Gurwicz, Yaniv

10 publications

ICML 2025 A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment Raanan Yehezkel Rohekar, Yaniv Gurwicz, Sungduk Yu, Estelle Aflalo, Vasudev Lal

NeurIPS 2025 Causal Climate Emulation with Bayesian Filtering Sebastian Hickman, Ilija Trajković, Julia Kaltenborn, Francis Pelletier, Alexander T Archibald, Yaniv Gurwicz, Peer Nowack, David Rolnick, Julien Boussard

NeurIPSW 2024 Causal World Representation in the GPT Model Raanan Yehezkel Rohekar, Yaniv Gurwicz, Sungduk Yu, Vasudev Lal

NeurIPS 2023 Causal Interpretation of Self-Attention in Pre-Trained Transformers Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov

NeurIPS 2023 ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning Julia Kaltenborn, Charlotte Lange, Venkatesh Ramesh, Philippe Brouillard, Yaniv Gurwicz, Chandni Nagda, Jakob Runge, Peer Nowack, David Rolnick

ICML 2023 From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders Raanan Yehezkel Rohekar, Shami Nisimov, Yaniv Gurwicz, Gal Novik

NeurIPS 2021 Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Gal Novik

NeurIPS 2019 Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections Raanan Yehezkel Rohekar, Yaniv Gurwicz, Shami Nisimov, Gal Novik

NeurIPS 2018 Bayesian Structure Learning by Recursive Bootstrap Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov, Guy Koren, Gal Novik

NeurIPS 2018 Constructing Deep Neural Networks by Bayesian Network Structure Learning Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Guy Koren, Gal Novik