Directional Resolution: The Davis-Putnam Procedure, Revisited. - Rina Dechter; Irina Rish (KR, 1994)

To Guess or to Think? Hybrid Algorithms for SAT (Extended Abstract). - Irina Rish; Rina Dechter (CP, 1996)

Summarizing CSP Hardness with Continuous Probability Distributions. - Daniel Frost; Irina Rish; Lluís Vila (AAAI/IAAI, 1997)

Statistical Analysis of Backtracking on Inconsistent CSPs. - Irina Rish; Daniel Frost (CP, 1997)

A Scheme for Approximating Probabilistic Inference. - Rina Dechter; Irina Rish (UAI, 1997)

Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding. - Irina Rish; Kalev Kask; Rina Dechter (UAI, 1998)

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

Resolution versus Search: Two Strategies for SAT. - Irina Rish; Rina Dechter (J. Autom. Reason., 2000)

Optimizing Probe Selection for Fault Localization. - Mark Brodie; Irina Rish; Sheng Ma (DSOM, 2001)

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

Accuracy vs. Efficiency Trade-offs in Probabilistic Diagnosis. - Irina Rish; Mark Brodie; Sheng Ma (AAAI/IAAI, 2002)

Inference Complexity as a Model-Selection Criterion for Learning Bayesian Networks. - Alina Beygelzimer; Irina Rish (KR, 2002)

Intelligent probing: A cost-effective approach to fault diagnosis in computer networks. - Mark Brodie; Irina Rish; Sheng Ma (IBM Syst. J., 2002)

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

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

Critical event prediction for proactive management in large-scale computer clusters. - Ramendra K. Sahoo; Adam J. Oliner; Irina Rish; Manish Gupta 002; José E. Moreira; Sheng Ma; Ricardo Vilalta; Anand Sivasubramaniam (KDD, 2003)

Approximability of Probability Distributions. - Alina Beygelzimer; Irina Rish (NIPS, 2003)

Mini-buckets: A general scheme for bounded inference. - Rina Dechter; Irina Rish (J. ACM, 2003)

Improving Network Robustness. - Alina Beygelzimer; Geoffrey Grinstein; Ralph Linsker; Irina Rish (ICAC, 2004)

Real-time problem determination in distributed systems using active probing. - Irina Rish; Mark Brodie; Natalia Odintsova; Sheng Ma; Genady Grabarnik (NOMS (1), 2004)

Test-based diagnosis: tree and matrix representations. - Alina Beygelzimer; Mark Brodie; Sheng Ma; Irina Rish (Integrated Network Management, 2005)

Statictical Models for Unequally Spaced Time Series. - Alina Beygelzimer; Emre Erdogan; Sheng Ma; Irina Rish (SDM, 2005)

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

Adaptive diagnosis in distributed systems. - Irina Rish; Mark Brodie; Sheng Ma; Natalia Odintsova; Alina Beygelzimer; Genady Grabarnik; Karina Hernandez (IEEE Trans. Neural Networks, 2005)

Bayesian Learning of Markov Network Structure. - Aleks Jakulin; Irina Rish (ECML, 2006)

Evaluation of Optimization Methods for Network Bottleneck Diagnosis. - Alina Beygelzimer; Jeffrey O. Kephart; Irina Rish (ICAC, 2007)

Estimating End-to-End Performance by Collaborative Prediction with Active Sampling. - Irina Rish; Gerald Tesauro (Integrated Network Management, 2007)

Blind source separation approach to performance diagnosis and dependency discovery. - Gaurav Chandalia; Irina Rish (Internet Measurement Conference, 2007)

Empirical Study of Topology Effects on Diagnosis in Computer Networks. - Natalia Odintsova; Irina Rish (MASS, 2007)

Closed-form supervised dimensionality reduction with generalized linear models. - Irina Rish; Genady Grabarnik; Guillermo A. Cecchi; Francisco Pereira 001; Geoffrey J. Gordon (ICML, 2008)

Active Collaborative Prediction with Maximum Margin Matrix Factorization. - Irina Rish; Gerald Tesauro (ISAIM, 2008)

