Rish, Irina

87 publications

ICML 2025 AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N Tianyu Zhang, Andrew Robert Williams, Phillip Wozny, Kai-Hendrik Cohrs, Koen Ponse, Marco Jiralerspong, Soham Rajesh Phade, Sunil Srinivasa, Lu Li, Yang Zhang, Prateek Gupta, Erman Acar, Irina Rish, Yoshua Bengio, Stephan Zheng

ICML 2025 Context Is Key: A Benchmark for Forecasting with Essential Textual Information Andrew Robert Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin

TMLR 2025 Continual Pre-Training of MoEs: How Robust Is Your Router? Benjamin Thérien, Charles-Étienne Joseph, Zain Sarwar, Ashwinee Panda, Anirban Das, Shi-Xiong Zhang, Stephen Rawls, Sambit Sahu, Eugene Belilovsky, Irina Rish

ICLR 2025 Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference Matthew Riemer, Gopeshh Subbaraj, Glen Berseth, Irina Rish

ICLR 2025 Handling Delay in Real-Time Reinforcement Learning Ivan Anokhin, Rishav Rishav, Matthew Riemer, Stephen Chung, Irina Rish, Samira Ebrahimi Kahou

TMLR 2025 Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons Simon Dufort-Labbé, Pierluca D'Oro, Evgenii Nikishin, Irina Rish, Pierre-Luc Bacon, Razvan Pascanu, Aristide Baratin

ICLR 2025 Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother, Irina Rish, Glen Berseth, Sanjiban Choudhury

ICLRW 2025 Scaling Laws and Efficient Inference for Ternary Language Models Tejas Vaidhya, Ayush Kaushal, Vineet Jain, Francis Couture-Harpin, Prashant Shishodia, Majid Behbahani, Irina Rish, Yuriy Nevmyvaka

ICLR 2025 Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning Md Rifat Arefin, Gopeshh Subbaraj, Nicolas Gontier, Yann LeCun, Irina Rish, Ravid Shwartz-Ziv, Christopher Pal

ICLR 2025 Surprising Effectiveness of Pretraining Ternary Language Model at Scale Ayush Kaushal, Tejas Vaidhya, Arnab Kumar Mondal, Tejas Pandey, Aaryan Bhagat, Irina Rish

NeurIPSW 2024 $\mu$LO: Compute-Efficient Meta-Generalization of Learned Optimizers Benjamin Thérien, Charles-Étienne Joseph, Boris Knyazev, Edouard Oyallon, Irina Rish, Eugene Belilovsky

ICMLW 2024 Adversarial Training with Synthesized Data: A Path to Robust and Generalizable Neural Networks Reza Bayat, Irina Rish

NeurIPSW 2024 Context Is Key: A Benchmark for Forecasting with Essential Textual Information Arjun Ashok, Andrew Robert Williams, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin

TMLR 2024 Effective Latent Differential Equation Models via Attention and Multiple Shooting Germán Abrevaya, Mahta Ramezanian-Panahi, Jean-Christophe Gagnon-Audet, Pablo Polosecki, Irina Rish, Silvina Ponce Dawson, Guillermo Cecchi, Guillaume Dumas

NeurIPSW 2024 General-Purpose Brain Foundation Models for Time-Series Neuroimaging Data Mohammad Javad Darvishi Bayazi, Hena Ghonia, Roland Riachi, Bruno Aristimunha, Arian Khorasani, Md Rifat Arefin, Amin Darabi, Guillaume Dumas, Irina Rish

ICMLW 2024 Gradient Dissent in Language Model Training and Saturation Andrei Mircea, Ekaterina Lobacheva, Irina Rish

ICMLW 2024 Handling Delay in Reinforcement Learning Caused by Parallel Computations of Neurons Ivan Anokhin, Rishav Rishav, Stephen Chung, Irina Rish, Samira Ebrahimi Kahou

ICMLW 2024 Interpretability in Action: Exploratory Analysis of VPT, a Minecraft Agent Karolis Jucys, George Adamopoulos, Mehrab Hamidi, Stephanie Milani, Mohammad Reza Samsami, Artem Zholus, Sonia Joseph, Blake Aaron Richards, Irina Rish, Özgür Şimşek

