Rapid Spline-based Kernel Density Estimation for Bayesian Networks. - Yaniv Gurwicz; Boaz Lerner (ICPR (3), 2004)

Bayesian network classification using spline-approximated kernel density estimation. - Yaniv Gurwicz; Boaz Lerner (Pattern Recognit. Lett., 2005)

Bayesian Class-Matched Multinet Classifier. - Yaniv Gurwicz; Boaz Lerner (SSPR/SPR, 2006)

Multiclass object classification for real-time video surveillance systems. - Yaniv Gurwicz; Raanan Yehezkel; Boaz Lachover (Pattern Recognit. Lett., 2011)

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 (NeurIPS, 2018)

Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections. - Raanan Y. Rohekar; Yaniv Gurwicz; Shami Nisimov; Gal Novik (NeurIPS, 2019)

A Single Iterative Step for Anytime Causal Discovery. - Raanan Y. Rohekar; Yaniv Gurwicz; Shami Nisimov; Gal Novik (CoRR, 2020)

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

Improving Efficiency and Accuracy of Causal Discovery Using a Hierarchical Wrapper. - Shami Nisimov; Yaniv Gurwicz; Raanan Y. Rohekar; Gal Novik (CoRR, 2021)

CLEAR: Causal Explanations from Attention in Neural Recommenders. - Shami Nisimov; Raanan Y. Rohekar; Yaniv Gurwicz; Guy Koren; Gal Novik (CoRR, 2022)

From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders. - Raanan Y. Rohekar; Shami Nisimov; Yaniv Gurwicz; Gal Novik (ICML, 2023)

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

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

Towards Causal Representations of Climate Model Data. - Julien Boussard; Chandni Nagda; Julia Kaltenborn; Charlotte Emilie Elektra Lange; Philippe Brouillard; Yaniv Gurwicz; Peer Nowack; David Rolnick (CoRR, 2023)

LVLM-Intrepret: An Interpretability Tool for Large Vision-Language Models. - Gabriela Ben Melech Stan; Estelle Aflalo; Raanan Yehezkel Rohekar; Anahita Bhiwandiwalla; Shao-Yen Tseng; Matthew Lyle Olson; Yaniv Gurwicz; Chenfei Wu; Nan Duan; Vasudev Lal (XAI4CV, 2024)

Causal Representation Learning in Temporal Data via Single-Parent Decoding. - Philippe Brouillard; Sébastien Lachapelle; Julia Kaltenborn; Yaniv Gurwicz; Dhanya Sridhar; Alexandre Drouin; Peer Nowack; Jakob Runge; David Rolnick (CoRR, 2024)

Causal World Representation in the GPT Model. - Raanan Y. Rohekar; Yaniv Gurwicz; Sungduk Yu; Vasudev Lal (CoRR, 2024)

Causal Climate Emulation with Bayesian Filtering. - Sebastian Hickman; Ilija Trajkovic; Julia Kaltenborn; Francis Pelletier; Alex Archibald; Yaniv Gurwicz; Peer Nowack; David Rolnick; Julien Boussard (CoRR, 2025)