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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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69139208277 · Jun 202019922001200920172026
48 results for participation rate

The paper uncovers two key laws of market impact influenced by volume and participation rate.

problem Understanding the roles of volume and participation rate in market price response.
method Extending the no arbitrage approach to include sophisticated market participants, deriving price dynamics from order flow dynamics.
result Recovery of two square root laws governing market impact.

Optimizes capital structure for life insurance companies with surplus participation.

problem Determining the optimal participation rate in life insurance contracts.
method Adapted Leland's dynamic capital structure model to life insurance context.
result Optimal participation rate is highly sensitive to contract duration and tax rate.

This work analyzes generalization in federated learning using information theory.

problem Generalization performance in federated learning is less explored compared to centralized learning.
method The work applies an information-theoretic analysis via the conditional mutual information (CMI) framework to study federated learning's two-level generalization.
result The work derives multiple CMI-based bounds, including hypothesis-based CMI bounds and fast-rate evaluated CMI bounds, which improve convergence rates for specific model aggregation strategies and structured loss functions.

Study of participating policies with guaranteed minimum interest rate and surrender option.

problem Analyzing the value and optimal surrender strategy of participating policies with minimum interest rate guarantee and surrender option.
method Probabilistic analysis using optimal stopping and free boundary theory.
result Identification of an optimal surrender strategy involving stop-loss and too-good-to-persist boundaries.

FedAvg converges linearly to global minimum in federated learning with partial participation.

problem Challenges in federated learning with partial client participation.
method Federated averaging (FedAvg) method for over-parameterized neural networks.
result FedAvg converges to global minimum at a linear rate after t iterations.

Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to the lack of feedback from physical therapists (PTs). Here, we analyze the feasibility of using trunk sway data and machine learning (ML) techniques to automatically evaluate balance, providing accurate assessments out…

2019-06-07abs ↗pdf ↗

Optimizes pension mix of PAYGO, EET, and individual savings.

problem Balancing PAYGO, EET, and individual savings in funded pension schemes.
method Solves a Nash equilibrium between pension participants and government, considering age-dependent preferences and optimal asset allocation.
result Identifies critical ages and optimal contribution rates for maximizing overall utility.

Federated learning enables a large amount of edge computing devices to jointly learn a model without data sharing. As a leading algorithm in this setting, Federated Averaging (\texttt{FedAvg}) runs Stochastic Gradient Descent (SGD) in parallel on a small subset of the total devices and averages the sequences only once …

2019-07-04abs ↗pdf ↗

Unified analysis of Federated Averaging and Nesterov FedAvg for linear speedup.

problem Understanding convergence of FL algorithms under non-i.i.d. data and partial participation.
method Systematic study of convergence guarantees for FedAvg and Nesterov FedAvg under different conditions.
result Unified analysis of linear speedup for FedAvg and Nesterov FedAvg in various settings.

This paper analyzes error feedback in compressed federated learning for non-convex optimization problems.

problem Reducing communication cost in federated learning with biased gradient compression.
method Proposes Fed-EF, a compressed federated learning scheme with error feedback, and analyzes its convergence rate and performance under partial client participation.
result Fed-EF can match the convergence rate of full-precision FL under data heterogeneity with a linear speedup and no extra slow-down factor due to stale error compensation.

Paper tackles unknown participation in FL, proposing FedAU for better performance.

problem Unknown participation statistics in federated learning impact performance.
method Adapting aggregation weights in FedAvg based on participation history.
result FedAU converges to optimal solution and has desirable properties.

The paper examines optimal annuitization for retirees with potential post-retirement work.

problem Post-retirement labor participation complicates optimal annuitization decisions.
method Stochastic control, optimal stopping, expected utility maximization, martingale methodology, duality techniques.
result The optimal annuitization time is linearly dependent on initial wealth, with or without labor income.

Study uses ML to predict non-participation in ELSA COVID-19 follow-up studies.

problem Predicting non-participation in follow-up surveys due to various factors.
method Used machine learning algorithms including KNN, RF, AdaBoost, logistic regression, NN, and SVC.
result Random Forest (RF) outperforms other models in balanced accuracy.

Artemis framework improves distributed learning with bidirectional compression and partial participation.

problem Learning in distributed or federated settings with communication constraints and device partial participation.
method Artemis framework using bidirectional compression, memory mechanism, and Polyak-Ruppert averaging.
result Fast rates of convergence (linear up to a threshold) under weak assumptions on stochastic gradients.

We consider models of financial markets in which all parties involved find incentives to participate. Strategies are evaluated directly by their virtual wealths. By tuning the price sensitivity and market impact, a phase diagram with several attractor behaviors resembling those of real markets emerge, reflecting the ro…

2007-08-01abs ↗pdf ↗

This paper analyzes deep federated learning for low-dimensional data, revealing intrinsic dimensionality's role in convergence rates.

problem Insufficient investigation of generalization error in heterogeneous federated learning, especially for low-dimensional data.
method Statistical analysis of deep federated regression in a two-stage sampling model.
result Intrinsic dimensionality, characterized by entropic dimension, determines convergence rates for deep learners.

