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Showing papers from Ben-Gurion University of the Negev Show all papers

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NeurIPS 2026Ben-Gurion University of the NegTime series foundation models

Closing the Loop: Co-Evolving EM for Irregular Time Series Generation in Lifted Representations

Idan Arbiv, Gal Fadlon, Omri Azencot

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Shenzhen Institutes of Advanced The University of TokyoBen-Gurion University of the NegShenzhen Institutes of Advanced 3D vision & reconstruction

Visual-to-Executable Procedural Reconstruction of Buildings.

Zichong Zhu, Zhihao Liu, Andrei Sharf, Zhanglin Cheng

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Tel AvivBen-Gurion University of the NegLLM evaluation & benchmarks

When Empathy Misses the Goal: A Benchmark for Goal Displacement in LLM Advice

Dean Ariel, Guy Laban

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Ben Gurion University of the NegBen-Gurion University of the NegSparse autoencoders

Local–Global Sparse Autoencoders for Multiscale Interpretability in Vision Models

Itay Benou, Tammy Riklin Raviv

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Ben Gurion University of the negBen GurionAmazonBen-Gurion University of the NegTime series forecasting

Koopman Generative Operators for Efficient Probabilistic Time-Series Forecasting

Raz Marshanski, Liran Nochumsohn, Mayank Jauhari Iitr, Boris Oreshkin and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Ben-Gurion University of the NegBen Gurion University of the NegFairness & bias

Fairness in limited resource prediction-driven decisions

Inbal Livni Navon, Eitan Bachmat

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Ben Gurion University of the NegBen-Gurion University of the NegLearning theory

Transfer Learning of Linear Regression with Multiple Pretrained Models: Benefiting from More Pretrained Models via Overparameterization Debiasing

Overparameterized pretrained linear models risk transfer learning bias, but combining many with a multiplicative debiasing correction improves target predictions.

Daniel Boharon, Yehuda Dar

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 7 of 20 reviewers recommend it
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