Good Papers

Showing papers from Tulane University Show all papers

45%Niche pick
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NeurIPS 2026TulaneIBMAI policy & governance

DeepVoting: Learning and Improving Voting Rules with Fine-Tuning

Leonardo Matone, Ben Abramowitz, Ben Armstrong, Avinash Balakrishnan and 1 more

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

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medium 0/10
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57%Worth a look
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NeurIPS 2026TulaneVision-language models

MindVLM: Neural-Grounded Visual Captioning via Subject-Aware Semantic Evidence Selection

Zixiang Yin, Yu-Ping Wang, Zhengming Ding

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

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
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NeurIPS 2026TulaneDeep RL

Behavior-Discriminative Reward Shaping for Reward-Robust Reinforcement Learning

Zixuan Liu, Fangzheng Wu, Brian Summa, Zizhan Zheng

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

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NeurIPS 2026Rajshahi University of EngineeriTulaneU OklahomaShahjalal University of Science Summarization

SEISMOS: A Statistical Signal Detection Framework for Semantic Chunking

Ishtiak M Saad, Mominul Islam, Shahriyar Z Ridoy, Md Manjurul Ahsan and 1 more

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

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
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NeurIPS 2026TulaneDiffusion models

Where to Look Is Not How to Fix: Pre-Denoising Diagnostics and Modality-Dependent Control in Diffusion Composition

Fangzheng Wu, Brian Summa

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

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strict 0/5
78%Highly rated
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NeurIPS 2026TulaneBackdoors & data poisoning

Stealthy World Model Manipulation via Data Poisoning

SWAAP uses bilevel optimization and gradient matching to poison world model fine-tuning data, degrading planning while evading defenses.

Yibin Hu, Zizhan Zheng

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · 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 5/5
medium 6/10
strict 0/5
86%Must read
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NeurIPS 2026TulaneNanyang TechnologicalU MelbourneImage recognition

Adapting in the Dark: Efficient and Stable Test-Time Adaptation for Black-Box Models

BETA uses a local steering model and prediction harmonization to stabilize black-box test-time adaptation with zero extra API queries and large accuracy gains.

Yunbei Zhang, Shuaicheng Niu, Chengyi Cai, Feng Liu and 1 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · 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 8/10
strict 1/5
80%Must read
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NeurIPS 2026Nanyang TechnologicalTulaneContinual learning

EVA-0: Test-Time Model Evolution with Only Two Forward Passes per Sample

EVA-0 achieves test-time adaptation with only two forward passes per sample by using scale-invariant loss, anchor-guided optimization, and symmetric perturbation to avoid backpropagation. It outperforms BP-based DeYO and BP-free FOA on ImageNet-C with ViT-Base while running 14x faster than FOA.

Guohao Chen, Shuaicheng Niu, Geng Li, Yunbei Zhang and 3 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 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 7/10
strict 0/5