New algorithm reduces communication time in federated learning.

problem Intermittent connectivity and non-i.i.d. data slow federated learning convergence.
method Lyapunov optimization for efficient device scheduling.
result Significant reduction in communication time with improved convergence rates.

FedSGM tackles constrained federated learning with unified framework.

problem Functional constraints, communication bottlenecks, local updates, and partial client participation in federated learning.
method Unified framework based on switching gradient method, incorporating bi-directional error feedback, and soft switching for stability.
result Achieves O(1/T)\boldsymbol{\mathcal{O}}(1/\sqrt{T}) convergence rate with high-probability bounds decoupling from sampling noise.

FedAVOT improves federated learning by aligning user distributions.

problem Partial client participation leads to biased and unstable updates in federated learning.
method Formulates aggregation as masked optimal transport to align availability and importance distributions.
result Achieves a standard O(1/√T) rate, independent of the number of participating users per round.

In his stimulating article on the reasons for two puzzling observations about the behaviour of interest rates, exchange rates and the rate of inflation, Charles Engel (2016) puts forward an explanation that rests on the concept of a non-pecuniary liquidity return on assets. Albeit intriguing the analysis struggles to a…

2016-04-29abs ↗pdf ↗

Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.

problem Difficult prediction of medium-horizon Alzheimer's disease progression due to tied clinical scores and irregular biomarker observations.
method Developed a residual gap-aware transformer that combines statistical reference with transformer-based residual learning.
result The proposed model reduces mean error and improves prediction-observation correlation compared to baseline models.

This paper optimizes portfolio compression by reducing excess notional in market contracts.

problem Reduction of excess notional in market contracts to decrease systemic risk.
method Examines and solves portfolio compression problems using graph theory and algorithms.
result Developed a clearing algorithm and method to compute maximum volume conservative compression.

Paper proposes MMVFL for multi-class VFL with multiple participants.

problem Privacy-preserving multi-class VFL with multiple participants.
method Extends multi-view learning to enable label sharing among multiple VFL participants.
result MMVFL effectively shares label information among multiple VFL participants and matches multi-class classification performance.

Bayesian rating system for large competitions improves prediction and efficiency.

problem Rating systems for large, competitive events like online programming contests.
method Developed a Bayesian rating system for many participants, proving robustness and runtime.
result The system outperforms existing systems in accuracy and computation speed.

Labor productivity in developed countries is analyzed and modeled. Modeling is based on our previous finding that the rate of labor force participation is a unique function of GDP per capita. Therefore, labor productivity is fully determined by the rate of economic growth, and thus, is a secondary economic variable. In…

2008-11-13abs ↗pdf ↗

Labor productivity in Turkey, Spain, Belgium, Austria, Switzerland, and New Zealand has been analyzed and modeled. These counties extend the previously analyzed set of the US, UK, Japan, France, Italy, and Canada. Modelling is based on the link between the rate of labor participation and real GDP per capita. New result…

2010-01-27abs ↗pdf ↗

Proposes SROF for row-wise fusion in federated learning for multivariate responses.

problem Heterogeneous client models with shared variable-level structure.
method Sparse Row-wise Fusion (SROF) regularizer and RowFed algorithm.
result Empirically shows consistent error reduction and stronger variable-level cluster recovery.

This paper introduces a novel framework for designing fair and sustainable unemployment benefits, grounded in cooperative game theory and real-time fiscal policy. The labor market is modeled as a coalitional game, where a random subset of participants is employed, generating stochastic economic output. To ensure fairne…

2018-08-26abs ↗pdf ↗

FedAMD framework improves federated learning with partial client participation.

problem Data heterogeneity and inactive client updates in partial client participation.
method Anchor sampling divides clients into anchor and miner groups, using large and small batches respectively.
result FedAMD achieves faster convergence and improved model performance compared to state-of-the-art methods.

FetchSGD reduces communication in federated learning with sketching.

problem Communication bottlenecks and convergence issues in federated learning.
method FetchSGD uses Count Sketch to compress and merge model updates efficiently.
result FetchSGD achieves high compression rates and good convergence without sparse client participation.

New algorithm achieves linear speedup in non-i.i.d. federated bilevel learning.

problem Linear speedup in convergence for non-i.i.d. datasets in federated bilevel optimization.
method Proposes FedMBO with a novel client sampling scheme for non-i.i.d. datasets.
result Achieves a convergence rate of O(1/√(nK) + 1/K + √n/K³/²).

We present an extended version of the recently proposed "LLOB" model for the dynamics of latent liquidity in financial markets. By allowing for finite cancellation and deposition rates within a continuous reaction-diffusion setup, we account for finite memory effects on the dynamics of the latent order book. We compute…

2017-10-10abs ↗pdf ↗

Optimal trading strategy in Proof-of-Stake blockchain using continuous-time control.

problem Finding the optimal balance between stake utility and consumption utility in Proof-of-Stake blockchain.
method Continuous-time control approach, dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations.
result Close-form solutions for linear and convex utility functions, optimal strategies identified.

The purpose of this article is to introduce, analyze and compare two performance participation methods based on a portfolio consisting of two risky assets: Option-Based Performance Participation (OBPP) and Constant Proportion Performance Participation (CPPP). By generalizing the provided guarantee to a participation in…

2013-02-21abs ↗pdf ↗