Sparse signal recovery with exponential-family noise. - Irina Rish; Genady Grabarnik (Allerton, 2009)

Map approach to learning sparse Gaussian Markov networks. - Narges Bani Asadi; Irina Rish; Katya Scheinberg; Dimitri Kanevsky; Bhuvana Ramabhadran (ICASSP, 2009)

Discriminative Network Models of Schizophrenia. - Guillermo A. Cecchi; Irina Rish; Benjamin Thyreau; Bertrand Thirion; Marion Plaze; Marie-Laure Paillère-Martinot; Catherine Martelli; Jean-Luc Martinot; Jean-Baptiste Poline (NIPS, 2009)

Prediction and interpretation of distributed neural activity with sparse models. - Melissa K. Carroll; Guillermo A. Cecchi; Irina Rish; Rahul Garg 001; A. Ravishankar Rao (NeuroImage, 2009)

Sparse Regression Models of Pain Perception. - Irina Rish; Guillermo A. Cecchi; Marwan N. Baliki; Apkar Vania Apkarian (Brain Informatics, 2010)

Sparse Markov net learning with priors on regularization parameters. - Katya Scheinberg; Irina Rish; Narges Bani Asadi (ISAIM, 2010)

Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach. - Katya Scheinberg; Irina Rish (ECML/PKDD (3), 2010)

Sparse regression analysis of task-relevant information distribution in the brain. - Irina Rish; Guillermo A. Cecchi; Kyle Heuton; Marwan N. Baliki; Apkar Vania Apkarian (Image Processing, 2012)

Variable Selection for Gaussian Graphical Models. - Jean Honorio; Dimitris Samaras; Irina Rish; Guillermo A. Cecchi (AISTATS, 2012)

Predictive Dynamics of Human Pain Perception. - Guillermo A. Cecchi; Lejian Huang; Javeria Ali Hashmi; Marwan N. Baliki; María V. Centeno; Irina Rish; Apkar Vania Apkarian (PLoS Comput. Biol., 2012)

Functional MRI Analysis with Sparse Models. - Irina Rish (ECML/PKDD (3), 2013)

MINT: Mutual Information based Transductive Feature Selection for Genetic Trait Prediction. - Dan He 001; Irina Rish; David Haws; Simon Teyssedre; Zivan Karaman; Laxmi Parida (CoRR, 2013)

Transductive HSIC Lasso. - Dan He 001; Irina Rish; Laxmi Parida (SDM, 2014)

Evaluating effects of methylphenidate on brain activity in cocaine addiction: a machine-learning approach. - Irina Rish; Pouya Bashivan; Guillermo A. Cecchi; Rita Z. Goldstein (Biomedical Applications in Molecular, Structural, and Functional Imaging, 2016)

Mental State Recognition via Wearable EEG. - Pouya Bashivan; Irina Rish; Steve Heisig (CoRR, 2016)

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

MINT: Mutual Information Based Transductive Feature Selection for Genetic Trait Prediction. - Dan He 001; Irina Rish; David Haws; Laxmi Parida (IEEE ACM Trans. Comput. Biol. Bioinform., 2016)

Bandit Models of Human Behavior: Reward Processing in Mental Disorders. - Djallel Bouneffouf 001; Irina Rish; Guillermo A. Cecchi (AGI, 2017)

Context Attentive Bandits: Contextual Bandit with Restricted Context. - Djallel Bouneffouf 001; Irina Rish; Guillermo A. Cecchi; Raphaël Féraud (IJCAI, 2017)

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

Learning discriminative functional network features of schizophrenia. - Mina Gheiratmand; Irina Rish; Guillermo A. Cecchi; Matthew R. G. Brown; Russell Greiner; Pouya Bashivan; Pablo Polosecki; Serdar M. Dursun (Biomedical Applications in Molecular, Structural, and Functional Imaging, 2017)

Efficient Representation for Natural Language Processing via Kernelized Hashcodes. - Sahil Garg; Aram Galstyan; Irina Rish; Guillermo A. Cecchi; Shuyang Gao (CoRR, 2017)

Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks. - Jumana Dakka; Pouya Bashivan; Mina Gheiratmand; Irina Rish; Shantenu Jha; Russell Greiner (CoRR, 2017)

Computing the structure of language for neuropsychiatric evaluation. - Guillermo A. Cecchi; Viatcheslav Gurev; Steve Heisig; Raquel Norel; Irina Rish; Samantha R. Schrecke (IBM J. Res. Dev., 2017)

Computational psychiatry: Advancing predictive modeling of neurodegeneration with neuroimaging of Huntington's disease. - Pablo Polosecki; Eduardo Castro; Andrew Wood; John H. Warner; Irina Rish; Guillermo A. Cecchi (IBM J. Res. Dev., 2017)

Holographic brain: Distributed versus local activation patterns in fMRI. - Irina Rish; Guillermo A. Cecchi (IBM J. Res. Dev., 2017)

Contextual Bandit with Adaptive Feature Extraction. - Baihan Lin; Djallel Bouneffouf 001; Guillermo A. Cecchi; Irina Rish (ICDM Workshops, 2018)

Dialogue Modeling Via Hash Functions. - Sahil Garg; Guillermo A. Cecchi; Irina Rish; Shuyang Gao; Greg Ver Steeg; Sarik Ghazarian; Palash Goyal; Aram Galstyan (LaCATODA@IJCAI, 2018)

Adaptive Representation Selection in Contextual Bandit with Unlabeled History. - Baihan Lin; Guillermo A. Cecchi; Djallel Bouneffouf 001; Irina Rish (CoRR, 2018)

Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM. - Germán Abrevaya; Aleksandr Y. Aravkin; Guillermo A. Cecchi; Irina Rish; Pablo Polosecki; Peng Zheng 002; Silvina Ponce Dawson (CoRR, 2018)

Beyond Backprop: Alternating Minimization with co-Activation Memory. - Anna Choromanska; Sadhana Kumaravel; Ronny Luss; Irina Rish; Brian Kingsbury; Ravi Tejwani; Djallel Bouneffouf 001 (CoRR, 2018)

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

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference. - Matthew Riemer; Ignacio Cases; Robert Ajemian; Miao Liu 001; Irina Rish; Yuhai Tu; Gerald Tesauro (ICLR (Poster), 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 001 (ICML, 2019)

Predicting conversion to psychosis in clinical high risk patients using resting-state functional MRI features. - Jolie McDonnell; William Hord; Jenna M. Reinen; Pablo Polosecki; Irina Rish; Guillermo A. Cecchi (Biomedical Applications in Molecular, Structural, and Functional Imaging, 2019)

Continual Learning with Self-Organizing Maps. - Pouya Bashivan; Martin Schrimpf; Robert Ajemian; Irina Rish; Matthew Riemer; Yuhai Tu (CoRR, 2019)

A Survey on Practical Applications of Multi-Armed and Contextual Bandits. - Djallel Bouneffouf 001; Irina Rish (CoRR, 2019)

Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders. - Baihan Lin; Guillermo A. Cecchi; Djallel Bouneffouf 001; Jenna M. Reinen; Irina Rish (CoRR, 2019)

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 (AAAI, 2020)

A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry. - Baihan Lin; Guillermo A. Cecchi; Djallel Bouneffouf 001; Jenna M. Reinen; Irina Rish (AAMAS, 2020)

Survey on Applications of Multi-Armed and Contextual Bandits. - Djallel Bouneffouf 001; Irina Rish; Charu C. Aggarwal (CEC, 2020)

Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL. - Baihan Lin; Guillermo A. Cecchi; Djallel Bouneffouf 001; Jenna M. Reinen; Irina Rish (HBAI@IJCAI, 2020)

Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning. - Massimo Caccia; Pau Rodríguez; Oleksiy Ostapenko; Fabrice Normandin; Min Lin; Lucas Page-Caccia; Issam Hadj Laradji; Irina Rish; Alexandre Lacoste; David Vázquez 001; Laurent Charlin (NeurIPS, 2020)

Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning. - Massimo Caccia; Pau Rodríguez; Oleksiy Ostapenko; Fabrice Normandin; Min Lin; Lucas Caccia; Issam H. Laradji; Irina Rish; Alexandre Lacoste; David Vázquez 001; Laurent Charlin (CoRR, 2020)

Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation. - Victor Schmidt; Makesh Narsimhan Sreedhar; Mostafa ElAraby; Irina Rish (CoRR, 2020)

Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL. - Baihan Lin; Guillermo A. Cecchi; Djallel Bouneffouf 001; Jenna M. Reinen; Irina Rish (CoRR, 2020)

COVI White Paper. - Hannah Alsdurf; Yoshua Bengio; Tristan Deleu; Prateek Gupta; Daphne Ippolito; Richard Janda; Max Jarvie; Tyler Kolody; Sekoul Krastev; Tegan Maharaj; Robert Obryk; Dan Pilat; et al. (CoRR, 2020)

Adversarial Feature Desensitization. - Pouya Bashivan; Blake A. Richards; Irina Rish (CoRR, 2020)

COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing. - 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; et al. (CoRR, 2020)

Toward Skills Dialog Orchestration with Online Learning. - Djallel Bouneffouf 001; Raphaël Féraud; Sohini Upadhyay; Mayank Agarwal; Yasaman Khazaeni; Irina Rish (ICASSP, 2021)

Double-Linear Thompson Sampling for Context-Attentive Bandits. - Djallel Bouneffouf 001; Raphaël Féraud; Sohini Upadhyay; Yasaman Khazaeni; Irina Rish (ICASSP, 2021)

Predicting Infectiousness for Proactive Contact Tracing. - 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; et al. (ICLR, 2021)

Toward Optimal Solution for the Context-Attentive Bandit Problem. - Djallel Bouneffouf 001; Raphaël Féraud; Sohini Upadhyay; Irina Rish; Yasaman Khazaeni (IJCAI, 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 (NeurIPS, 2021)

Adversarial Feature Desensitization. - Pouya Bashivan; Reza Bayat; Adam Ibrahim; Kartik Ahuja; Mojtaba Faramarzi; Touraj Laleh; Blake A. Richards; Irina Rish (NeurIPS, 2021)

Understanding Continual Learning Settings with Data Distribution Drift Analysis. - Timothée Lesort; Massimo Caccia; Irina Rish (CoRR, 2021)

Towards Causal Federated Learning For Enhanced Robustness and Privacy. - Sreya Francis; Irene Tenison; Irina Rish (CoRR, 2021)

Gradient Masked Federated Optimization. - Irene Tenison; Sreya Francis; Irina Rish (CoRR, 2021)

Continual Learning in Deep Networks: an Analysis of the Last Layer. - Timothée Lesort; Thomas George; Irina Rish (CoRR, 2021)

SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization. - Soroosh Shahtalebi; Jean-Christophe Gagnon-Audet; Touraj Laleh; Mojtaba Faramarzi; Kartik Ahuja; Irina Rish (CoRR, 2021)

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. - Kartik Ahuja; Ethan Caballero; Dinghuai Zhang; Yoshua Bengio; Ioannis Mitliagkas; Irina Rish (CoRR, 2021)

Parametric Scattering Networks. - Shanel Gauthier; Benjamin Thérien; Laurent Alsène-Racicot; Irina Rish; Eugene Belilovsky; Michael Eickenberg; Guy Wolf (CoRR, 2021)

Sequoia: A Software Framework to Unify Continual Learning Research. - 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; et al. (CoRR, 2021)

Approximate Bayesian Optimisation for Neural Networks. - Nadhir Hassen; Irina Rish (CoRR, 2021)

Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers. - Gabriele Prato 001; Simon Guiroy; Ethan Caballero; Irina Rish; Sarath Chandar (CoRR, 2021)

Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks. - Germán Abrevaya; Guillaume Dumas; Aleksandr Y. Aravkin; Peng Zheng 002; Jean-Christophe Gagnon-Audet; James R. Kozloski; Pablo Polosecki; Guillaume Lajoie; David D. Cox; Silvina Ponce Dawson; Guillermo A. Cecchi; Irina Rish (Neural Comput., 2021)

