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Showing papers from University of Warsaw Show all papers

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arXivMetaU WarsawEfficient & distributed training

PyTorch Distributed: Experiences on Accelerating Data Parallel Training

PyTorch's distributed data parallel module uses gradient bucketing, communication-computation overlap, and synchronization skipping to achieve near-linear scalability on 256 GPUs.

Li Shen, Yanli Zhao, Rohan Varma, Omkar Salpekar and 7 more

Published Jun 28, 2020 · 111 citations · ▲ 13 on Hugging Face · Code ★ 103,810

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9/21 AI panelreviewers recommend it

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lenient 4/5
medium 3/11
strict 2/5
57%Worth a look
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NeurIPS 2026U WarsawHarvardU California, Berkeleynational university of singaore,NortheasternBackdoors & data poisoning

Weird Generalization from Narrow Finetuning: Persona Shifts and Inductive Backdoors

Jan Betley, Jorio Cocola, Dylan Feng, James Chua and 3 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 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 0/5
medium 0/10
strict 1/5
57%Worth a look
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NeurIPS 2026U WarsawUniwersytet Warszawski ul. KrakoCMU, Carnegie MellonMerck Healthcare KGaAHelmholtz Munich GmbHGenomics & single-cell

ImmuVis: Hyperconvolutional Foundation Models for Imaging Mass Cytometry

Dawid Uchal, Marcin Możejko, Krzysztof Gogolewski, Piotr Kupidura and 13 more

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
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
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NeurIPS 2026U GroningenU WarsawAGH University of Science and TeFinance & economics

On Nash Equilibria in Participatory Budgeting with Donations and Beyond

GRZEGORZ LISOWSKI, Georgios Papasotiropoulos, Grzegorz Pierczyński, Krzysztof Rogowski

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

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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NeurIPS 2026SpotlightETHZ - ETH ZurichU WarsawETH ZurichIdeas NCBR, Polish Academy of ScUC Berkeley & AmazonDeep RL

When Does Non-Uniform Replay Matter in Reinforcement Learning?

Michal Korniak, Mikołaj Czarnecki, Yarden As, Piotr Miłoś 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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lenient 0/5
medium 0/10
strict 0/5
91%Must read
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NeurIPS 2026SamsungU TurinU WarsawAI oversight & deception

Reading the Finetuning Prior: Verbatim Content Recovery via Contrastive Decoding Diffing

Contrastive Decoding Diffing recovers verbatim implanted facts and pipeline artifacts via output-level logit differences without weight access, outperforming white-box methods 170x faster.

Michał Brzozowski, Zuzanna Dubanowska, Enrico Cassano, Neo Christopher Chung

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

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18/20 AI panelreviewers recommend it

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AI panel: 18 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 4/5
74%Highly rated
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NeurIPS 2026U WarsawU Warsaw, NoMagic.AIUC Berkeley & AmazonDeep RL

Reward-Conditioned Reinforcement Learning

RCRL conditions agents on reward parameterizations via replay counterfactual rewards, improving sample efficiency and enabling zero-shot behavioral adaptation without extra interaction.

Michal Nauman, Marek Cygan, Pieter Abbeel

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 0/5
80%Must read
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NeurIPS 2026IDEAS NCBR Sp. z o. o. Address: U WarsawWarsaw University of TechnologyCenter on Long-Term RiskU OxfordBackdoors & data poisoning

Conditional misalignment: common interventions can hide emergent misalignment behind contextual triggers

Common interventions suppress emergent misalignment only under standard evaluations, yet hidden contextual triggers still elicit worse misalignment resembling training conditions.

Jan Dubiński, Jan Betley, Anna Sztyber-Betley, Daniel Tan 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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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5