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ORCIDIrina Rish
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- affiliation: Mila - Quebec AI Institute, Montreal, Canada
- affiliation: IBM Research
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2020 – today
- 2025
[c84]Ivan Anokhin, Rishav Rishav, Matthew Riemer, Stephen Chung, Irina Rish, Samira Ebrahimi Kahou:
Handling Delay in Real-Time Reinforcement Learning. ICLR 2025- [c83]Md Rifat Arefin, Gopeshh Subbaraj, Nicolas Gontier, Yann LeCun, Irina Rish, Ravid Shwartz-Ziv, Christopher Pal:
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning. ICLR 2025 - [c82]Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother, Irina Rish, Glen Berseth, Sanjiban Choudhury:
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching. ICLR 2025 - [c81]Ayush Kaushal, Tejas Vaidhya, Arnab Kumar Mondal, Tejas Pandey, Aaryan Bhagat, Irina Rish:
Surprising Effectiveness of pretraining Ternary Language Model at Scale. ICLR 2025 - [c80]Matthew Riemer, Gopeshh Subbaraj, Glen Berseth, Irina Rish:
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference. ICLR 2025 - [i78]Alexis Roger, Prateek Humane, Daniel Z. Kaplan, Kshitij Gupta, Qi Sun, George Adamopoulos, Jonathan Siu Chi Lim, Quentin Anthony, Edwin Fennell, Irina Rish:
Robin: a Suite of Multi-Scale Vision-Language Models and the CHIRP Evaluation Benchmark. CoRR abs/2501.09672 (2025) - [i77]Vaibhav Singh, Paul Janson, Paria Mehrbod, Adam Ibrahim, Irina Rish, Eugene Belilovsky, Benjamin Thérien:
Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training. CoRR abs/2503.02844 (2025) - [i76]Benjamin Thérien, Charles-Étienne Joseph, Zain Sarwar, Ashwinee Panda, Anirban Das, Shi-Xiong Zhang, Stephen Rawls, Sambit Sahu, Eugene Belilovsky, Irina Rish:
Continual Pre-training of MoEs: How robust is your router? CoRR abs/2503.05029 (2025) - [i75]Ivan Anokhin, Rishav Rishav, Matthew Riemer, Stephen Chung, Irina Rish, Samira Ebrahimi Kahou:
Handling Delay in Real-Time Reinforcement Learning. CoRR abs/2503.23478 (2025) - 2024
- [j18]Mohammad-Javad Darvishi Bayazi
, Mohammad Sajjad Ghaemi, Timothée Lesort, Md Rifat Arefin, Jocelyn Faubert, Irina Rish:
Amplifying pathological detection in EEG signaling pathways through cross-dataset transfer learning. Comput. Biol. Medicine 169: 107893 (2024) - [j17]Germán Abrevaya, Mahta Ramezanian-Panahi, Jean-Christophe Gagnon-Audet, Pablo Polosecki, Irina Rish, Silvina Ponce Dawson, Guillermo A. Cecchi, Guillaume Dumas:
Effective Latent Differential Equation Models via Attention and Multiple Shooting. Trans. Mach. Learn. Res. 2024 (2024) - [j16]Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Gregory Anthony, Eugene Belilovsky, Timothée Lesort, Irina Rish:
Simple and Scalable Strategies to Continually Pre-train Large Language Models. Trans. Mach. Learn. Res. 2024 (2024) - [c79]Mohammad-Javad Darvishi Bayazi, Mohammad Sajjad Ghaemi, Jocelyn Faubert, Irina Rish:
Dance of the Neurons: Unraveling Sex from Brain Signals (short paper). ML4CMH@AAAI 2024: 35-40 - [c78]Rishika Bhagwatkar, Shravan Nayak, Pouya Bashivan, Irina Rish:
Improving Adversarial Robustness in Vision-Language Models with Architecture and Prompt Design. EMNLP (Findings) 2024: 17003-17020 - [c77]Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi:
Unsupervised Concept Discovery Mitigates Spurious Correlations. ICML 2024 - [c76]Alessio Mora, Irene Tenison, Paolo Bellavista, Irina Rish:
Knowledge Distillation in Federated Learning: A Practical Guide. IJCAI 2024: 8188-8196 - [c75]Connor Brennan, Andrew Williams, Omar G. Younis, Vedant Vyas, Daria Yasafova, Irina Rish:
Using Unity to Help Solve Reinforcement Learning. NeurIPS 2024 - [c74]Maurice Weber, Daniel Y. Fu, Quentin Anthony, Yonatan Oren, Shane Adams, Anton Alexandrov, Xiaozhong Lyu, Huu Nguyen, Xiaozhe Yao, Virginia Adams, Ben Athiwaratkun, Rahul Chalamala, Kezhen Chen, Max Ryabinin, Tri Dao, Percy Liang, Christopher Ré, Irina Rish, Ce Zhang:
RedPajama: an Open Dataset for Training Large Language Models. NeurIPS 2024 - [i74]Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi:
