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An estimator can be accurate and stable on typical data, yet sensitive to a few possible records. Differentially private mechanisms commonly scale their noise to the worst-case influence of a single r

Moshe Shenfeld, Eliad Tsfadia, Katrina Ligett

The Neural Thickets hypothesis posits that pretrained language models lie in dense neighborhoods of task-specific experts in weight space, which random Gaussian perturbations can uncover. We reproduce

Nicolas Dufour, Elouan Gardès, Amil Dravid

Pixel-space generative Transformers typically use one token per image patch, so capturing finer detail requires more tokens and makes global attention increasingly expensive. We observe that finer det

Jiongfan Yang, Weichen Fan, Haiwen Diao

Distributionally robust optimization (DRO) hedges data-driven two-stage stochastic programs against error in the distribution estimated from a finite record, and standard ambiguity sets differ in how

Aron Brenner, Saurabh Amin

Luggage re-identification (ReID) is a critical task in video surveillance for forensics, security or border management (suspicious or abandoned luggage recognition), as well as luggage management (los

Quentin Dhôte, Romaric Audigier

Extreme weather is forecast days ahead, yet its consequences for agriculture remain hard to predict: a forecast says where a storm will strike, not which crops will suffer or how much is at risk, and

Minghan Yu, Mengyu Dai, Yixin Wen

World models can achieve strong control performance without faithfully capturing the dynamics of their environment, limiting their potential for mechanistic interpretability and scientific discovery.

Tobias Schmidt, Rodrigo Gonzalez Laiz, Krisztián Szuppinger

An observer sees only part of the surrounding world, yet must keep track of what lies beyond the current view. Spatial perception therefore involves more than recognizing individual images: it calls f

Baiqiao Yin, Zhongang Cai, Ruisi Wang

The marginals of a continuous diffusion model can be reproduced by a deterministic ordinary differential equation (ODE), known as the probability flow ODE, which provides a foundation for accelerated

Samson Gourevitch, Yazid Janati, Eric Moulines

Modern diffusion models are trained on massive datasets that often include copyrighted or private images. Membership inference attacks (MIAs) address this by testing whether a given image was in a mod

Michael Joseph, Yedid Hoshen

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