks. NeurIPS 2019: 10943-10953 - [c151]Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Rémi Munos:
Hindsight Credit Assignment. NeurIPS 2019: 12467-12476 - [c150]André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan J. Hunt, Shibl Mourad, David Silver, Doina Precup:
The Option Keyboard: Combining Skills in Reinforcement Learning. NeurIPS 2019: 13031-13041 - [i71]Olivier Tieleman, Angeliki Lazaridou, Shibl Mourad, Charles Blundell, Doina Precup:
Community size effect in artificial learning systems. ViGIL@NeurIPS 2019 - [i70]Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup:
The Termination Critic. CoRR abs/1902.09996 (2019) - [i69]Hossein Aboutalebi, Doina Precup, Tibor Schuster:
Learning Modular Safe Policies in the Bandit Setting with Application to Adaptive Clinical Trials. CoRR abs/1903.01026 (2019) - [i68]Sanjay Thakur, Herke van Hoof, Juan Camilo Gamboa Higuera, Doina Precup, David Meger:
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks. CoRR abs/1903.05697 (2019) - [i67]Pierre Thodoroff, Nishanth Anand, Lucas Caccia, Doina Precup, Joelle Pineau:
Recurrent Value Functions. CoRR abs/1905.09562 (2019) - [i66]Zilun Peng, Ahmed Touati, Pascal Vincent, Doina Precup:
SVRG for Policy Evaluation with Fewer Gradient Evaluations. CoRR abs/1906.03704 (2019) - [i65]Charles C. Onu, Jonathan Lebensold, William L. Hamilton, Doina Precup:
Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia. CoRR abs/1906.10199 (2019) - [i64]Srinivas Venkattaramanujam, Eric Crawford, Thang Doan, Doina Precup:
Self-supervised Learning of Distance Functions for Goal-Conditioned Reinforcement Learning. CoRR abs/1907.02998 (2019) - [i63]Vincent Michalski, Vikram Voleti, Samira Ebrahimi Kahou, Anthony Ortiz, Pascal Vincent, Chris Pal, Doina Precup:
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation. CoRR abs/1908.00061 (2019) - [i62]Sitao Luan, Xiao-Wen Chang, Doina Precup:
Revisit Policy Optimization in Matrix Form. CoRR abs/1909.09186 (2019) - [i61]David Venuto, Léonard Boussioux, Junhao Wang, Rola Dali, Jhelum Chakravorty, Yoshua Bengio, Doina Precup:
Avoidance Learning Using Observational Reinforcement Learning. CoRR abs/1909.11228 (2019) - [i60]Shruti Mishra, Abbas Abdolmaleki, Arthur Guez, Piotr Trochim, Doina Precup:
Augmenting learning using symmetry in a biologically-inspired domain. CoRR abs/1910.00528 (2019) - [i59]Jonathan Lebensold, William L. Hamilton, Borja Balle, Doina Precup:
Actor Critic with Differentially Private Critic. CoRR abs/1910.05876 (2019) - [i58]Martin Weiss, Simon Chamorro, Roger Girgis, Margaux Luck, Samira Ebrahimi Kahou, Joseph Paul Cohen, Derek Nowrouzezahrai, Doina Precup, Florian Golemo, Chris Pal:
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments. CoRR abs/1910.13249 (2019) - [i57]Tianyu Li, Bogdan Mazoure, Doina Precup, Guillaume Rabusseau:
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning. CoRR abs/1911.05010 (2019) - [i56]Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare:
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction. CoRR abs/1911.12511 (2019) - [i55]Jhelum Chakravorty, Patrick Nadeem Ward, Julien Roy, Maxime Chevalier-Boisvert, Sumana Basu, Andrei Lupu, Doina Precup:
Option-critic in cooperative multi-agent systems. CoRR abs/1911.12825 (2019) - [i54]Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Greg Wayne, Satinder Singh, Doina Precup, Rémi Munos:
Hindsight Credit Assignment. CoRR abs/1912.02503 (2019) - [i53]Riashat Islam, Raihan Seraj, Pierre-Luc Bacon, Doina Precup:
Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods. CoRR abs/1912.05104 (2019) - [i52]Riashat Islam, Raihan Seraj, Samin Yeasar Arnob, Doina Precup:
Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning. CoRR abs/1912.05109 (2019) - [i51]Riashat Islam, Zafarali Ahmed, Doina Precup:
Marginalized State Distribution Entropy Regularization in Policy Optimization. CoRR abs/1912.05128 (2019) - [i50]Olivier Tieleman, Angeliki Lazaridou, Shibl Mourad, Charles Blundell, Doina Precup:
Shaping representations through communication: community size effect in artificial learning systems. CoRR abs/1912.06208 (2019) - 2018
- [j26]Pierre-Luc Bacon, Doina Precup:
