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Showing papers from Los Alamos National Laboratory Show all papers

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NeurIPS 2026Los Alamos National LaboratoryPhysics-informed ML & PDEs

Foundation Models for Particle Accelerators

Mahindra Rautela, Alexander Scheinker

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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45%Niche pick
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NeurIPS 2026Los Alamos National LaboratoryMax Planck Institute, Max-PlanckVision backbones

On Making $SE(2)$-Invariant Networks Optimal

Tomas Karella, Emily Shinkle, Alice Allen, Pieter J Swart and 2 more

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

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NeurIPS 2026Georgia Institute of TechnologyPSRLos Alamos National LaboratoryAdversarial robustness

Masked Sobolev Training for Feasibility-Reliable Optimization Proxies

Andrew ROSEMBERG, Joaquim Dias Garcia, Russell Bent, Pascal Van Hentenryck

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

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NeurIPS 2026U California, BerkeleyHarvard University, HarvardLos Alamos National LaboratoryU California BerkeleyBayesian & probabilistic methods

Fast Accurate Quantum Monte Carlo without Metropolis Adjustment

Reuben Cohn-Gordon, Gabriel Pescia, Sumner N Hearth, Jakob Robnik and 2 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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86%Must read
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NeurIPS 2026Michigan StateLos Alamos National LaboratoryImage restoration

A Principled Self-Referenced Early Stopping Approach for Deep Image Prior

Proposed pseudo self-referenced early stopping for Deep Image Prior uses constructed image pairs to detect overfitting, outperforming existing methods across inverse imaging problems without requiring noise level estimates.

Chaoyan Huang, Cheng-Han Huang, Ismail Alkhouri, Rongrong Wang

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 1/5
70%Highly rated
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NeurIPS 2026Los Alamos National LabLos Alamos National LaboratoryLearning theory

Computationally sufficient statistics for Ising models

Ising model parameters with ℓ1 width γ are learnable from O(γ)-order statistics efficiently, with faster recovery if structure is known.

Abhijith Jayakumar, Shreya Shukla, Marc Vuffray, Andrey Lokhov and 1 more

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

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AI panel: 5 of 20 reviewers recommend it
lenient 2/5
medium 2/10
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72%Highly rated
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NeurIPS 2026Los Alamos National LaboratoryBayesian & probabilistic methods

Wasserstein Gradient Flows and Forward-Only Diffusion Are Not Enough for Multimodal Sampling

Wasserstein gradient flows and forward-only diffusion share metastable dynamics causing exponentially slow mixing across well-separated modes, revealing a structural limit of local sampling.

Daniel McBride, Pratik Khandagale, Cristina Garcia-Cardona, Yen Ting Lin

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

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AI panel: 8 of 20 reviewers recommend it
lenient 2/5
medium 4/10
strict 2/5
76%Highly rated
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NeurIPS 2026Lawrence Berkeley National LaborLos Alamos National LaboratoryLawrence Berkeley National LabOptimization

Fast and Stable Gradient Approximation for Bilinear Forms of Hermitian Matrix Functions

A forward-only gradient approximation for bilinear forms of Hermitian matrix functions reuses the Lanczos pass with minimal overhead, offering provable error bounds and unconditional stability without reorthogonalization.

Navjot Singh, Kipton Barros, Sherry Li

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

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AI panel: 10 of 20 reviewers recommend it
lenient 3/5
medium 5/10
strict 2/5