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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

Asymptotic Equivalence of Immediate and Deferred Acceptance

https://arxiv.org/html/2607.24970v2
That total number of applications mirrors the classical coupon-collector problem: if each breakfast cereal box contains one of nn equally likely coupons, how many cereal boxes do I need to buy to collect all nn coupons

Stabilization of First-Order Partial Integro-Differential Equations with

https://arxiv.org/html/2608.02851v1
org perpetual non-exclusive license arXiv:2608

Activation-Guided Neuron Intervention to Induce Alzheimer’s-Related

https://arxiv.org/html/2608.03067v1
7Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain

A Heterogeneous Spatiotemporal GARCH Model: A Predictive Framework for

https://arxiv.org/html/2508.20101v1
However, in real-world financial systems, individual assets are not isolated

1. Introduction

https://arxiv.org/html/2608.02909v1
In practice, most researchers use the generated variables “as is� on the right-hand side of the regression, as if they captured the true, latent variable of interest

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

Learning to Control Unknown Strongly Monotone Games

https://arxiv.org/html/2407.00575v3
Even worse, the manager would need to collect the parameters periodically if they are time-varying, and then solve the large-scale optimization problem again

United We Stand: Decentralized Multi-Agent Planning With Attrition

https://arxiv.org/html/2407.08254v2
We prove that our approach guarantees that the average joint action of all agents converges to a Nash equilibrium (NE) if every agent applies the same RM procedure in any cooperative game with a submodular utility function

Learning Equilibrium with Estimated Payoffs in Population Games

https://arxiv.org/html/2407.06328v2
In such dynamic game settings, if agents have limited capabilities for observing the game’s state, they must estimate it and use this estimate to infer expected payoffs and select one of available strategies

Untitled Document

https://arxiv.org/html/2607.28294v1
A second scheme preserves the realized count and sets the number of events in the bootstrap world as n∗=n​(T)n^{\ast}=n(T)

Finite-Time Analysis of Asynchronous Multi-Agent TD Learning

https://arxiv.org/html/2407.20441v1
However, in real-world multi-agent systems, inter-node communication imposes strong constraints, such as limited link capacity and transmission delays [4, 5, 6]

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

Eliminating Majority Illusion is Easy

https://arxiv.org/html/2407.20187v1
If there are more blue nodes than red nodes then we say blue is the majority

Forthcoming in SIAM Journal on Financial Mathematics Decentralised

https://arxiv.org/html/2309.08431v3
Concentration risk refers to the decrease in fee revenue if the marginal exchange rate (akin to the midprice in a limit order book) in the pool exits the LP’s range of liquidity

Maximizing utility in multi-agent environments by anticipating the

https://arxiv.org/html/2407.04889v1
Therefore, if an agent is good at anticipating the behavior of the other agents, in particular how they will make decisions in each round as a function of their experience that far, it could try to judiciously make its own decisions over the rounds of the interaction so as to influence the other agents to behave in a

1 Introduction

https://arxiv.org/html/2407.15077v2
2 Problem Definition 3 Method 3.1 Monotonic Improvement of B2MAPO scheme 3.2 B2MAPO Framework 3.2.1 Batch Sequence Generation Layer 3.2.2 Batch by Batch Policy Optimization Layer 4 DAG-based B2MAPO Algorithm 4.1 Hierarchical DAG

Stochastic Games with Minimally Bounded Action Costs

https://arxiv.org/html/2407.18010v2
1 Introduction 2 Formulation 3 Existence of a Value and Saddle Point Equilibrium 4 Learning in Unknown Environments 4.1 Convergence using Linear Function Approximators 5 Intervention Budget Constraints 6 Discussion on Subcases 7 Related

Inverse Learning of Latent Risk-Neutral Densities from Irregular Option

https://arxiv.org/html/2607.27188v1
org perpetual non-exclusive license arXiv:2607

Report GitHub Issue

https://arxiv.org/html/2608.03077v1
org perpetual non-exclusive license arXiv:2608

Computer Vision and Pattern Recognition

https://arxiv.org/list/cs.CV/new
Project Page: this https URL [2] arXiv:2608

Computational Geometry

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

Effort-Centric Fairness in Lending Decisions

https://arxiv.org/html/2607.28847v1
2 Proof of Lemma 1 (Optimality of Boundary Crossing) D

Hardware Architecture

https://arxiv.org/list/cs.AR/new
more | all New submissions (showing 4 of 4 entries) [1] arXiv:2608

Systems and Control

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

Microstructural Foundations of Rough Noise1footnote

https://arxiv.org/html/2607.29442v2
1footnoteFootnotefootnotesFootnotes1footnote 1We are grateful to Carsten Chong for fruitful discussions on rough noise. We benefitted from comments by and discussions with Kim Christensen, Mikko Pakkanen, Bent Jesper Christensen, and Mikkel

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https://arxiv.org/pdf/1811.12628
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General Relativity and Quantum Cosmology

https://arxiv.org/list/gr-qc/new
We observe that each emitted graviton carries away an angular momentum of $\dot{J}/\dot {N}\simeq 2\hbar$, not only for elliptical orbits (as has already been pointed out by Page [arXiv:2409

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https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv

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https://arxiv.org/pdf/2609.18842
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