Tootfinder

Locally Optimal Private Sampling: Beyond the Global Minimax
Hrad Ghoukasian, Bonwoo Lee, Shahab Asoodeh
https://arxiv.org/abs/2510.09485 https://arxiv.org/…

Locally Optimal Private Sampling: Beyond the Global Minimax
We study the problem of sampling from a distribution under local differential privacy (LDP). Given a private distribution $P \in \mathcal{P}$, the goal is to generate a single sample from a distribution that remains close to $P$ in $f$-divergence while satisfying the constraints of LDP. This task captures the fundamental challenge of producing realistic-looking data under strong privacy guarantees. While prior work by Park et al. (NeurIPS'24) focuses on global minimax-optimality across a class …

Smooth Uncertainty Sets: Dependence of Uncertain Parameters via a Simple Polyhedral Set
Noam Goldberg, Michael Poss, Shimrit Shtern
https://arxiv.org/abs/2510.08843 https://

Smooth Uncertainty Sets: Dependence of Uncertain Parameters via a Simple Polyhedral Set
We propose a novel polyhedral uncertainty set for robust optimization, termed the smooth uncertainty set, which captures dependencies of uncertain parameters by constraining their pairwise differences. The bounds on these differences may be dictated by the underlying physics of the problem and may be expressed by domain experts. When correlations are available, the bounds can be set to ensure that the associated probabilistic constraints are satisfied for any given probability. We explore spe…

Retentive Relevance: Capturing Long-Term User Value in Recommendation Systems
Saeideh Bakhshi, Phuong Mai Nguyen, Robert Schiller, Tiantian Xu, Pawan Kodandapani, Andrew Levine, Cayman Simpson, Qifan Wang
https://arxiv.org/abs/2510.07621

Retentive Relevance: Capturing Long-Term User Value in Recommendation Systems
Recommendation systems have traditionally relied on short-term engagement signals, such as clicks and likes, to personalize content. However, these signals are often noisy, sparse, and insufficient for capturing long-term user satisfaction and retention. We introduce Retentive Relevance, a novel content-level survey-based feedback measure that directly assesses users' intent to return to the platform for similar content. Unlike other survey measures that focus on immediate satisfaction, Retenti…

Learning Continuous Receive Apodization Weights via Implicit Neural Representation for Ultrafast ICE Ultrasound Imaging
R\'emi Delaunay, Christoph Hennersperger, Stefan W\"orz
https://arxiv.org/abs/2510.05694

Learning Continuous Receive Apodization Weights via Implicit Neural Representation for Ultrafast ICE Ultrasound Imaging
Ultrafast intracardiac echocardiography (ICE) uses unfocused transmissions to capture cardiac motion at frame rates exceeding 1 kHz. While this enables real-time visualization of rapid dynamics, image quality is often degraded by diffraction artifacts, requiring many transmits to achieve satisfying resolution and contrast. To address this limitation, we propose an implicit neural representation (INR) framework to encode complex-valued receive apodization weights in a continuous manner, enabling…

A Complement to Neural Networks for Anisotropic Inelasticity at Finite Strains
Hagen Holthusen, Ellen Kuhl
https://arxiv.org/abs/2510.04187 https://arxiv.o…

A Complement to Neural Networks for Anisotropic Inelasticity at Finite Strains
We propose a complement to constitutive modeling that augments neural networks with material principles to capture anisotropy and inelasticity at finite strains. The key element is a dual potential that governs dissipation, consistently incorporates anisotropy, and-unlike conventional convex formulations-satisfies the dissipation inequality without requiring convexity. Our neural network architecture employs invariant-based input representations in terms of mixed elastic, inelastic and struct…

Sample complexity for entropic optimal transport with radial cost
Ruiyu Han. Johannes Wiesel
https://arxiv.org/abs/2510.05685 https://arxiv.org/pdf/2510.05…

Sample complexity for entropic optimal transport with radial cost
We prove a new sample complexity result for entropy regularized optimal transport. Our bound holds for probability measures on $\mathbb R^d$ with exponential tail decay and for radial cost functions that satisfy a local Lipschitz condition. It is sharp up to logarithmic factors, and captures the intrinsic dimension of the marginal distributions through a generalized covering number of their supports. Examples that fit into our framework include subexponential and subgaussian distributions and r…

ISMIE: A Framework to Characterize Information Seeking in Modern Information Environments
Shuoqi Sun, Danula Hettiachchi, Damiano Spina
https://arxiv.org/abs/2510.07644 https://…

ISMIE: A Framework to Characterize Information Seeking in Modern Information Environments
The modern information environment (MIE) is increasingly complex, shaped by a wide range of techniques designed to satisfy users' information needs. Information seeking (IS) models are effective mechanisms for characterizing user-system interactions. However, conceptualizing a model that fully captures the MIE landscape poses a challenge. We argue: Does such a model exist? To address this, we propose the Information Seeking in Modern Information Environments (ISMIE) framework as a fundamental s…

Modularity and random graphs
Colin McDiarmid, Fiona Skerman
https://arxiv.org/abs/2509.22066 https://arxiv.org/pdf/2509.22066

Modularity and random graphs
This work will appear as a chapter in a forthcoming volume titled `Topics in Probabilistic Graph Theory'. For a given graph $G$, each partition of the vertices has a modularity score, with higher values indicating that the partition better captures community structure in $G$. The modularity $q^*(G)$ of $G$ is the maximum over all vertex-partitions of the modularity score, and satisfies $0\leq q^*(G)< 1$. Modularity lies at the heart of the most popular algorithms for community detection. In t…

Day 24: Yvonne Adhiambra Owuor
Owuor wrote "Dust", a novel that follows a scattered family's struggles with intergenerational trauma through a vivid tapestry of Kenyan history. Not only is it full of carefully rendered complex characters who both deal with their own issues and who are entangled in larger threads, but it also depicts a series of deeply personal reactions to and interactions with historical moments that give a gestalt sense of the painful history of Kenya both during and after the colonial era.
It's a gripping read despite not having a traditional suspense structure, where in the last third of the book every chapter seems to be tying up one more loose thread you had almost forgotten about, only to leave a little more still to discover, right up to the end. Owuor's skill at constructing such a detailed and complex plot and especially in navigating it to a satisfying conclusion is impressive, and her depictions of human foibles and struggles in the face of grief and not-wanting-to-know are relatable.
CW for domestic abuse, state murder, genocide, torture, etc.
#30AuthorsNoMen

Interactive Recommendation Agent with Active User Commands
Jiakai Tang, Yujie Luo, Xunke Xi, Fei Sun, Xueyang Feng, Sunhao Dai, Chao Yi, Dian Chen, Zhujin Gao, Yang Li, Xu Chen, Wen Chen, Jian Wu, Yuning Jiang, Bo Zheng
https://arxiv.org/abs/2509.21317

Interactive Recommendation Agent with Active User Commands
Traditional recommender systems rely on passive feedback mechanisms that limit users to simple choices such as like and dislike. However, these coarse-grained signals fail to capture users' nuanced behavior motivations and intentions. In turn, current systems cannot also distinguish which specific item attributes drive user satisfaction or dissatisfaction, resulting in inaccurate preference modeling. These fundamental limitations create a persistent gap between user intentions and system interp…