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Irina Rish

Understanding Continual Learning Settings with Data Distribution Drift Analysis


Apr 04, 2021
Timothée Lesort, Massimo Caccia, Irina Rish


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Towards Continual Reinforcement Learning: A Review and Perspectives


Dec 25, 2020
Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup

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* Preprint, 52 pages, 8 figures 
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COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing


Oct 30, 2020
Prateek Gupta, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, Soren Harnois-Leblanc, Victor Schmidt, Pierre-Luc St. Charles, Tristan Deleu, Andrew Williams, Akshay Patel, Meng Qu, Olexa Bilaniuk, Gaétan Marceau Caron, Pierre Luc Carrier, Satya Ortiz-Gagné, Marc-Andre Rousseau, David Buckeridge, Joumana Ghosn, Yang Zhang, Bernhard Schölkopf, Jian Tang, Irina Rish, Christopher Pal, Joanna Merckx, Eilif B. Muller, Yoshua Bengio

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Predicting Infectiousness for Proactive Contact Tracing


Oct 23, 2020
Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, Gáetan Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

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Double-Linear Thompson Sampling for Context-Attentive Bandits


Oct 15, 2020
Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay, Yasaman Khazaeni, Irina Rish

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* arXiv admin note: text overlap with arXiv:1906.09384 
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Adversarial Feature Desensitization


Jun 08, 2020
Pouya Bashivan, Blake Richards, Irina Rish

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* submitted to Neurips 2020 
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COVI White Paper


May 18, 2020
Hannah Alsdurf, Yoshua Bengio, Tristan Deleu, Prateek Gupta, Daphne Ippolito, Richard Janda, Max Jarvie, Tyler Kolody, Sekoul Krastev, Tegan Maharaj, Robert Obryk, Dan Pilat, Valerie Pisano, Benjamin Prud'homme, Meng Qu, Nasim Rahaman, Irina Rish, Jean-Franois Rousseau, Abhinav Sharma, Brooke Struck, Jian Tang, Martin Weiss, Yun William Yu

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* 63 pages, 1 figure 
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Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL


May 12, 2020
Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish

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* This article supersedes and extends our work arXiv:1706.02897 (MAB) and arXiv:1906.11286 (RL) into the Contextual Bandit (CB) framework. It generalized extensively into multi-armed bandits, contextual bandits and RL settings to create a unified framework of human behavioral agents 
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Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation


Mar 31, 2020
Victor Schmidt, Makesh Narsimhan Sreedhar, Mostafa ElAraby, Irina Rish

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Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning


Mar 12, 2020
Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam Laradji, Irina Rish, Alexande Lacoste, David Vazquez, Laurent Charlin

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Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders


Jun 28, 2019
Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish

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* arXiv admin note: substantial text overlap with arXiv:1706.02897 
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Continual Learning with Self-Organizing Maps


Apr 19, 2019
Pouya Bashivan, Martin Schrimpf, Robert Ajemian, Irina Rish, Matthew Riemer, Yuhai Tu

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* Continual Learning Workshop - NeurIPS 2018 
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A Survey on Practical Applications of Multi-Armed and Contextual Bandits


Apr 02, 2019
Djallel Bouneffouf, Irina Rish

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* under review by IJCAI 2019 Survey 
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Kernelized Hashcode Representations for Biomedical Relation Extraction


Oct 31, 2018
Sahil Garg, Aram Galstyan, Greg Ver Steeg, Irina Rish, Guillermo Cecchi, Shuyang Gao

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* To appear in the proceedings of conference, AAAI-19 
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Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference


Oct 29, 2018
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro

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Beyond Backprop: Online Alternating Minimization with Auxiliary Variables


Oct 24, 2018
Anna Choromanska, Sadhana Kumaravel, Ronny Luss, Irina Rish, Brian Kingsbury, Mattia Rigotti, Paolo DiAchille, Viatcheslav Gurev, Ravi Tejwani, Djallel Bouneffouf

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* First four authors contributed equally to this work: A.C. - theory, manuscript, S.K. - code, experiments, R.L. - algorithm, experiments, I.R. - algorithm, manuscript 
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Dialogue Modeling Via Hash Functions


Oct 18, 2018
Sahil Garg, Irina Rish, Guillermo Cecchi, Shuyang Gao, Palash Goyal, Sarik Ghazarian, Greg Ver Steeg, Aram Galstyan

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* Presented at IJCAI-ICML 2018 Workshops. The paper is revised significantly with an addition of elaborate experimental analysis 
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Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM


May 24, 2018
German Abrevaya, Aleksandr Aravkin, Guillermo Cecchi, Irina Rish, Pablo Polosecki, Peng Zheng, Silvina Ponce Dawson

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* 12 pages, 7 figures 
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Adaptive Representation Selection in Contextual Bandit


May 15, 2018
Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Irina Rish

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Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks


Dec 01, 2017
Jumana Dakka, Pouya Bashivan, Mina Gheiratmand, Irina Rish, Shantenu Jha, Russell Greiner

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* To be published as a workshop paper at NIPS 2017 Machine Learning for Health (ML4H) 
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Context Attentive Bandits: Contextual Bandit with Restricted Context


Jun 07, 2017
Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi, Raphael Feraud

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* IJCAI 2017 
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Bandit Models of Human Behavior: Reward Processing in Mental Disorders


Jun 07, 2017
Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi

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* Conference on Artificial General Intelligence, AGI-17 
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NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)


Apr 10, 2017
Leila Wehbe, Anwar Nunez-Elizalde, Marcel van Gerven, Irina Rish, Brian Murphy, Moritz Grosse-Wentrup, Georg Langs, Guillermo Cecchi


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Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World


Feb 19, 2017
Sahil Garg, Irina Rish, Guillermo Cecchi, Aurelie Lozano

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Mental State Recognition via Wearable EEG


Jun 05, 2016
Pouya Bashivan, Irina Rish, Steve Heisig

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* Proceedings of 5th NIPS workshop on Machine Learning and Interpretation in Neuroimaging (MLINI15) (2015) 5-1 
 * Presented at MLINI-2015 workshop, 2015 (arXiv:cs/0101200) 
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Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks


Feb 29, 2016
Pouya Bashivan, Irina Rish, Mohammed Yeasin, Noel Codella

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* To be published as a conference paper at ICLR 2016 
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MINT: Mutual Information based Transductive Feature Selection for Genetic Trait Prediction


Oct 07, 2013
Dan He, Irina Rish, David Haws, Simon Teyssedre, Zivan Karaman, Laxmi Parida

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A Scheme for Approximating Probabilistic Inference


Feb 06, 2013
Rina Dechter, Irina Rish

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* Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997) 
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Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding


Jan 30, 2013
Irina Rish, Kalev Kask, Rina Dechter

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* Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998) 
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Efficient Test Selection in Active Diagnosis via Entropy Approximation


Jul 04, 2012
Alice X. Zheng, Irina Rish, Alina Beygelzimer

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* Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005) 
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