Unsupervised Concept Discovery Mitigates Spurious Correlations. CoRR abs/2402.13368 (2024) - [i73]Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Anthony, Timothée Lesort, Eugene Belilovsky, Irina Rish:
Simple and Scalable Strategies to Continually Pre-train Large Language Models. CoRR abs/2403.08763 (2024) - [i72]Sahil Garg, Anderson Schneider, Anant Raj, Kashif Rasul, Yuriy Nevmyvaka, Sneihil Gopal, Amit Dhurandhar, Guillermo A. Cecchi, Irina Rish:
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI. CoRR abs/2404.07377 (2024) - [i71]Benjamin Thérien, Charles-Étienne Joseph, Boris Knyazev, Edouard Oyallon, Irina Rish, Eugene Belilovsky:
μLO: Compute-Efficient Meta-Generalization of Learned Optimizers. CoRR abs/2406.00153 (2024) - [i70]Tommaso Tosato, Pascal Tikeng Notsawo Jr., Saskia Helbling, Irina Rish, Guillaume Dumas:
Lost in Translation: The Algorithmic Gap Between LMs and the Brain. CoRR abs/2407.04680 (2024) - [i69]Rishika Bhagwatkar, Shravan Nayak, Reza Bayat, Alexis Roger, Daniel Z. Kaplan, Pouya Bashivan, Irina Rish:
Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques. CoRR abs/2407.11121 (2024) - [i68]Karolis Jucys, George Adamopoulos, Mehrab Hamidi, Stephanie Milani, Mohammad Reza Samsami, Artem Zholus, Sonia Joseph, Blake A. Richards, Irina Rish, Özgür Simsek:
Interpretability in Action: Exploratory Analysis of VPT, a Minecraft Agent. CoRR abs/2407.12161 (2024) - [i67]Ayush Kaushal, Tejas Pandey, Tejas Vaidhya, Aaryan Bhagat, Irina Rish:
Spectra: A Comprehensive Study of Ternary, Quantized, and FP16 Language Models. CoRR abs/2407.12327 (2024) - [i66]Mohammad-Javad Darvishi Bayazi, Md Rifat Arefin, Jocelyn Faubert, Irina Rish:
VFA: Vision Frequency Analysis of Foundation Models and Human. CoRR abs/2409.05817 (2024) - [i65]Andrew Robert Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin:
Context is Key: A Benchmark for Forecasting with Essential Textual Information. CoRR abs/2410.18959 (2024) - [i64]Md Rifat Arefin, Gopeshh Subbaraj, Nicolas Gontier, Yann LeCun, Irina Rish, Ravid Shwartz-Ziv, Christopher Pal:
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning. CoRR abs/2411.02344 (2024) - [i63]Nizar Islah, Justine Gehring, Diganta Misra, Eilif B. Müller, Irina Rish, Terry Yue Zhuo, Massimo Caccia:
GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models. CoRR abs/2411.05830 (2024) - [i62]Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother, Irina Rish, Glen Berseth, Sanjiban Choudhury:
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching. CoRR abs/2411.07007 (2024) - [i61]Maurice Weber, Daniel Y. Fu, Quentin Anthony, Yonatan Oren, Shane Adams, Anton Alexandrov, Xiaozhong Lyu, Huu Nguyen, Xiaozhe Yao, Virginia Adams, Ben Athiwaratkun, Rahul Chalamala, Kezhen Chen, Max Ryabinin, Tri Dao, Percy Liang, Christopher Ré, Irina Rish, Ce Zhang:
RedPajama: an Open Dataset for Training Large Language Models. CoRR abs/2411.12372 (2024) - [i60]Matthew Riemer, Gopeshh Subbaraj, Glen Berseth, Irina Rish:
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference. CoRR abs/2412.14355 (2024) - 2023
- [j15]Jean-Christophe Gagnon-Audet, Kartik Ahuja, Mohammad-Javad Darvishi Bayazi, Pooneh Mousavi, Guillaume Dumas, Irina Rish:
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series. Trans. Mach. Learn. Res. 2023 (2023) - [j14]Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Irina Rish, Eugene Belilovsky:
Gradient Masked Averaging for Federated Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c73]Ardavan Salehi Nobandegani, Irina Rish, Thomas R. Shultz:
AI Agents Learn to Trust. CogSci 2023 - [c72]Timothée Lesort, Oleksiy Ostapenko, Pau Rodríguez, Diganta Misra, Md Rifat Arefin, Laurent Charlin, Irina Rish:
Challenging Common Assumptions about Catastrophic Forgetting and Knowledge Accumulation. CoLLAs 2023: 43-65 - [c71]Djallel Bouneffouf, Mayank Agarwal, Irina Rish:
Dialogue System with Missing Observation. ICASSP 2023: 1-5 - [c70]Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger:
Broken Neural Scaling Laws. ICLR 2023 - [c69]Arnav Kumar Jain, Lucas Lehnert, Irina Rish, Glen Berseth:
Maximum State Entropy Exploration using Predecessor and Successor Representations. NeurIPS 2023 - [i59]Baihan Lin, Djallel Bouneffouf, Irina Rish:
A Survey on Compositional Generalization in Applications. CoRR abs/2302.01067 (2023) - [i58]Alexis Roger, Esma Aïmeur, Irina Rish:
Towards ethical multimodal systems. CoRR abs/2304.13765 (2023) - [i57]Pascal Tikeng Notsawo Jr., Hattie Zhou, Mohammad Pezeshki, Irina Rish, Guillaume Dumas:
Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok. CoRR abs/2306.13253 (2023) - [i56]Arnav Kumar Jain, Lucas Lehnert, Irina Rish, Glen Berseth:
Maximum State Entropy Exploration using Predecessor and Successor Representations. CoRR abs/2306.14808 (2023) - [i55]Germán Abrevaya, Mahta Ramezanian-Panahi, Jean-Christophe Gagnon-Audet, Irina Rish, Pablo Polosecki, Silvina Ponce Dawson, Guillermo A. Cecchi, Guillaume Dumas:
GOKU-UI: Ubiquitous Inference through Attention and Multiple Shooting for Continuous-time Generative Models. CoRR abs/2307.05735 (2023) - [i54]Kshitij Gupta, Benjamin Thérien, Adam Ibrahim, Mats L. Richter, Quentin Anthony, Eugene Belilovsky, Irina Rish, Timothée Lesort:
Continual Pre-Training of Large Language Models: How to (re)warm your model? CoRR abs/2308.04014 (2023) - [i53]Mohammad-Javad Darvishi Bayazi, Mohammad Sajjad Ghaemi, Timothée Lesort, Md Rifat Arefin, Jocelyn Faubert, Irina Rish:
Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning. CoRR abs/2309.10910 (2023) - [i52]Ayush Kaushal, Tejas Vaidhya, Irina Rish:
LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression. CoRR abs/2309.14021 (2023) - [i51]Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Bilos, Hena Ghonia, Nadhir Vincent Hassen, Anderson Schneider, Sahil Garg, Alexandre Drouin, Nicolas Chapados, Yuriy Nevmyvaka, Irina Rish:
Lag-Llama: Towards Foundation Models for Time Series Forecasting. CoRR abs/2310.08278 (2023) - [i50]Ardavan Salehi Nobandegani, Irina Rish, Thomas R. Shultz:
Towards Machines that Trust: AI Agents Learn to Trust in the Trust Game. CoRR abs/2312.12868 (2023) - 2022
- [j13]Mahta Ramezanian-Panahi, Germán Abrevaya, Jean-Christophe Gagnon-Audet, Vikram Voleti, Irina Rish, Guillaume Dumas:
Generative Models of Brain Dynamics. Frontiers Artif. Intell. 5 (2022) - [j12]Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup:
Towards Continual Reinforcement Learning: A Review and Perspectives. J. Artif. Intell. Res. 75: 1401-1476 (2022) - [c68]Ardavan Salehi Nobandegani, Thomas R. Shultz, Irina Rish:
Cognitive Models as Simulators: The Case of Moral Decision-Making. CogSci 2022 - [c67]Oleksiy Ostapenko, Timothée Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, Laurent Charlin:
Continual Learning with Foundation Models: An Empirical Study of Latent Replay. CoLLAs 2022: 60-91 - [c66]Shanel Gauthier, Benjamin Thérien, Laurent Alsène-Racicot, Muawiz Chaudhary, Irina Rish, Eugene Belilovsky, Michael Eickenberg, Guy Wolf:
Parametric Scattering Networks. CVPR 2022: 5739-5748 - [c65]Jean-Christophe Gagnon-Audet, Soroosh Shahtalebi, Frank Rudzicz, Irina Rish:
A Remedy For Distributional Shifts Through Expected Domain Translation. ICASSP 2022: 4523-4527 - [c64]Sarthak Mittal, Sharath Chandra Raparthy, Irina Rish, Yoshua Bengio, Guillaume Lajoie:
Compositional Attention: Disentangling Search and Retrieval. ICLR 2022 - [c63]Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake A. Richards, Yoshua Bengio:
Towards Scaling Difference Target Propagation by Learning Backprop Targets. ICML 2022: 5968-5987 - [c62]Matthew Riemer, Sharath Chandra Raparthy, Ignacio Cases, Gopeshh Subbaraj, Maximilian Puelma Touzel, Irina Rish:
Continual Learning In Environments With Polynomial Mixing Times. NeurIPS 2022 - [i49]Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Eugene Belilovsky, Irina Rish:
Gradient Masked Averaging for Federated Learning. CoRR abs/2201.11986 (2022) - [i48]Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake A. Richards, Yoshua Bengio:
Towards Scaling Difference Target Propagation by Learning Backprop Targets. CoRR abs/2201.13415 (2022) - [i47]Jean-Christophe Gagnon-Audet, Kartik Ahuja, Mohammad-Javad Darvishi Bayazi, Guillaume Dumas, Irina Rish:
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks. CoRR abs/2203.09978 (2022) - [i46]Diganta Misra, Bharat Runwal, Tianlong Chen, Zhangyang Wang, Irina Rish:
APP: Anytime Progressive Pruning. CoRR abs/2204.01640 (2022) - [i45]Oleksiy Ostapenko, Timothée Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, Laurent Charlin:
Foundational Models for Continual Learning: An Empirical Study of Latent Replay. CoRR abs/2205.00329 (2022) - [i44]Timothée Lesort, Oleksiy Ostapenko, Diganta Misra, Md Rifat Arefin, Pau Rodríguez, Laurent Charlin, Irina Rish:
Scaling the Number of Tasks in Continual Learning. CoRR abs/2207.04543 (2022) - [i43]Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas, Irina Rish, David Krueger, Pouya Bashivan:
Towards Out-of-Distribution Adversarial Robustness. CoRR abs/2210.03150 (2022) - [i42]Ardavan Salehi Nobandegani, Thomas R. Shultz, Irina Rish:
Cognitive Models as Simulators: The Case of Moral Decision-Making. CoRR abs/2210.04121 (2022) - [i41]Jean-Charles Layoun, Alexis Roger, Irina Rish:
Aligning MAGMA by Few-Shot Learning and Finetuning. CoRR abs/2210.14161 (2022) - [i40]Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger:
Broken Neural Scaling Laws. CoRR abs/2210.14891 (2022) - [i39]Alessio Mora, Irene Tenison, Paolo Bellavista, Irina Rish:
Knowledge Distillation for Federated Learning: a Practical Guide. CoRR abs/2211.04742 (2022) - 2021
- [j11]Germán Abrevaya, Guillaume Dumas, Aleksandr Y. Aravkin, Peng Zheng, Jean-Christophe Gagnon-Audet, James R. Kozloski, Pablo Polosecki, Guillaume Lajoie, David D. Cox, Silvina Ponce Dawson, Guillermo A. Cecchi, Irina Rish:
Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks. Neural Comput. 33(8): 2087-2127 (2021) - [c61]Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay, Yasaman Khazaeni, Irina Rish:
Double-Linear Thompson Sampling for Context-Attentive Bandits. ICASSP 2021: 3450-3454 - [c60]Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay, Mayank Agarwal, Yasaman Khazaeni, Irina Rish:
Toward Skills Dialog Orchestration with Online Learning. ICASSP 2021: 3600-3604 - [c59]Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Benjamin Müller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilaniuk, David L. Buckeridge, Gaétan Marceau-Caron, Pierre Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Christopher J. Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams:
Predicting Infectiousness for Proactive Contact Tracing. ICLR 2021 - [c58]Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay, Irina Rish, Yasaman Khazaeni:
Toward Optimal Solution for the Context-Attentive Bandit Problem. IJCAI 2021: 3493-3500 - [c57]Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish:
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. NeurIPS 2021: 3438-3450 - [c56]Pouya Bashivan, Reza Bayat, Adam Ibrahim, Kartik Ahuja, Mojtaba Faramarzi, Touraj Laleh, Blake A. Richards, Irina Rish:
Adversarial Feature Desensitization. NeurIPS 2021: 10665-10677 - [i38]Timothée Lesort, Massimo Caccia, Irina Rish:
Understanding Continual Learning Settings with Data Distribution Drift Analysis. CoRR abs/2104.01678 (2021) - [i37]Sreya Francis, Irene Tenison, Irina Rish:
Towards Causal Federated Learning For Enhanced Robustness and Privacy. CoRR abs/2104.06557 (2021) - [i36]Irene Tenison, Sreya Francis, Irina Rish:
Gradient Masked Federated Optimization. CoRR abs/2104.10322 (2021) - [i35]Timothée Lesort, Thomas George, Irina Rish:
Continual Learning in Deep Networks: an Analysis of the Last Layer. CoRR abs/2106.01834 (2021) - [i34]Soroosh Shahtalebi, Jean-Christophe Gagnon-Audet, Touraj Laleh, Mojtaba Faramarzi, Kartik Ahuja, Irina Rish:
SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization. CoRR abs/2106.02266 (2021) - [i33]Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish:
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. CoRR abs/2106.06607 (2021) - [i32]Shanel Gauthier, Benjamin Thérien, Laurent Alsène-Racicot, Irina Rish, Eugene Belilovsky, Michael Eickenberg, Guy Wolf:
Parametric Scattering Networks. CoRR abs/2107.09539 (2021) - [i31]Fabrice Normandin, Florian Golemo, Oleksiy Ostapenko, Pau Rodríguez, Matthew D. Riemer, Julio Hurtado, Khimya Khetarpal, Dominic Zhao, Ryan Lindeborg, Timothée Lesort, Laurent Charlin, Irina Rish, Massimo Caccia:
Sequoia: A Software Framework to Unify Continual Learning Research. CoRR abs/2108.01005 (2021) - [i30]Nadhir Hassen, Irina Rish:
Approximate Bayesian Optimisation for Neural Networks. CoRR abs/2108.12461 (2021) - [i29]Gabriele Prato, Simon Guiroy, Ethan Caballero, Irina Rish, Sarath Chandar:
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers. CoRR abs/2110.06990 (2021) - [i28]Sarthak Mittal, Sharath Chandra Raparthy, Irina Rish, Yoshua Bengio, Guillaume Lajoie:
Compositional Attention: Disentangling Search and Retrieval. CoRR abs/2110.09419 (2021) - [i27]Matthew Riemer, Sharath Chandra Raparthy, Ignacio Cases, Gopeshh Subbaraj, Maximilian Puelma Touzel, Irina Rish:
Continual Learning In Environments With Polynomial Mixing Times. CoRR abs/2112.07066 (2021) - 2020
- [c55]Sahil Garg, Irina Rish, Guillermo A. Cecchi, Palash Goyal, Sarik Ghazarian, Shuyang Gao, Greg Ver Steeg, Aram Galstyan:
Modeling Dialogues with Hashcode Representations: A Nonparametric Approach. AAAI 2020: 3970-3979 - [c54]Baihan Lin, Guillermo A. Cecchi, Djallel Bouneffouf, Jenna M. Reinen, Irina Rish:
A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry. AAMAS 2020: 744-752 - [c53]Djallel Bouneffouf, Irina Rish, Charu C. Aggarwal:
Survey on Applications of Multi-Armed and Contextual Bandits. CEC 2020: 1-8 - [c52]Baihan Lin, Guillermo A. Cecchi, Djallel Bouneffouf, Jenna M. Reinen, Irina Rish:
Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL. HBAI@IJCAI 2020: 14-33 - [c51]Massimo Caccia, Pau Rodríguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Page-Caccia, Issam Hadj Laradji, Irina Rish, Alexandre Lacoste, David Vázquez, Laurent Charlin:
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning. NeurIPS 2020 - [i26]Massimo Caccia, Pau Rodríguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam H. Laradji, Irina Rish, Alexandre Lacoste, David Vázquez, Laurent Charlin:
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning. CoRR abs/2003.05856 (2020) - [i25]Victor Schmidt, Makesh Narsimhan Sreedhar, Mostafa ElAraby, Irina Rish:
Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation. CoRR abs/2004.00161 (2020) - [i24]Baihan Lin, Guillermo A. Cecchi, Djallel Bouneffouf, Jenna M. Reinen, Irina Rish:
Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL. CoRR abs/2005.04544 (2020) - [i23]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:
COVI White Paper. CoRR abs/2005.08502 (2020) - [i22]Pouya Bashivan, Blake A. Richards, Irina Rish:
Adversarial Feature Desensitization. CoRR abs/2006.04621 (2020) - [i21]Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay, Yasaman Khazaeni, Irina Rish:
Double-Linear Thompson Sampling for Context-Attentive Bandits. CoRR abs/2010.09473 (2020) - [i20]Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif B. Müller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilaniuk, David L. Buckeridge, Gaétan Marceau Caron, Pierre Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams:
Predicting Infectiousness for Proactive Contact Tracing. CoRR abs/2010.12536 (2020) - [i19]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 L. Buckeridge, Joumana Ghosn, Yang Zhang, Bernhard Schölkopf, Jian Tang, Irina Rish, Christopher Joseph Pal, Joanna Merckx, Eilif B. Müller, Yoshua Bengio:
COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing. CoRR abs/2010.16004 (2020) - [i18]Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup:
Towards Continual Reinforcement Learning: A Review and Perspectives. CoRR abs/2012.13490 (2020)
2010 – 2019
- 2019
- [c50]Sahil Garg, Aram Galstyan, Greg Ver Steeg, Irina Rish, Guillermo A. Cecchi, Shuyang Gao:
Kernelized Hashcode Representations for Relation Extraction. AAAI 2019: 6431-6440 - [c49]Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro:
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference. ICLR (Poster) 2019 - [c48]Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf:
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables. ICML 2019: 1193-1202 - [c47]Jolie McDonnell, William Hord, Jenna M. Reinen, Pablo Polosecki, Irina Rish, Guillermo A. Cecchi:
Predicting conversion to psychosis in clinical high risk patients using resting-state functional MRI features. Biomedical Applications in Molecular, Structural, and Functional Imaging 2019: 109532A - [i17]Pouya Bashivan, Martin Schrimpf, Robert Ajemian, Irina Rish, Matthew Riemer, Yuhai Tu:
Continual Learning with Self-Organizing Maps. CoRR abs/1904.09330 (2019) - [i16]Djallel Bouneffouf, Irina Rish:
A Survey on Practical Applications of Multi-Armed and Contextual Bandits. CoRR abs/1904.10040 (2019) - [i15]Baihan Lin, Guillermo A. Cecchi, Djallel Bouneffouf, Jenna M. Reinen, Irina Rish:
Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders. CoRR abs/1906.11286 (2019) - 2018
- [c46]Baihan Lin, Djallel Bouneffouf, Guillermo A. Cecchi, Irina Rish:
Contextual Bandit with Adaptive Feature Extraction. ICDM Workshops 2018: 937-944 - [c45]Sahil Garg, Guillermo A. Cecchi, Irina Rish, Shuyang Gao, Greg Ver Steeg, Sarik Ghazarian, Palash Goyal, Aram Galstyan:
Dialogue Modeling Via Hash Functions. LaCATODA@IJCAI 2018: 24-36 - [i14]Baihan Lin, Guillermo A. Cecchi, Djallel Bouneffouf, Irina Rish:
Adaptive Representation Selection in Contextual Bandit with Unlabeled History. CoRR abs/1802.00981 (2018) - [i13]Germán Abrevaya, Aleksandr Y. Aravkin, Guillermo A. Cecchi, Irina Rish, Pablo Polosecki, Peng Zheng, Silvina Ponce Dawson:
Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM. CoRR abs/1805.09874 (2018) - [i12]Anna Choromanska, Sadhana Kumaravel, Ronny Luss, Irina Rish, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf:
Beyond Backprop: Alternating Minimization with co-Activation Memory. CoRR abs/1806.09077 (2018) - [i11]Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro:
Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference. CoRR abs/1810.11910 (2018) - 2017
- [j10]Guillermo A. Cecchi, Viatcheslav Gurev, Steve Heisig, Raquel Norel, Irina Rish, Samantha R. Schrecke:
Computing the structure of language for neuropsychiatric evaluation. IBM J. Res. Dev. 61(2/3): 1:1-1:10 (2017) - [j9]Irina Rish, Guillermo A. Cecchi:
Holographic brain: Distributed versus local activation patterns in fMRI. IBM J. Res. Dev. 61(2/3): 3:1-3:9 (2017) - [j8]Pablo Polosecki, Eduardo Castro, Andrew Wood, John H. Warner, Irina Rish, Guillermo A. Cecchi:
Computational psychiatry: Advancing predictive modeling of neurodegeneration with neuroimaging of Huntington's disease. IBM J. Res. Dev. 61(2/3): 4:1-4:10 (2017) - [c44]Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi:
Bandit Models of Human Behavior: Reward Processing in Mental Disorders. AGI 2017: 237-248 - [c43]Sahil Garg, Irina Rish, Guillermo A. Cecchi, Aurélie C. Lozano:
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World. ICLR (Workshop) 2017 - [c42]Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi, Raphaël Féraud:
Context Attentive Bandits: Contextual Bandit with Restricted Context. IJCAI 2017: 1468-1475 - [c41]Sahil Garg, Irina Rish, Guillermo A. Cecchi, Aurélie C. Lozano:
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World. IJCAI 2017: 1696-1702 - [c40]Mina Gheiratmand, Irina Rish, Guillermo A. Cecchi, Matthew R. G. Brown, Russell Greiner, Pouya Bashivan, Pablo Polosecki, Serdar M. Dursun:
Learning discriminative functional network features of schizophrenia. Biomedical Applications in Molecular, Structural, and Functional Imaging 2017: 101371A - [i10]Sahil Garg, Irina Rish, Guillermo A. Cecchi, Aurélie C. Lozano:
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World. CoRR abs/1701.06106 (2017) - [i9]Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi, Raphaël Féraud:
Context Attentive Bandits: Contextual Bandit with Restricted Context. CoRR abs/1705.03821 (2017) - [i8]Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi:
Bandit Models of Human Behavior: Reward Processing in Mental Disorders. CoRR abs/1706.02897 (2017) - [i7]Sahil Garg, Aram Galstyan, Irina Rish, Guillermo A. Cecchi, Shuyang Gao:
Efficient Representation for Natural Language Processing via Kernelized Hashcodes. CoRR abs/1711.04044 (2017) - [i6]Jumana Dakka, Pouya Bashivan, Mina Gheiratmand, Irina Rish, Shantenu Jha, Russell Greiner:
Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks. CoRR abs/1712.00512 (2017) - 2016
- [j7]Dan He, Irina Rish, David Haws, Laxmi Parida:
