Irina Rish

Publications

Context is Key: A Benchmark for Forecasting with Essential Textual Information. Andrew Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin. At International Conference on Machine Learning (ICML), 2025.

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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. At International Conference of Learning Representations (ICLR), 2025.

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Context is Key: A Benchmark for Forecasting with Essential Textual Information. Andrew Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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Context is Key: A Benchmark for Forecasting with Essential Textual Information. Andrew Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin. At Foundation Models for Time Series, 2024.

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Context is Key: A Benchmark for Forecasting with Essential Textual Information. Andrew Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin. At Montreal AI Symposium (MAIS), 2024.

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Lag-Llama: A Foundation Model for Probabilistic Time Series Forecasting. Kashif Rasul, Arjun Ashok, Marin Bilos, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Hena Ghonia, Nadhir Hassen, Anderson Schneider, Sahil Garg, Alexandre Drouin, Nicolas Chapados, Yuriy Nevmyvaka, Irina Rish. At Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

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Continual Learning with Foundation Models: An Empirical Study of Latent Replay. Oleksiy Ostapenko, Timothee Lesort, Pau Rodriguez, Arthur Douillard, Irina Rish, Laurent Charlin. At Conference on Lifelong Learning Agents (CoLLAs), 2022.

Continual Learning with Foundation Models: An Empirical Study of Latent Replay. Oleksiy Ostapenko, Timothee Lesort, Pau Rodriguez, Arthur Douillard, Irina Rish, Laurent Charlin. At Workshop at the Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

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. At International Conference on Learning Representations (ICLR), 2021.

Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning. Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam H. Laradji, Irina Rish, Alexandre Lacoste, David Vazquez, Laurent Charlin. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning. Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam H. Laradji, Irina Rish, Alexandre Lacoste, David Vazquez, Laurent Charlin. At Workshop at the Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

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