We statistically investigate the distribution of share price and the distributions of three common financial indicators using data from approximately 8,000 companies publicly listed worldwide for the period 2004-2013. We find that the distribution of share price follows Zipf's law; that is, it can be approximated by a …
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48 results for “distributive shares”
This paper proposes a new D2D data sharing approach to improve distributed machine learning training speed.
problem Straggler dilemma in distributed edge learning.
method Proposes a D2D data sharing approach to balance computation loads and optimize radio resource allocation.
result Significantly reduces training delay and enhances training accuracy in non-i.i.d. data environments.
We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities. Each data distribution is modeled with a mixture of generator distributions. As the generators are partially shared between the modeling of different true data distributions, shared ones ca…
We study the problem of distributed multi-task learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared low-dimensional subspaces, i.e. when the predictor matrix has low rank. We consider a setting where each task is handled by a different machine, with sa…
Distributed machine learning has been widely studied in order to handle exploding amount of data. In this paper, we study an important yet less visited distributed learning problem where features are inherently distributed or vertically partitioned among multiple parties, and sharing of raw data or model parameters amo…
Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations. SplitNN does not share raw data or model details with collaborating institutions. The prop…
Unpaired multi-domain causal representation learning is possible with sufficient conditions.
problem Learning shared causal representation from unpaired data across domains.
method Identify sufficient conditions for joint distribution and shared causal graph recovery.
result Practical method to recover shared latent causal graph from marginal distributions.
SharedMF uses secret sharing to protect privacy in distributed recommendation systems.
problem Privacy issues in multi-source data for recommendation systems.
method Federated learning and secret sharing technology.
result SharedMF achieves faster execution speed and better data adaptability compared to homomorphic encryption methods.
New method identifies shared components from unpaired multimodal mixtures.
problem Identify shared components from unpaired multimodal mixtures.
method Distribution divergence minimization-based loss with sufficient conditions for identifiability.
result Sufficient conditions for shared component identifiability from unaligned multimodal mixtures.
Compensation-based risk-sharingq-fin.RM
The paper analyzes fairness of compensation-based risk-sharing schemes for fund payouts.
problem Fair allocation of payouts in an endowment contingency fund.
method Analyzes two types of administrators and general non-negative loss distributions.
result General conditions for actuarial fairness are provided.
Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomnesscs.LG
Algorithm reduces regret in distributed kernel bandits with shared randomness.
problem Minimizing regret in collaborative function maximization.
method Uniform exploration at local agents and shared randomness with central server.
result Achieves optimal regret order with sublinear communication cost.
Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)cs.CR
Distributed learning across a coalition of organizations allows the members of the coalition to train and share a model without sharing the data used to optimize this model. In this paper, we propose new secure architectures that guarantee preservation of data privacy, trustworthy sequence of iterative learning and equ…
Insurance benefits risk sharing for finite mean risks but not for infinite mean risks.
problem The effect of risk sharing and diversification for infinite mean risks.
method Investigation of risk sharing and diversification for infinite mean models, including stable, Pareto, and Fréchet distributions.
result Risk sharing can have a negative effect for infinite mean models, a phenomenon known as the nondiversification trap.
Scaling Law for the Distribution of Fluctuations of Share Volumecond-mat.stat-mech
We show power-scaling behaviors for fluctuations in share volume, which no other studies have so far done. After analyzing a database of the daily transactions for all securities listed on the Tokyo Stock Exchange, we selected 1050 large companies that each had an unbroken series of daily trading activity from January …
Comment on ``Tests of scaling and universality of the distributions of trade size and share volume: Evidence from three distinct markets" by Plerou and Stanley, Phys. Rev. E 76, 046109 (2007)
A mechanism to share risks and costs with guarantees against extreme outcomes.
problem Softening extreme individual burdens in risk sharing schemes.
method Formalizes Certified Allocation Problem; uses Conformal Risk Sharing with interpretable sharing policy and split conformal calibration.
result Reduces extreme obligations for high-risk agents while controlling harm to others.