MINT: Mutual Information Based Transductive Feature Selection for Genetic Trait Prediction. IEEE ACM Trans. Comput. Biol. Bioinform. 13(3): 578-583 (2016) - [c39]Irina Rish, Pouya Bashivan, Guillermo A. Cecchi, Rita Z. Goldstein:
Evaluating effects of methylphenidate on brain activity in cocaine addiction: a machine-learning approach. Biomedical Applications in Molecular, Structural, and Functional Imaging 2016: 97880O - [c38]Pouya Bashivan, Irina Rish, Mohammed Yeasin, Noel Codella:
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks. ICLR (Poster) 2016 - [e2]Irina Rish, Georg Langs, Leila Wehbe, Guillermo A. Cecchi, Kai-min Kevin Chang, Brian Murphy:
Machine Learning and Interpretation in Neuroimaging - 4th International Workshop, MLINI 2014, Held at NIPS 2014, Montreal, QC, Canada, December 13, 2014, Revised Selected Papers. Lecture Notes in Computer Science 9444, Springer 2016, ISBN 978-3-319-45173-2 [contents] - [i5]Pouya Bashivan, Irina Rish, Steve Heisig:
Mental State Recognition via Wearable EEG. CoRR abs/1602.00985 (2016) - 2014
- [c37]Dan He, Irina Rish, Laxmi Parida:
Transductive HSIC Lasso. SDM 2014: 154-162 - 2013
- [c36]Irina Rish:
Functional MRI Analysis with Sparse Models. ECML/PKDD (3) 2013: 632-636 - [i4]Irina Rish, Kalev Kask, Rina Dechter:
Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding. CoRR abs/1301.7409 (2013) - [i3]Rina Dechter, Irina Rish:
A Scheme for Approximating Probabilistic Inference. CoRR abs/1302.1534 (2013) - [i2]Dan He, Irina Rish, David Haws, Simon Teyssedre, Zivan Karaman, Laxmi Parida:
MINT: Mutual Information based Transductive Feature Selection for Genetic Trait Prediction. CoRR abs/1310.1659 (2013) - 2012
- [j6]Guillermo A. Cecchi, Lejian Huang, Javeria Ali Hashmi, Marwan N. Baliki, María V. Centeno, Irina Rish, Apkar Vania Apkarian:
Predictive Dynamics of Human Pain Perception. PLoS Comput. Biol. 8(10) (2012) - [c35]Irina Rish, Guillermo A. Cecchi, Kyle Heuton, Marwan N. Baliki, Apkar Vania Apkarian:
Sparse regression analysis of task-relevant information distribution in the brain. Image Processing 2012: 831412 - [c34]Jean Honorio, Dimitris Samaras, Irina Rish, Guillermo A. Cecchi:
Variable Selection for Gaussian Graphical Models. AISTATS 2012: 538-546 - [e1]Georg Langs, Irina Rish, Moritz Grosse-Wentrup, Brian Murphy:
Machine Learning and Interpretation in Neuroimaging - International Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions. Lecture Notes in Computer Science 7263, Springer 2012, ISBN 978-3-642-34712-2 [contents] - [i1]Alice X. Zheng, Irina Rish, Alina Beygelzimer:
Efficient Test Selection in Active Diagnosis via Entropy Approximation. CoRR abs/1207.1418 (2012) - 2010
- [c33]Irina Rish, Guillermo A. Cecchi, Marwan N. Baliki, Apkar Vania Apkarian:
Sparse Regression Models of Pain Perception. Brain Informatics 2010: 212-223 - [c32]Katya Scheinberg, Irina Rish, Narges Bani Asadi:
Sparse Markov net learning with priors on regularization parameters. ISAIM 2010 - [c31]Katya Scheinberg, Irina Rish:
Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach. ECML/PKDD (3) 2010: 196-212
2000 – 2009
- 2009
- [j5]Melissa K. Carroll, Guillermo A. Cecchi, Irina Rish, Rahul Garg, A. Ravishankar Rao:
Prediction and interpretation of distributed neural activity with sparse models. NeuroImage 44(1): 112-122 (2009) - [c30]Irina Rish, Genady Grabarnik:
Sparse signal recovery with exponential-family noise. Allerton 2009: 60-66 - [c29]Narges Bani Asadi, Irina Rish, Katya Scheinberg, Dimitri Kanevsky, Bhuvana Ramabhadran:
Map approach to learning sparse Gaussian Markov networks. ICASSP 2009: 1721-1724 - [c28]Guillermo A. Cecchi, Irina Rish, Benjamin Thyreau, Bertrand Thirion, Marion Plaze, Marie-Laure Paillère-Martinot, Catherine Martelli, Jean-Luc Martinot, Jean-Baptiste Poline:
Discriminative Network Models of Schizophrenia. NIPS 2009: 252-260 - 2008
- [c27]Irina Rish, Genady Grabarnik, Guillermo A. Cecchi, Francisco Pereira, Geoffrey J. Gordon:
Closed-form supervised dimensionality reduction with generalized linear models. ICML 2008: 832-839 - [c26]Irina Rish, Gerald Tesauro:
Active Collaborative Prediction with Maximum Margin Matrix Factorization. ISAIM 2008 - 2007
