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Showing papers from Saarland University Show all papers

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Learning Pareto Stationary Fronts via Single-Pass Backpropagation

Elina Rojin Celik, Marcos M. Raimundo, Isabel Valera

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

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A Faster Algorithm for the Half-Trek Criterion in Structural Causal Models

Yasmine Briefs, Markus Bläser

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

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Should We Pay This Much for Robustness? Efficient Proxy Certificates with Marginal Guarantees

Sayed Soroush Haj Zargarbashi, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski

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

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What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rules

Tim Nico Bauerschmidt, Isabel Valera, Jilles Vreeken

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

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Understanding the Curse of Unrolling

Non-asymptotic analysis explains the curse of unrolling, early derivative divergence when differentiating through iterative algorithms, and shows that truncating early iterations mitigates it while reducing memory, with warm-starting providing implicit truncation in bilevel optimization.

Sheheryar Mehmood, Florian Knoll, Peter Ochs

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 4/5
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Semantic Motion Anchors: Bridging Motion and Meaning in Co-Speech Gestures

Semantic motion anchors discretize gesture motion into verbalized primitives to align text and gestures, improving retrieval and generation by capturing communicative intent over low-level kinematics.

Varsha Suresh, Mohammad Mahdi Abootorabi, Mohamed Salman, M. Hamza Mughal and 4 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 8/10
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72%Highly rated
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Prune to Protect: Faster Training and Enhanced Privacy by Dynamic Data Pruning

WLIB dynamically prunes easy samples and reweights hard ones to reduce memorization, improve privacy, and speed up training.

Chinmay Joshi, Advait Gadhikar, Celia Rubio-Madrigal, Aneet Kumar Dutta and 2 more

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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AI panel: 8 of 20 reviewers recommend it
lenient 5/5
medium 3/10
strict 0/5