Multi-domain translation seeks to learn a probabilistic coupling between marginal distributions that reflects the correspondence between different domains. We assume that data from different domains are generated from a shared latent representation based on a structural equation model. Under this assumption, we show th…
Optimal high-dimensional and nonparametric distributed testing under communication constraintsmath.ST
Optimal distributed testing under communication constraints with shared randomness.
problem Signal detection in a distributed system with limited communication.
method Derivation of minimax testing errors, distributed testing algorithms, and theoretical lower bounds.
result Consistent nonparametric distributed testing is possible even with minimal communication.
Quaternion self-attention reduces computational cost and improves performance.
problem Existing quaternion self-attention increases computational cost and diverges attention distributions.
method Proposes a shared-score quaternion self-attention mechanism.
result Reduces score-computation multiplications by 75% and softmax operations from four to one.
Do firms share the same functional form of their growth rate distribution? A new statistical testphysics.data-an
We introduce a new statistical test of the hypothesis that a balanced panel of firms have the same growth rate distribution or, more generally, that they share the same functional form of growth rate distribution. We applied the test to European Union and US publicly quoted manufacturing firms data, considering functio…
Mathew Effect in Artificial Stock Marketcond-mat.stat-mech
In this article, we established a stock market model based on agents' investing mentality. The agents decide whether to purchase the shares at the probability, according to their anticipation of the market's behaviors. The expectation of the amount of shares they want to buy is directly proportional to the value of ass…
Exact optimality achieved in distributed mean estimation with shared randomness.
problem Achieving optimal communication, privacy, and utility tradeoffs in distributed mean estimation.
method Utilization of a rotationally symmetric shared random codebook and a -closest encoding mechanism.
result Proposed mechanism achieves exact optimality for randomly rotated simplex codebook.
CEDAR efficiently analyzes distributed EHR data without sharing patient-level info.
problem Analyzing patient-level data from multiple EHRs databases without sharing raw data.
method Tackles by turning problem into missing data, incorporating posterior samples.
result Improves efficiency and privacy of parameter estimates in sparse regressions.
CoreFlow models matrix-valued distributions efficiently, preserving shared low-rank structure.
problem Learning matrix-valued distributions from high-dimensional and incomplete data.
method Low-rank flow model that learns shared row/column subspaces and trains a normalizing flow on the core.
result CoreFlow improves generation quality in few-sample regimes and remains competitive in data-rich settings.
Representation Results for Law Invariant Recursive Dynamic Deviation Measures and Risk Sharingq-fin.RM
In this paper we analyze a dynamic recursive extension of the (static) notion of a deviation measure and its properties. We study distribution invariant deviation measures and show that the only dynamic deviation measure which is law invariant and recursive is the variance. We also solve the problem of optimal risk-sha…
The paper proposes methods to find a shared active subspace for multivariate vector-valued functions.
problem Minimizing the deviation between function evaluations in the original and reconstructed spaces.
method Manipulating gradients or SPD matrices to identify a shared structure.
result Summing SPD matrices often identifies the best shared active subspace.
We introduce and discuss a nonlinear kinetic equation of Boltzmann type which describes the evolution of wealth in a pure gambling process, where the entire sum of wealths of two agents is up for gambling, and randomly shared between the agents. For this equation the analytical form of the steady states is found for va…
A new method is proposed to obtain the risk neutral probability of share prices without stochastic calculus and price modeling, via an embedding of the price return modeling problem in Le Cam's statistical experiments framework. Strategies-probabilities and are thus determined and used, respective…
In many distributed learning problems, the heterogeneous loading of computing machines may harm the overall performance of synchronous strategies. In this paper, we propose an effective asynchronous distributed framework for the minimization of a sum of smooth functions, where each machine performs iterations in parall…
The paper analyzes how shared priors affect Bayesian data fusion performance.
problem Effect of shared priors on Bayesian data fusion performance.
method Theoretical analysis using two divergences common in Bayesian inference.
result Theoretical analysis and experimental validation of performance behavior.
The paper optimizes private data sharing by selecting statistics and using MCMC for Bayesian inference.
problem Optimizing private data sharing by selecting statistics and performing Bayesian inference.
method Promotes Fisher information for statistic selection and proposes MCMC algorithms for inference.
result The Fisher information of the privatized statistic predicts the relative performance of the statistic in Bayesian estimation.