- [c25]Alina Beygelzimer, Jeffrey O. Kephart, Irina Rish:
Evaluation of Optimization Methods for Network Bottleneck Diagnosis. ICAC 2007: 20 - [c24]Irina Rish, Gerald Tesauro:
Estimating End-to-End Performance by Collaborative Prediction with Active Sampling. Integrated Network Management 2007: 294-303 - [c23]Gaurav Chandalia, Irina Rish:
Blind source separation approach to performance diagnosis and dependency discovery. Internet Measurement Conference 2007: 259-264 - [c22]Natalia Odintsova, Irina Rish:
Empirical Study of Topology Effects on Diagnosis in Computer Networks. MASS 2007: 1-6 - 2006
- [c21]Aleks Jakulin, Irina Rish:
Bayesian Learning of Markov Network Structure. ECML 2006: 198-209 - 2005
- [j4]Irina Rish, Mark Brodie, Sheng Ma, Natalia Odintsova, Alina Beygelzimer, Genady Grabarnik, Karina Hernandez:
Adaptive diagnosis in distributed systems. IEEE Trans. Neural Networks 16(5): 1088-1109 (2005) - [c20]Alina Beygelzimer, Mark Brodie, Sheng Ma, Irina Rish:
Test-based diagnosis: tree and matrix representations. Integrated Network Management 2005: 529-542 - [c19]Alina Beygelzimer, Emre Erdogan, Sheng Ma, Irina Rish:
Statictical Models for Unequally Spaced Time Series. SDM 2005: 626-630 - [c18]Alice X. Zheng, Irina Rish, Alina Beygelzimer:
Efficient Test Selection in Active Diagnosis via Entropy Approximation. UAI 2005: 675- - 2004
- [c17]Alina Beygelzimer, Geoffrey Grinstein, Ralph Linsker, Irina Rish:
Improving Network Robustness. ICAC 2004: 322-323 - [c16]Irina Rish, Mark Brodie, Natalia Odintsova, Sheng Ma, Genady Grabarnik:
Real-time problem determination in distributed systems using active probing. NOMS (1) 2004: 133-146 - 2003
- [j3]Rina Dechter, Irina Rish:
Mini-buckets: A general scheme for bounded inference. J. ACM 50(2): 107-153 (2003) - [c15]Ricardo Vilalta, Irina Rish:
A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes. ECML 2003: 444-455 - [c14]Mark Brodie, Irina Rish, Sheng Ma, Natalia Odintsova:
Active Probing Strategies for Problem Diagnosis in Distributed Systems. IJCAI 2003: 1337-1338 - [c13]Ramendra K. Sahoo, Adam J. Oliner, Irina Rish, Manish Gupta, José E. Moreira, Sheng Ma, Ricardo Vilalta, Anand Sivasubramaniam:
Critical event prediction for proactive management in large-scale computer clusters. KDD 2003: 426-435 - [c12]Alina Beygelzimer, Irina Rish:
Approximability of Probability Distributions. NIPS 2003: 377-384 - 2002
- [j2]Mark Brodie, Irina Rish, Sheng Ma:
Intelligent probing: A cost-effective approach to fault diagnosis in computer networks. IBM Syst. J. 41(3): 372-385 (2002) - [c11]Irina Rish, Mark Brodie, Sheng Ma:
Accuracy vs. Efficiency Trade-offs in Probabilistic Diagnosis. AAAI/IAAI 2002: 560-566 - [c10]Alina Beygelzimer, Irina Rish:
Inference Complexity as a Model-Selection Criterion for Learning Bayesian Networks. KR 2002: 558-567 - 2001
- [c9]Mark Brodie, Irina Rish, Sheng Ma:
Optimizing Probe Selection for Fault Localization. DSOM 2001: 88-98 - [c8]Ricardo Vilalta, Mark Brodie, Daniel Oblinger, Irina Rish:
A Unified Framework for Evaluation Metrics in Classification Using Decision Trees. ECML 2001: 503-514 - 2000
- [j1]Irina Rish, Rina Dechter:
Resolution versus Search: Two Strategies for SAT. J. Autom. Reason. 24(1/2): 225-275 (2000) - [c7]Joseph L. Hellerstein, T. S. Jayram, Irina Rish:
Recognizing End-User Transactions in Performance Management. AAAI/IAAI 2000: 596-602
1990 – 1999
- 1998
- [c6]Irina Rish, Kalev Kask, Rina Dechter:
Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding. UAI 1998: 455-463 - 1997
- [c5]Daniel Frost, Irina Rish, Lluís Vila:
Summarizing CSP Hardness with Continuous Probability Distributions. AAAI/IAAI 1997: 327-333 - [c4]Irina Rish, Daniel Frost:
Statistical Analysis of Backtracking on Inconsistent CSPs. CP 1997: 150-162 - [c3]Rina Dechter, Irina Rish:
A Scheme for Approximating Probabilistic Inference. UAI 1997: 132-141 - 1996
- [c2]Irina Rish, Rina Dechter:
To Guess or to Think? Hybrid Algorithms for SAT (Extended Abstract). CP 1996: 555-556 - 1994
- [c1]Rina Dechter, Irina Rish:
Directional Resolution: The Davis-Putnam Procedure, Revisited. KR 1994: 134-145
Coauthor Index
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