ICMLW 2024 Is a Good Description Worth a Thousand Pictures? Reducing Multimodal Alignment to Text-Based, Unimodal Alignment Amin Memarian, Touraj Laleh, Irina Rish, Ardavan S. Nobandegani

IJCAI 2024 Knowledge Distillation in Federated Learning: A Practical Guide Alessio Mora, Irene Tenison, Paolo Bellavista, Irina Rish

NeurIPSW 2024 LLMs and Personalities: Inconsistencies Across Scales Tosato Tommaso, Mahmood Hegazy, David Lemay, Mohammed Abukalam, Irina Rish, Guillaume Dumas

NeurIPSW 2024 Language Model Scaling Laws and Zero-Sum Learning Andrei Mircea, Ekaterina Lobacheva, Supriyo Chakraborty, Nima Chitsazan, Irina Rish

ICMLW 2024 LoRD: Low-Rank Decomposition of Monolingual Code LLMs for One-Shot Compression Ayush Kaushal, Tejas Vaidhya, Irina Rish

ICMLW 2024 Lost in Translation: The Algorithmic Gap Between LMs and the Brain Tosato Tommaso, Tikeng Notsawo Pascal Junior, Helbling Saskia, Irina Rish, Guillaume Dumas

ICMLW 2024 Realtime Reinforcement Learning: Towards Rapid Asynchronous Deployment of Large Models Matthew Riemer, Gopeshh Subbaraj, Glen Berseth, Irina Rish

NeurIPS 2024 RedPajama: An Open Dataset for Training Large Language Models 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

ICMLW 2024 Revisiting Successor Features for Inverse Reinforcement Learning Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother, Irina Rish, Glen Berseth, Sanjiban Choudhury

ICMLW 2024 Scalable Approaches for a Theory of Many Minds Maximilian Puelma Touzel, Amin Memarian, Matthew Riemer, Andrei Mircea, Andrew Robert Williams, Elin Ahlstrand, Lucas Lehnert, Rupali Bhati, Guillaume Dumas, Irina Rish

TMLR 2024 Simple and Scalable Strategies to Continually Pre-Train Large Language Models Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats Leon Richter, Quentin Gregory Anthony, Eugene Belilovsky, Timothée Lesort, Irina Rish

ICMLW 2024 The Effect of Data Corruption on Multimodal Long Form Responses Daniel Z Kaplan, Alexis Roger, Mohamed Osman, Irina Rish

ICMLW 2024 Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques Rishika Bhagwatkar, Shravan Nayak, Reza Bayat, Alexis Roger, Daniel Z Kaplan, Pouya Bashivan, Irina Rish

ICMLW 2024 Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques Rishika Bhagwatkar, Shravan Nayak, Reza Bayat, Alexis Roger, Daniel Z Kaplan, Pouya Bashivan, Irina Rish

ICMLW 2024 TriLM vs FloatLM: Ternary LLMs Are More Performant than Quantized FP16 LLMs Ayush Kaushal, Tejas Vaidhya, Tejas Pandey, Aaryan Bhagat, Irina Rish

ICML 2024 Unsupervised Concept Discovery Mitigates Spurious Correlations Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi

NeurIPS 2024 Using Unity to Help Solve Reinforcement Learning Connor Brennan, Andrew Robert Williams, Omar G. Younis, Vedant Vyas, Daria Yasafova, Irina Rish

ICMLW 2024 VFA: Vision Frequency Analysis of Foundation Models and Human Mohammad Javad Darvishi Bayazi, Md Rifat Arefin, Jocelyn Faubert, Irina Rish

ICLR 2023 Broken Neural Scaling Laws Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger

ICLRW 2023 Broken Neural Scaling Laws Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger

CoLLAs 2023 Challenging Common Assumptions About Catastrophic Forgetting and Knowledge Accumulation Timothée Lesort, Oleksiy Ostapenko, Pau Rodríguez, Diganta Misra, Md Rifat Arefin, Laurent Charlin, Irina Rish

