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Did you mean if cam mojeek site en wikipedia org site lemmy would site:arxiv.org?
Freshness: newest result fetched 25 d ago · median age of these results 25 d · 1 of 30 carry a fetch time
https://arxiv.org/html/2511.06451v1
1 Data Protocol (arXiv Release)
Synthetic risk–neutral generator
https://arxiv.org/pdf/1811.12628
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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)
https://arxiv.org/list/math/new
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https://arxiv.org/list/cs.LG/2022-12
2001-2004
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[1] arXiv:2212
https://arxiv.org/list/q-fin.MF/2021-04
A lacuna in the proof of the old Lemma 3
https://arxiv.org/list/cs.SY/new
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[1] arXiv:2608
https://arxiv.org/html/2508.20108v2
Normalization for Accurate Prediction
1 Introduction
2 Related Works
2.1 Geometric Brownian Motion
2.2 Deep Learning-Based Stock Price Prediction
2.3 Methods for Addressing Distribution Shift
3 Proposed Method
3.1 Overview
3.2 Return
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
https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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https://arxiv.org/html/2407.10811v1
II RELATED WORK
II-A Traditional methods
II-B RL methods.
III PRELIMINARIES
IV METHOD
IV-A RL Setup
IV-B Network design
IV-C Training
IV-D Theoretical Analysis
V EXPERIMENT
V-A Towards Real-world Dataset and Simulation
V-B Benchmark
https://arxiv.org/html/2508.20101v1
Volatility in Financial Networks
1 Introduction
2 Heterogeneous Spatiotemporal GARCH model
2.1 Model Assumptions
2.2 Prediction at Unobserved Locations
2.3 Estimation of Parameter Surface
2.4 Properties of the Estimator
3 Monte Carlo
https://arxiv.org/html/2608.02800v1
and Self-Preferencing
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https://arxiv.org/html/2607.26405v1
& Optimal Design
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https://arxiv.org/html/2607.28294v1
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https://arxiv.org/html/2407.20441v1
I Introduction
II System Model and Problem Formulation
III Main result
IV Proof of the main result
V Simulations
VI Conclusion and Future Work
Finite-Time Analysis of Asynchronous Multi-Agent TD Learning
Nicolò Dal Fabbro, Arman Adibi
https://arxiv.org/html/2412.12213v2
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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
https://arxiv.org/html/2407.04974v1
Observable Environments
I Introduction
II State of the art
II-A Settings
II-B State estimation in model-based learning
II-C State estimation in model-free RL
II-C1 Neural Network-based Approaches
II-C2 Non-neural-based Approach
II-D
https://arxiv.org/list/cs.CL/new
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A core calculus gives an effect-confinement lemma and six safety properties
https://arxiv.org/html/2407.17789v2
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arXiv:2407
https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2608.02694
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https://arxiv.org/pdf/2508.11152
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https://arxiv.org/pdf/2608.02609
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