Achituve, Idan

12 publications

ICML 2025 Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo Idan Achituve, Hai Victor Habi, Amir Rosenfeld, Arnon Netzer, Idit Diamant, Ethan Fetaya

ICML 2024 Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning Idan Achituve, Idit Diamant, Arnon Netzer, Gal Chechik, Ethan Fetaya

ECCV 2024 De-Confusing Pseudo-Labels in Source-Free Domain Adaptation Idit Diamant, Amir Rosenfeld, Idan Achituve, Jacob Goldberger, Arnon Netzer

NeurIPSW 2023 Data Augmentations in Deep Weight Spaces Aviv Shamsian, David Zhang, Aviv Navon, Yan Zhang, Miltiadis Kofinas, Idan Achituve, Riccardo Valperga, Gertjan Burghouts, Efstratios Gavves, Cees Snoek, Ethan Fetaya, Gal Chechik, Haggai Maron

ICML 2023 Equivariant Architectures for Learning in Deep Weight Spaces Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron

UAI 2023 Guided Deep Kernel Learning Idan Achituve, Gal Chechik, Ethan Fetaya

NeurIPS 2022 Functional Ensemble Distillation Coby Penso, Idan Achituve, Ethan Fetaya

ICML 2022 Multi-Task Learning as a Bargaining Game Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya

ICLR 2021 Auxiliary Learning by Implicit Differentiation Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya

ICML 2021 GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya

NeurIPS 2021 Personalized Federated Learning with Gaussian Processes Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya

WACV 2021 Self-Supervised Learning for Domain Adaptation on Point Clouds Idan Achituve, Haggai Maron, Gal Chechik