ICMLW 2023 Cognitive Models as Simulators: Using Cognitive Models to Tap into Implicit Human Feedback Ardavan S. Nobandegani, Thomas Shultz, Irina Rish

ICMLW 2023 Continual Pre-Training of Large Language Models: How to Re-Warm Your Model? Kshitij Gupta, Benjamin Thérien, Adam Ibrahim, Mats Leon Richter, Quentin Gregory Anthony, Eugene Belilovsky, Irina Rish, Timothée Lesort

NeurIPSW 2023 Effective Latent Differential Equation Models via Attention and Multiple Shooting Germán Abrevaya, Mahta Ramezanian-Panahi, Jean-Christophe Gagnon-Audet, Pablo Polosecki, Irina Rish, Silvina Ponce Dawson, Guillermo Cecchi, Guillaume Dumas

TMLR 2023 Gradient Masked Averaging for Federated Learning Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Irina Rish, Eugene Belilovsky

NeurIPSW 2023 Lag-Llama: Towards Foundation Models for Time Series Forecasting Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Biloš, Hena Ghonia, Nadhir Hassen, Anderson Schneider, Sahil Garg, Alexandre Drouin, Nicolas Chapados, Yuriy Nevmyvaka, Irina Rish

NeurIPS 2023 Maximum State Entropy Exploration Using Predecessor and Successor Representations Arnav Kumar Jain, Lucas Lehnert, Irina Rish, Glen Berseth

ICMLW 2023 Maximum State Entropy Exploration Using Predecessor and Successor Representations Arnav Kumar Jain, Lucas Lehnert, Irina Rish, Glen Berseth

ICMLW 2023 Towards Out-of-Distribution Adversarial Robustness Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas, Irina Rish, David Krueger, Pouya Bashivan

TMLR 2023 WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Jean-Christophe Gagnon-Audet, Kartik Ahuja, Mohammad Javad Darvishi Bayazi, Pooneh Mousavi, Guillaume Dumas, Irina Rish

NeurIPSW 2022 Broken Neural Scaling Laws Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger

ICLR 2022 Compositional Attention: Disentangling Search and Retrieval Sarthak Mittal, Sharath Chandra Raparthy, Irina Rish, Yoshua Bengio, Guillaume Lajoie

NeurIPS 2022 Continual Learning in Environments with Polynomial Mixing Times Matthew Riemer, Sharath Chandra Raparthy, Ignacio Cases, Gopeshh Subbaraj, Maximilian Puelma Touzel, Irina Rish

CoLLAs 2022 Continual Learning with Foundation Models: An Empirical Study of Latent Replay Oleksiy Ostapenko, Timothee Lesort, Pau Rodriguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, Laurent Charlin

CVPR 2022 Parametric Scattering Networks Shanel Gauthier, Benjamin Thérien, Laurent Alsène-Racicot, Muawiz Chaudhary, Irina Rish, Eugene Belilovsky, Michael Eickenberg, Guy Wolf

ICLRW 2022 Summarizing Societies: Agent Abstraction in Multi-Agent Reinforcement Learning Amin Memarian, Maximilian Puelma Touzel, Matthew Riemer, Rupali Bhati, Irina Rish

JAIR 2022 Towards Continual Reinforcement Learning: A Review and Perspectives Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup

ICML 2022 Towards Scaling Difference Target Propagation by Learning Backprop Targets Maxence M Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake Richards, Yoshua Bengio

NeurIPS 2021 Adversarial Feature Desensitization Pouya Bashivan, Reza Bayat, Adam Ibrahim, Kartik Ahuja, Mojtaba Faramarzi, Touraj Laleh, Blake Richards, Irina Rish

NeurIPS 2021 Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish

ICLR 2021 Predicting Infectiousness for Proactive Contact Tracing Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Benjamin Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilaniuk, David Buckeridge, Gaetan Caron, Pierre Luc Carrier, Joumana Ghosn, Satya Ortiz Gagne, Christopher Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

