Good Papers

Showing papers from Heriot-Watt University Show all papers

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How deep is your network? Deep vs. shallow learning of transfer operators

Mohammad Tabish, Benedict Leimkuhler, Stefan Klus

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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medium 0/10
strict 0/5
72%Highly rated
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Mirror Descent-Ascent for mean-field min-max problems

Mirror descent-ascent achieves O(N^{-1/2}) and O(N^{-2/3}) convergence to mean-field Nash equilibria via infinite-dimensional dual Bregman analysis.

Razvan-Andrei Lascu, Mateusz Majka, Lukasz Szpruch

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

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8/20 AI panelreviewers recommend it

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AI panel: 8 of 20 reviewers recommend it
lenient 2/5
medium 4/10
strict 2/5
86%Must read
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SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning

SLOPE constructs optimistic potential landscapes via distributional regression to amplify sparse success signals and guide planning, outperforming baselines across sparse reward benchmarks.

Yao-Hui Li, Zeyu Wang, Xin Li, Wei Pang and 6 more

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

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14/20 AI panelreviewers recommend it

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AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 2/5
72%Highly rated
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Consistency Regularised Gradient Flows for Inverse Problems

A Euclidean-Wasserstein-2 gradient-flow framework unifies posterior sampling and prompt optimization for low-NFE inverse problem solving using vision-language diffusion priors without backpropagating through autoencoders.

Alessio Spagnoletti, Tim Wang, O. Deniz Akyildiz, Marcelo Pereyra

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 3/5
medium 5/10
strict 0/5
91%Must read
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Reflective Prompted Policy Optimization: Trajectory-Grounded Revision and Salience Bias

R2PO uses trajectory-level behavioral evidence rather than scalar rewards to guide LLM policy search, achieving faster and more stable optimization across ten environments despite a critic salience bias.

Rahaf Abu Hara, Vaibbhav Murarri, Claudio Zito

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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17/20 AI panelreviewers recommend it

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AI panel: 17 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 3/5