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The mixed anomaly between these is accommodated by the not-on-site action of the symmetries
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Condensed Matter

https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries

1 Introduction

https://arxiv.org/html/2511.06451v1
1 Data Protocol (arXiv Release) Synthetic risk–neutral generator

You’ve Got to be Efficient: Ambiguity, Misspecification and

https://arxiv.org/html/2604.05327v4
For these problems, it is therefore always optimal for the decision-maker to proceed as if the likelihood were correctly specified and select the resulting optimal decision rule

Mathematics

https://arxiv.org/list/math/new
up to 2000 entries per page: fewer | more | all New submissions (showing 219 of 219 entries) [1] arXiv:2608

A Heterogeneous Spatiotemporal GARCH Model: A Predictive Framework for

https://arxiv.org/html/2508.20101v1
Unlike other spatial GARCH models, our framework does not rely on a fixed adjacency matrix; instead, spatial proximity is defined in a proxy space constructed from balance sheet characteristics

Computation and Language

https://arxiv.org/list/cs.CL/new
In BBO, the search space design, a part of the problem formulation, and the selection of the optimization algorithm are crucial for problem-solving

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
Extending the pattern-generation paradigm of Neural Cellular Automata (NCA), it navigates the degenerate Boolean search space to satisfy a computational task, rather than regenerating a fixed target state

Stochastic Choice with Advertising

https://arxiv.org/html/2608.03504v1
org perpetual non-exclusive license arXiv:2608

Report GitHub Issue

https://arxiv.org/html/2607.22511v2
× Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to Abstract Download PDF

Approximating Choice Data by Discrete Choice Models

https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset M Additional Empirical Application: Fishing-site Choice M

Mitigating Distribution Shift in Stock Price Data via Return-Volatility

https://arxiv.org/html/2508.20108v2
, 2023) rely on adaptation to the representation space, increasing the complexity of the model without directly resolving the distribution shift

I INTRODUCTION

https://arxiv.org/html/2407.10811v1
Specifically, they need a combination of queue length, traffic flow, wait or delay time, car position, and so on as the observation space [2, 3, 4, 5, 6, 7]

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https://arxiv.org/pdf/2006.02318
org/pdfua/ns/id/ pdfuaid PDF/UA ID Schema internal Part of PDF/UA standard part Integer PRISM metadata http://prismstandard

Data Structures and Algorithms

https://arxiv.org/list/cs.DS/new
If the input strings have lengths $N$ and $M$, and their run-length encodings have $n$ and $m$ runs, respectively, the algorithm runs in $O(mN+nM)$ time using $O(nm)$ space

On the Complexity of Learning to Cooperate with Populations of Socially

https://arxiv.org/html/2407.00419v1
W say that a population of adaptive agents is socially intelligent if its members are (1) individually Hannan-consistent and (2) compatible in the sense that any pair of agents will perform nearly as well as some Pareto-optimal Nash equilibrium of the matrix game

United We Stand: Decentralized Multi-Agent Planning With Attrition

https://arxiv.org/html/2407.08254v2
A first family of approaches to address this is given by point-based methods, which scale well, but they may not cover the entire belief space well [35, 42]

Existence of Richter–Peleg representation for general preferences

https://arxiv.org/html/2508.08980v3
org perpetual non-exclusive license arXiv:2508

Report GitHub Issue

https://arxiv.org/html/2608.02609v1
× Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to Abstract Download PDF

Machine Learning Dec 2022

https://arxiv.org/list/cs.LG/2022-12
2001-2004 Showing up to 50 entries per page: fewer | more | all [1] arXiv:2212

Raising Rivals’ Costs on Hybrid Platforms: The Complementarity of Fees

https://arxiv.org/html/2608.02800v1
com in China, Walmart Marketplace and Target Plus in the United States, Otto in Germany, Flipkart in India, and Rakuten in Japan

Multi-Currency AMMs for Decentralized FOREX Markets: Feasibility

https://arxiv.org/html/2607.26405v1
& Optimal Design Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report

Finance-Informed Neural Network: Learning the Geometry of Option Pricing

https://arxiv.org/html/2412.12213v2
Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to

Report GitHub Issue

https://arxiv.org/html/2608.02612v1
org perpetual non-exclusive license arXiv:2608

Computational Geometry

https://arxiv.org/list/cs.CG/new
PR) The Johnson--Lindenstrauss lemma asserts that every set of $n$ points in $d$-dimensional Euclidean space embeds into $O(\varepsilon^{-2}\log n)$-dimensional Euclidean space with distortion at most $1+\varepsilon$

Systems and Control

https://arxiv.org/list/cs.SY/new
more | all New submissions (showing 8 of 8 entries) [1] arXiv:2608

Information Theory

https://arxiv.org/list/cs.IT/new
all New submissions (showing 11 of 11 entries) [1] arXiv:2608

Report GitHub Issue

https://arxiv.org/html/2608.02942v1
org perpetual non-exclusive license arXiv:2608

Microstructural Foundations of Rough Noise1footnote

https://arxiv.org/html/2607.29442v2
Since the null hypothesis of non-rough noise places the roughness index on the boundary of the parameter space, standard asymptotics fail

Optimal testing in a class of nonregular models

https://arxiv.org/html/2403.16413v3
org perpetual non-exclusive license arXiv:2403

Finite-Time Analysis of Asynchronous Multi-Agent TD Learning

https://arxiv.org/html/2407.20441v1
For the MDP, we consider a finite set 𝒮𝒮\mathcal{S}caligraphic_S of n𝑛nitalic_n states, a finite action space 𝒜𝒜\mathcal{A}caligraphic_A, a set of action-dependent Markov transition kernels 𝒫𝒫\mathcal{P}caligraphic_P, a reward function ℛℛ\mathcal{R}caligraphic_R, and we denote by γ∈(0,1)