Good Papers

Showing papers from IST Austria Show all papers

45%Niche pick
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Algorithms for Linear Equations with Min and Max Operators Under (Absolutely) Halting Condition

Krishnendu Chatterjee, Ruichen Luo, Raimundo Saona, Jakub Svoboda

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

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
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Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach

Yiwen Qiu, Francesco Montagna, Shimeng Huang, Francesco Locatello

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

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1/20 AI panelreviewers recommend it

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
71%Highly rated
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Sink vs. diagonal patterns as mechanisms for attention switch and oversmoothing prevention

Sinks and diagonal patterns serve as attention switches and anti-oversmoothing mechanisms, with sinks favored in pretrained transformers due to lower representation costs.

Peter Súkeník, Cristina Lopez Amado, Christoph Lampert, Marco Mondelli

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

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6/20 AI panelreviewers recommend it

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AI panel: 6 of 20 reviewers recommend it
lenient 1/5
medium 3/10
strict 2/5
78%Highly rated
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Grid Games: The Power Of Multiple Grids for Quantizing Large Language Models

Multiple grids per group improve 4-bit quantization by selecting better grids per group, consistently boosting accuracy over single-grid FP4 for weights and activations.

Vage Egiazarian, Erik Schultheis, Andrei Panferov, Earl Killian and 2 more

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

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 0/5
76%Highly rated
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Optimal Representation Size: High-Dimensional Analysis of Pretraining and Linear Probing

High-dimensional analysis of pretraining via PCA and linear probing derives exact errors versus representation size, showing compression helps with abundant unlabeled but scarce labeled data.

Valentina Njaradi, Clémentine Dominé, Rachel A Swanson, Marco Mondelli 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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10/20 AI panelreviewers recommend it

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 4/10
strict 2/5
70%Highly rated
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Multi-Environment POMDPs with Finite-Horizon Objectives

Finite-horizon multi-environment POMDP optimization is PSPACE-complete, and a new practical algorithm significantly outperforms prior methods on benchmarks.

Léonard Brice, Filip Cano, Krishnendu Chatterjee, Thomas Henzinger and 1 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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5/20 AI panelreviewers recommend it

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AI panel: 5 of 20 reviewers recommend it
lenient 1/5
medium 3/10
strict 1/5