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Showing papers from Alan Turing Institute Show all papers

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Adaptive Random Forests from Online Learning and Testing by Betting

Salim I. Amoukou, Saumitra Mishra, Manuela Veloso

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

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45%Niche pick
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Outlier-Robust Multi-Output Gaussian Processes

Joshua Rooijakkers, Leiv Rønneberg, Francois-Xavier Briol, Jeremias Knoblauch and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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57%Worth a look
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The Adversarial Gait: Detecting Visual Adversarial Attacks against Vision-Language Models via Self-Targeted Gradient Characterization

Mauricio Byrd Victorica, Ezzeldin Shereen, György Dán

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
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medium 0/10
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89%Must read
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Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples

Poisoning LLM pretraining requires only ~250 malicious documents regardless of dataset or model scale, revealing constant-cost backdoor injection risks for large models.

Alexandra Souly, Javier Rando, Ed Chapman, Xander Davies and 9 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 5 on Hugging Face

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 4/5
74%Highly rated
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Conservative neural posterior estimation via distributionally robust training

DRO-NPE trains neural posterior estimators with distributionally robust worst-case losses to reduce overconfidence and improve calibration under limited simulation budgets.

William Laplante, Yuga Hikida, Charita Dellaporta, Francois-Xavier Briol and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · 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 3/5
medium 6/10
strict 0/5
76%Highly rated
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Affine Tracing: A New Paradigm for Probabilistic Linear Solvers

Affine tracing unifies probabilistic linear solvers by showing Bayesian methods are non-stationary affine iterative methods that are calibrated, and automatically generates probabilistic multigrid solvers via symbolic computation graphs.

Disha Hegde, Marvin Pförtner, Jon Cockayne

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

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AI panel: 10 of 20 reviewers recommend it
lenient 2/5
medium 6/10
strict 2/5