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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

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

Cryptography and Security

https://arxiv.org/list/cs.CR/new
fewer | more | all New submissions (showing 32 of 32 entries) [1] arXiv:2608

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

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

Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially

https://arxiv.org/html/2407.04974v1
The primary reason for focusing on this MARL setting is that off-policy learning [6, 7, 8, 9] provides a more general setting compared to its on-policy counterpart, as it allows the behavioral policy to differ from the estimated target policy

Mitigating Distribution Shift in Stock Price Data via Return-Volatility

https://arxiv.org/html/2508.20108v2
Extensive experiments on real-world datasets demonstrate that ReVol enhances the performance of the state-of-the-art backbone models in most cases, achieving an average improvement of more than 0

I INTRODUCTION

https://arxiv.org/html/2407.10811v1
However, most RL methods face difficulties when applied in the real world due to three factors: input, output, and the cycle-flow relation

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https://arxiv.org/pdf/2006.02318
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Computational Geometry

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

Matching-Driven Deep Reinforcement Learning for Energy-Efficient

https://arxiv.org/html/2407.13076v1
Such inter-SF interference may increase the possibility of packet loss when the signal-to-interference ratio (SIR) is below a specific threshold [12]

Data Structures and Algorithms

https://arxiv.org/list/cs.DS/new
per page: fewer | more | all New submissions (showing 7 of 7 entries) [1] arXiv:2608

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

Information Theory

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

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

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

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]

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

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

Homogeneous Distributed Observers for Quasilinear Systems

https://arxiv.org/html/2407.05763v1
A system is said to be finite-time stable if it is Lyapunov stable and its states reach zeros at a finite instant of time (which may depend on the initial state of the system)

Hardware Architecture

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

Mathematical Finance Apr 2021

https://arxiv.org/list/q-fin.MF/2021-04
A lacuna in the proof of the old Lemma 3

Systems and Control

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

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https://arxiv.org/pdf/1811.12628
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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

Numerical Analysis

https://arxiv.org/list/cs.NA/new
all New submissions (showing 18 of 18 entries) [1] arXiv:2608

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https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/src/1910.03771
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