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Showing papers from University of Michigan, Ann Arbor Show all papers

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NeurIPS 2026U Michigan - Ann ArborU Michigan, Ann ArborResearch, GoogleU Southern CaliforniaU MichiganTabular data

Evolutionary Feature Engineering for Structured Data

Evolutionary Feature Engineering uses LLM-based evolution to discover structured-data preprocessing transformations, reducing forecasting errors up to 19% and improving tabular predictions.

Ege Onur Taga, Yilin Zhuang, Muhammed Emrullah Ildiz, Petros Mol and 3 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · 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 5/5
medium 4/10
strict 1/5
86%Must read
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NeurIPS 2026SpotlightEPFLU Michigan, Ann ArborUSITU Wien, AITHYRA, University of KAISTLLM reasoning & chain of thought

MUX: Continuous Reasoning via Multiplexed Tokens

MUX compresses reasoning into continuous multiplexed tokens via lossless superposition, accelerating reasoning and outperforming latent baselines across 32 settings.

Ayhan Suleymanzade, Halil Alperen Gozeten, Michael Bronstein, Ismail Ilkan Ceylan and 1 more

Sydney Poster Session 2, Tue, Dec 8, 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 4/5
medium 8/10
strict 2/5
91%Must read
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NeurIPS 2026U Michigan - Ann ArborU Michigan, Ann ArborMidcontinent ISOU MichiganLLM evaluation & benchmarks

Information Discernment in Large Language Models

LLMs fail at source and truth discernment, relying on popularity over reliability and updating equally for accurate and inaccurate claims despite simple inference-time fixes existing.

Joshua Ashkinaze, Laura Kurek, Alina Faisal, Tongyuan Miao and 3 more

Sydney Poster Session 2, Tue, Dec 8, 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 5/5
medium 8/10
strict 4/5
78%Highly rated
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NeurIPS 2026Department of Computer Science, New YorkNanyang TechnologicalMetaNVIDIAControllable generation

Exploring MLLM-Diffusion Information Transfer with MetaCanvas

MetaCanvas enables multimodal LLMs to plan directly in diffusion latent spaces, outperforming global-conditioning baselines across six precise visual generation tasks.

Han Lin, Xichen Pan, Ziqi Huang, Ji Hou and 9 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 15 on Hugging Face

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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
71%Highly rated
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NeurIPS 2026U Michigan, Ann ArborWashington University in St. LouWashington University, St. LouisU MichiganActive learning

Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery

A geospatial discovery framework combines active learning, online meta-learning, and concept relevance to robustly find hidden targets like PFAS under sparse, changing conditions with limited sampling budgets.

Jowaria Khan, Anindya Sarkar, Yevgeniy Vorobeychik, Elizabeth Bondi-Kelly

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

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7/20 AI panelreviewers recommend it

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AI panel: 7 of 20 reviewers recommend it
lenient 5/5
medium 2/10
strict 0/5