Constructing Temporal Abstractions Autonomously in Reinforcement Learning. AI Mag. 39(1): 39-50 (2018) - [c149]Yuri Grinberg, Hossein Aboutalebi, Melanie Lyman-Abramovitch, Borja Balle, Doina Precup:
Learning Predictive State Representations From Non-Uniform Sampling. AAAI 2018: 3061-3068 - [c148]Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup:
When Waiting Is Not an Option: Learning Options With a Deliberation Cost. AAAI 2018: 3165-3172 - [c147]Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé:
Learning With Options That Terminate Off-Policy. AAAI 2018: 3173-3182 - [c146]Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup:
OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning. AAAI 2018: 3199-3206 - [c145]Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger:
Deep Reinforcement Learning That Matters. AAAI 2018: 3207-3214 - [c144]Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor:
Learning Robust Options. AAAI 2018: 6409-6416 - [c143]Andrei Lupu, Doina Precup:
Imitation Upper Confidence Bound for Bandits on a Graph. AAAI 2018: 8113-8114 - [c142]Tianyu Li, Guillaume Rabusseau, Doina Precup:
Nonlinear Weighted Finite Automata. AISTATS 2018: 679-688 - [c141]Ayush Jain, Doina Precup:
Eligibility Traces for Options. AAMAS 2018: 1008-1016 - [c140]Andrei Lupu, Audrey Durand, Doina Precup:
Leveraging Observational Learning for Exploration in Bandits. AAMAS 2018: 2001-2003 - [c139]Lara J. Kanbar, Charles C. Onu, Wissam Shalish, Karen A. Brown, Guilherme M. Sant'Anna, Doina Precup, Robert E. Kearney:
Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants. EMBC 2018: 4940-4944 - [c138]Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent:
Convergent TREE BACKUP and RETRACE with Function Approximation. ICML 2018: 4962-4971 - [c137]Tanya Nair, Doina Precup, Douglas L. Arnold, Tal Arbel:
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation. MICCAI (1) 2018: 655-663 - [c136]Pierre Thodoroff, Audrey Durand, Joelle Pineau, Doina Precup:
Temporal Regularization for Markov Decision Process. NeurIPS 2018: 1784-1794 - [c135]Jessie Huang, Fa Wu, Doina Precup, Yang Cai:
Learning Safe Policies with Expert Guidance. NeurIPS 2018: 9123-9132 - [c134]Kian Kenyon-Dean, Jackie Chi Kit Cheung, Doina Precup:
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization. *SEM@NAACL-HLT 2018: 1-10 - [i49]Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor:
Learning Robust Options. CoRR abs/1802.03236 (2018) - [i48]Valentin Thomas, Emmanuel Bengio, William Fedus, Jules Pondard, Philippe Beaudoin, Hugo Larochelle, Joelle Pineau, Doina Precup, Yoshua Bengio:
Disentangling the independently controllable factors of variation by interacting with the world. CoRR abs/1802.09484 (2018) - [i47]Jessie Huang, Fa Wu, Doina Precup, Yang Cai:
Learning Safe Policies with Expert Guidance. CoRR abs/1805.08313 (2018) - [i46]Ryan Faulkner, Doina Precup:
Dyna Planning using a Feature Based Generative Model. CoRR abs/1805.10129 (2018) - [i45]Kian Kenyon-Dean, Jackie Chi Kit Cheung, Doina Precup:
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization. CoRR abs/1805.10985 (2018) - [i44]Guillaume Rabusseau, Tianyu Li, Doina Precup:
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning. CoRR abs/1807.01406 (2018) - [i43]Arushi Jain, Khimya Khetarpal, Doina Precup:
Safe Option-Critic: Learning Safety in the Option-Critic Architecture. CoRR abs/1807.08060 (2018) - [i42]Khimya Khetarpal, Doina Precup:
Attend Before you Act: Leveraging human visual attention for continual learning. CoRR abs/1807.09664 (2018) - [i41]Tanya Nair, Doina Precup, Douglas L. Arnold, Tal Arbel:
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation. CoRR abs/1808.01200 (2018) - [i40]Charles C. Onu, Lara J. Kanbar, Wissam Shalish, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney, Doina Precup:
Predicting Extubation Readiness in Extreme Preterm Infants based on Patterns of Breathing. CoRR abs/1808.07991 (2018) - [i39]Lara J. Kanbar, Charles C. Onu, Wissam Shalish, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney, Doina Precup:
Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants. CoRR abs/1808.07992 (2018) - [i38]Vincent François-Lavet, Yoshua Bengio, Doina Precup, Joelle Pineau:
Combined Reinforcement Learning via Abstract Representations. CoRR abs/1809.04506 (2018) - [i37]