Cognitive Models as Simulators: The Case of Moral Decision-Making. - Ardavan Salehi Nobandegani; Thomas R. Shultz; Irina Rish (CogSci, 2022)

Continual Learning with Foundation Models: An Empirical Study of Latent Replay. - Oleksiy Ostapenko; Timothée Lesort; Pau Rodríguez; Md Rifat Arefin; Arthur Douillard; Irina Rish; Laurent Charlin (CoLLAs, 2022)

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

A Remedy For Distributional Shifts Through Expected Domain Translation. - Jean-Christophe Gagnon-Audet; Soroosh Shahtalebi; Frank Rudzicz; Irina Rish (ICASSP, 2022)

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

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

Continual Learning In Environments With Polynomial Mixing Times. - Matthew Riemer; Sharath Chandra Raparthy; Ignacio Cases; Gopeshh Subbaraj; Maximilian Puelma Touzel; Irina Rish (NeurIPS, 2022)

WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks. - Jean-Christophe Gagnon-Audet; Kartik Ahuja; Mohammad-Javad Darvishi Bayazi; Guillaume Dumas; Irina Rish (CoRR, 2022)

APP: Anytime Progressive Pruning. - Diganta Misra; Bharat Runwal; Tianlong Chen 001; Zhangyang Wang; Irina Rish (CoRR, 2022)

Foundational Models for Continual Learning: An Empirical Study of Latent Replay. - Oleksiy Ostapenko; Timothée Lesort; Pau Rodríguez; Md Rifat Arefin; Arthur Douillard; Irina Rish; Laurent Charlin (CoRR, 2022)

Scaling the Number of Tasks in Continual Learning. - Timothée Lesort; Oleksiy Ostapenko; Diganta Misra; Md Rifat Arefin; Pau Rodríguez; Laurent Charlin; Irina Rish (CoRR, 2022)

Towards Out-of-Distribution Adversarial Robustness. - Adam Ibrahim; Charles Guille-Escuret; Ioannis Mitliagkas; Irina Rish; David Krueger 001; Pouya Bashivan (CoRR, 2022)

Aligning MAGMA by Few-Shot Learning and Finetuning. - Jean-Charles Layoun; Alexis Roger; Irina Rish (CoRR, 2022)

Generative Models of Brain Dynamics. - Mahta Ramezanian-Panahi; Germán Abrevaya; Jean-Christophe Gagnon-Audet; Vikram Voleti; Irina Rish; Guillaume Dumas (Frontiers Artif. Intell., 2022)

Towards Continual Reinforcement Learning: A Review and Perspectives. - Khimya Khetarpal; Matthew Riemer; Irina Rish; Doina Precup (J. Artif. Intell. Res., 2022)

AI Agents Learn to Trust. - Ardavan Salehi Nobandegani; Irina Rish; Thomas R. Shultz (CogSci, 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 (CoLLAs, 2023)

Dialogue System with Missing Observation. - Djallel Bouneffouf 001; Mayank Agarwal; Irina Rish (ICASSP, 2023)

Broken Neural Scaling Laws. - Ethan Caballero; Kshitij Gupta; Irina Rish; David Krueger 001 (ICLR, 2023)

Maximum State Entropy Exploration using Predecessor and Successor Representations. - Arnav Kumar Jain; Lucas Lehnert; Irina Rish; Glen Berseth (NeurIPS, 2023)

A Survey on Compositional Generalization in Applications. - Baihan Lin; Djallel Bouneffouf 001; Irina Rish (CoRR, 2023)

Towards ethical multimodal systems. - Alexis Roger; Esma Aïmeur; Irina Rish (CoRR, 2023)

Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok. - Pascal Tikeng Notsawo Jr.; Hattie Zhou; Mohammad Pezeshki; Irina Rish; Guillaume Dumas (CoRR, 2023)

