Azencot, Omri

23 publications

ICLR 2026 DiffSDA: Unsupervised Diffusion Sequential Disentanglement Across Modalities Hedi Zisling, Ilan Naiman, Nimrod Berman, Supasorn Suwajanakorn, Omri Azencot

TMLR 2026 Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting Liran Nochumsohn, Raz Marshanski, Hedi Zisling, Omri Azencot

TMLR 2026 XCTFormer: Leveraging Cross-Channel and Cross-Time Dependencies for Enhanced Time-Series Analysis Israel Zexer, Omri Azencot

NeurIPS 2025 A Diffusion Model for Regular Time Series Generation from Irregular Data with Completion and Masking Gal Fadlon, Idan Arbiv, Nimrod Berman, Omri Azencot

AISTATS 2025 A Multi-Task Learning Approach to Linear Multivariate Forecasting Liran Nochumsohn, Hedi Zisling, Omri Azencot

ICML 2025 Curvature Enhanced Data Augmentation for Regression Ilya Kaufman, Omri Azencot

TMLR 2025 Data Augmentation Policy Search for Long-Term Forecasting Liran Nochumsohn, Omri Azencot

NeurIPS 2025 Disentanglement Beyond Static vs. Dynamic: A Benchmark and Evaluation Framework for Multi-Factor Sequential Representations Tal Barami, Nimrod Berman, Ilan Naiman, Amos Haviv Hason, Rotem Ezra, Omri Azencot

NeurIPS 2025 One-Step Offline Distillation of Diffusion-Based Models via Koopman Modeling Nimrod Berman, Ilan Naiman, Moshe Eliasof, Hedi Zisling, Omri Azencot

TMLR 2025 Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes Nimrod Berman, Eitan Kosman, Dotan Di Castro, Omri Azencot

NeurIPS 2025 Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach Tal Gonen, Itai Pemper, Ilan Naiman, Nimrod Berman, Omri Azencot

NeurIPS 2025 Towards General Modality Translation with Contrastive and Predictive Latent Diffusion Bridge Nimrod Berman, Omkar Joglekar, Eitan Kosman, Dotan Di Castro, Omri Azencot

TMLR 2024 Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning Ilya Kaufman, Omri Azencot

ICML 2024 First-Order Manifold Data Augmentation for Regression Learning Ilya Kaufman, Omri Azencot

ICLR 2024 Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs Ilan Naiman, N. Benjamin Erichson, Pu Ren, Michael W. Mahoney, Omri Azencot

ICML 2024 Sequential Disentanglement by Extracting Static Information from a Single Sequence Element Nimrod Berman, Ilan Naiman, Idan Arbiv, Gal Fadlon, Omri Azencot

NeurIPS 2024 Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series Ilan Naiman, Nimrod Berman, Itai Pemper, Idan Arbiv, Gal Fadlon, Omri Azencot

AAAI 2023 An Operator Theoretic Approach for Analyzing Sequence Neural Networks Ilan Naiman, Omri Azencot

ICML 2023 Data Representations’ Study of Latent Image Manifolds Ilya Kaufman, Omri Azencot

ICLR 2023 Multifactor Sequential Disentanglement via Structured Koopman Autoencoders Nimrod Berman, Ilan Naiman, Omri Azencot

ICML 2023 Sample and Predict Your Latent: Modality-Free Sequential Disentanglement via Contrastive Estimation Ilan Naiman, Nimrod Berman, Omri Azencot

ICLR 2021 Lipschitz Recurrent Neural Networks N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, Michael W. Mahoney

ICML 2020 Forecasting Sequential Data Using Consistent Koopman Autoencoders Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael Mahoney