IJCAI 2021 Toward Optimal Solution for the Context-Attentive Bandit Problem Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay, Irina Rish, Yasaman Khazaeni

ICMLW 2020 Chaotic Continual Learning Touraj Laleh, Mojtaba Faramarzi, Irina Rish, Sarath Chandar

AAAI 2020 Modeling Dialogues with Hashcode Representations: A Nonparametric Approach Sahil Garg, Irina Rish, Guillermo A. Cecchi, Palash Goyal, Sarik Ghazarian, Shuyang Gao, Greg Ver Steeg, Aram Galstyan

NeurIPS 2020 Online Fast Adaptation and Knowledge Accumulation (OSAKA): A New Approach to Continual Learning Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Page-Caccia, Issam Hadj Laradji, Irina Rish, Alexandre Lacoste, David Vázquez, Laurent Charlin

ICML 2019 Beyond Backprop: Online Alternating Minimization with Auxiliary Variables Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf

AAAI 2019 Kernelized Hashcode Representations for Relation Extraction Sahil Garg, Aram Galstyan, Greg Ver Steeg, Irina Rish, Guillermo A. Cecchi, Shuyang Gao

ICLR 2019 Learning to Learn Without Forgetting by Maximizing Transfer and Minimizing Interference Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro

NeurIPSW 2019 Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish

IJCAI 2017 Context Attentive Bandits: Contextual Bandit with Restricted Context Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi, Raphaël Féraud

ICLR 2017 Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World Sahil Garg, Irina Rish, Guillermo A. Cecchi, Aurélie C. Lozano

IJCAI 2017 Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World Sahil Garg, Irina Rish, Guillermo A. Cecchi, Aurélie C. Lozano

ICLR 2016 Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks Pouya Bashivan, Irina Rish, Mohammed Yeasin, Noel Codella

ECML-PKDD 2013 Functional MRI Analysis with Sparse Models Irina Rish

AISTATS 2012 Variable Selection for Gaussian Graphical Models Jean Honorio, Dimitris Samaras, Irina Rish, Guillermo Cecchi

ECML-PKDD 2010 Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach Katya Scheinberg, Irina Rish

NeurIPS 2009 Discriminative Network Models of Schizophrenia Irina Rish, Benjamin Thyreau, Bertrand Thirion, Marion Plaze, Marie-laure Paillere-martinot, Catherine Martelli, Jean-luc Martinot, Jean-baptiste Poline, Guillermo A. Cecchi

ICML 2008 Closed-Form Supervised Dimensionality Reduction with Generalized Linear Models Irina Rish, Genady Grabarnik, Guillermo A. Cecchi, Francisco Pereira, Geoffrey J. Gordon

ECML-PKDD 2006 Bayesian Learning of Markov Network Structure Aleks Jakulin, Irina Rish

UAI 2005 Efficient Test Selection in Active Diagnosis via Entropy Approximation Alice X. Zheng, Irina Rish, Alina Beygelzimer

ECML-PKDD 2003 A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes Ricardo Vilalta, Irina Rish

IJCAI 2003 Active Probing Strategies for Problem Diagnosis in Distributed Systems Mark Brodie, Irina Rish, Sheng Ma, Natalia Odintsova

NeurIPS 2003 Approximability of Probability Distributions Alina Beygelzimer, Irina Rish

AAAI 2002 Accuracy vs. Efficiency Trade-Offs in Probabilistic Diagnosis Irina Rish, Mark Brodie, Sheng Ma

ECML-PKDD 2001 A Unified Framework for Evaluation Metrics in Classification Using Decision Trees Ricardo Vilalta, Mark Brodie, Daniel Oblinger, Irina Rish

AAAI 2000 Recognizing End-User Transactions in Performance Management Joseph L. Hellerstein, T. S. Jayram, Irina Rish

UAI 1998 Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding Irina Rish, Kalev Kask, Rina Dechter

UAI 1997 A Scheme for Approximating Probabilistic Inference Rina Dechter, Irina Rish

AAAI 1997 Summarizing CSP Hardness with Continuous Probability Distributions Daniel Frost, Irina Rish, Lluís Vila