GOKU-UI: Ubiquitous Inference through Attention and Multiple Shooting for Continuous-time Generative Models. - Germán Abrevaya; Mahta Ramezanian-Panahi; Jean-Christophe Gagnon-Audet; Irina Rish; Pablo Polosecki; Silvina Ponce Dawson; Guillermo A. Cecchi; Guillaume Dumas (CoRR, 2023)

Continual Pre-Training of Large Language Models: How to (re)warm your model? - Kshitij Gupta; Benjamin Thérien; Adam Ibrahim; Mats L. Richter; Quentin Anthony; Eugene Belilovsky; Irina Rish; Timothée Lesort (CoRR, 2023)

LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression. - Ayush Kaushal; Tejas Vaidhya; Irina Rish (CoRR, 2023)

Lag-Llama: Towards Foundation Models for Time Series Forecasting. - 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; et al. (CoRR, 2023)

Towards Machines that Trust: AI Agents Learn to Trust in the Trust Game. - Ardavan Salehi Nobandegani; Irina Rish; Thomas R. Shultz (CoRR, 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 (Trans. Mach. Learn. Res., 2023)

Gradient Masked Averaging for Federated Learning. - Irene Tenison; Sai Aravind Sreeramadas; Vaikkunth Mugunthan; Edouard Oyallon; Irina Rish; Eugene Belilovsky (Trans. Mach. Learn. Res., 2023)

Dance of the Neurons: Unraveling Sex from Brain Signals (short paper). - Mohammad-Javad Darvishi Bayazi; Mohammad Sajjad Ghaemi; Jocelyn Faubert; Irina Rish (ML4CMH@AAAI, 2024)

Improving Adversarial Robustness in Vision-Language Models with Architecture and Prompt Design. - Rishika Bhagwatkar; Shravan Nayak; Pouya Bashivan; Irina Rish (EMNLP (Findings), 2024)

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

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

Using Unity to Help Solve Reinforcement Learning. - Connor Brennan; Andrew Williams; Omar G. Younis; Vedant Vyas; Daria Yasafova; 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; et al. (NeurIPS, 2024)

Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI. - Sahil Garg; Anderson Schneider; Anant Raj; Kashif Rasul; Yuriy Nevmyvaka; Sneihil Gopal; Amit Dhurandhar; Guillermo A. Cecchi; Irina Rish (CoRR, 2024)

μLO: Compute-Efficient Meta-Generalization of Learned Optimizers. - Benjamin Thérien; Charles-Étienne Joseph; Boris Knyazev 001; Edouard Oyallon; Irina Rish; Eugene Belilovsky (CoRR, 2024)

Lost in Translation: The Algorithmic Gap Between LMs and the Brain. - Tommaso Tosato; Pascal Tikeng Notsawo Jr.; Saskia Helbling; Irina Rish; Guillaume Dumas (CoRR, 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 (CoRR, 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 A. Richards; Irina Rish; Özgür Simsek (CoRR, 2024)

Spectra: A Comprehensive Study of Ternary, Quantized, and FP16 Language Models. - Ayush Kaushal; Tejas Pandey; Tejas Vaidhya; Aaryan Bhagat; Irina Rish (CoRR, 2024)

VFA: Vision Frequency Analysis of Foundation Models and Human. - Mohammad-Javad Darvishi Bayazi; Md Rifat Arefin; Jocelyn Faubert; Irina Rish (CoRR, 2024)

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 (CoRR, 2024)

GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models. - Nizar Islah; Justine Gehring; Diganta Misra; Eilif B. Müller; Irina Rish; Terry Yue Zhuo; Massimo Caccia (CoRR, 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 A. Cecchi; Guillaume Dumas (Trans. Mach. Learn. Res., 2024)

Simple and Scalable Strategies to Continually Pre-train Large Language Models. - Adam Ibrahim; Benjamin Thérien; Kshitij Gupta; Mats L. Richter; Quentin Gregory Anthony; Eugene Belilovsky; Timothée Lesort; Irina Rish (Trans. Mach. Learn. Res., 2024)

Amplifying pathological detection in EEG signaling pathways through cross-dataset transfer learning. - Mohammad-Javad Darvishi Bayazi; Mohammad Sajjad Ghaemi; Timothée Lesort; Md Rifat Arefin; Jocelyn Faubert; Irina Rish (Comput. Biol. Medicine, 2024)

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning. - Andrei Mircea; Supriyo Chakraborty; Nima Chitsazan; Irina Rish; Ekaterina Lobacheva (ACL (1), 2025)

Scaling Laws and Efficient Inference for Ternary Language Models. - Tejas Vaidhya; Ayush Kaushal; Vineet Jain; Francis Couture Harpin; Prashant Shishodia; Majid Behbahani; Yuriy Nevmyvaka; Irina Rish (ACL (1), 2025)

Handling Delay in Real-Time Reinforcement Learning. - Ivan Anokhin; Rishav Rishav; Matthew Riemer; Stephen Chung; Irina Rish; Samira Ebrahimi Kahou (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)

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

Surprising Effectiveness of pretraining Ternary Language Model at Scale. - Ayush Kaushal; Tejas Vaidhya; Arnab Kumar Mondal; Tejas Pandey; Aaryan Bhagat; Irina Rish (ICLR, 2025)

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference. - Matthew Riemer; Gopeshh Subbaraj; Glen Berseth; Irina Rish (ICLR, 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 (Trans. Mach. Learn. Res., 2025)

Robin: a Suite of Multi-Scale Vision-Language Models and the CHIRP Evaluation Benchmark. - Alexis Roger; Prateek Humane; Daniel Z. Kaplan; Kshitij Gupta; Qi Sun; George Adamopoulos; Jonathan Siu Chi Lim; Quentin Anthony; Edwin Fennell; Irina Rish (CoRR, 2025)

Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training. - Vaibhav Singh; Paul Janson; Paria Mehrbod; Adam Ibrahim; Irina Rish; Eugene Belilovsky; Benjamin Thérien (CoRR, 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 001; Stephen Rawls; Sambit Sahu; Eugene Belilovsky; Irina Rish (CoRR, 2025)

MuLoCo: Muon is a practical inner optimizer for DiLoCo. - Benjamin Thérien; Xiaolong Huang; Irina Rish; Eugene Belilovsky (CoRR, 2025)

Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting. - Roland Riachi; Kashif Rasul; Arjun Ashok; Prateek Humane; Alexis Roger; Andrew Robert Williams; Yuriy Nevmyvaka; Irina Rish (CoRR, 2025)

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models. - Tejas Vaidhya; Ayush Kaushal; Vineet Jain; Francis Couture Harpin; Prashant Shishodia; Majid Behbahani; Yuriy Nevmyvaka; Irina Rish (CoRR, 2025)

GitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities. - Diganta Misra; Nizar Islah; Victor May; Brice Rauby; Zihan Wang; Justine Gehring; Antonio Orvieto; Muawiz Chaudhary; Eilif B. Muller; Irina Rish; Samira Ebrahimi Kahou; Massimo Caccia (CoRR, 2025)

Revisiting Replay and Gradient Alignment for Continual Pre-Training of Large Language Models. - Istabrak Abbes; Gopeshh Subbaraj; Matthew Riemer; Nizar Islah; Benjamin Thérien; Tsuguchika Tabaru; Hiroaki Kingetsu; Sarath Chandar; Irina Rish (CoRR, 2025)

Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation History. - Tommaso Tosato; Saskia Helbling; Yorguin José Mantilla Ramos; Mahmood Hegazy; Alberto Tosato; David John Lemay; Irina Rish; Guillaume Dumas (CoRR, 2025)

Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs. - Arjun Ashok; Andrew Robert Williams; Vincent Zhihao Zheng; Irina Rish; Nicolas Chapados; Étienne Marcotte; Valentina Zantedeschi; Alexandre Drouin (CoRR, 2025)

A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy. - Maxime Heuillet; Rishika Bhagwatkar; Jonas Ngnawé; Yann Pequignot; Alexandre Larouche; Christian Gagné 001; Irina Rish; Ola Ahmad; Audrey Durand (CoRR, 2025)