Study compares federated learning and coreset approaches for privacy in distributed machine learning.
problem Measuring privacy in distributed machine learning approaches.
method Comparison of federated learning and coreset approaches using membership inference attack.
result Federated learning offers better privacy than coreset, but with higher communication cost.
We consider high-dimensional distribution estimation through autoregressive networks. By combining the concepts of sparsity, mixtures and parameter sharing we obtain a simple model which is fast to train and which achieves state-of-the-art or better results on several standard benchmark datasets. Specifically, we use a…
'Egalitarian pooling and sharing of longevity risk', a.k.a. 'The many ways to skin a tontine cat'q-fin.RM
The paper explores how to fairly share longevity risk among participants of tontine schemes.
problem Fair distribution of longevity risk among participants with varying wealth and health.
method Develops a modeling framework for sharing benefits among survivors in tontine schemes.
result There are multiple ways to share longevity risk, depending on social cohesion.
We consider the risk sharing problem for capital requirements induced by capital adequacy tests and security markets. The agents involved in the sharing procedure may be heterogeneous in that they apply varying capital adequacy tests and have access to different security markets. We discuss conditions under which there…
We analyze systems of agents sharing light-tailed risky claims issued by different financial objects. Assuming exponentially distributed claims, we obtain that both agents' and system's losses follow generalized exponential mixture distributions. We show that this leads to qualitatively different results on individual …
Enhances generative model accuracy through knowledge transfer.
problem Improving generative model precision across different tasks.
method Introduces a novel framework for transfer learning using shared structures.
result Demonstrates enhanced performance in diffusion and normalizing flows models.
We build a multiassets heterogeneous agents model with fundamentalists and chartists, who make investment decisions by maximizing the constant relative risk aversion utility function. We verify that the model can reproduce the main stylized facts in real markets, such as fat-tailed return distribution and long-term mem…
This paper introduces a privacy-aware Bayesian approach that combines ensembles of classifiers and clusterers to perform semi-supervised and transductive learning. We consider scenarios where instances and their classification/clustering results are distributed across different data sites and have sharing restrictions.…
Two approaches improve parameter learning in various mixture models.
problem Parameter learning in mixture models.
method Complex-analytic and algebraic-combinatorial methods.
result Improved sample sufficiency for parameter estimation in specific mixture models.
PerPCA separates unique and shared features from heterogeneous data.
problem Extracting shared and unique features from data collected from different sources with varying trends.
method Personalized PCA (PerPCA) uses orthogonal global and local principal components to encode both unique and shared features.
result PerPCA can identify and recover both unique and shared features under mild conditions.
New test ensures quality of shared data in machine learning.
problem Ensuring quality of external data in machine learning tasks.
method Distribution-free two-sample testing procedures grounded in conformal outlier detection.
result Identifies valuable external data agents for model personalization.
A novel Bayesian framework for private linear regression with MCMC.
problem Private linear regression in a distributed setting.
method Generative statistical model, MCMC algorithms, fast Bayesian estimation.
result The proposed methods provide well-rounded estimation and prediction.
Unified Bayesian framework for clustered federated learning improves model performance.
problem Handling non-IID client data in federated learning.
method A unified Bayesian framework for clustered federated learning that associates clients to clusters and proposes practical algorithms for data associations.
result The proposed framework increases model performance by circumventing the need for unique client-cluster associations.
Deep learning with medical data often requires larger samples sizes than are available at single providers. While data sharing among institutions is desirable to train more accurate and sophisticated models, it can lead to severe privacy concerns due the sensitive nature of the data. This problem has motivated a number…
The study finds that the export shares of machinery and food/crude materials are significantly correlated with GDP.
problem Understanding the relationship between export shares and GDP across different commodity sectors.
method Analysis of GDP and international trade data using the SITC classification from 1962 to 2000.
result The export shares of machinery and food/crude materials are significantly correlated with GDP, following a power-law relationship.
BGANs enable multi-agent learning with distributed private datasets.
problem Learning from distributed private datasets in multi-agent scenarios.
method Brainstorming GAN architecture for fully distributed learning.
result BGANs generate high-quality data samples with lower JSD and FID.
Improved DLG extracts accurate labels from gradients, overcoming DLG's convergence issues.
problem Private training data leakage from shared gradients in distributed learning systems.
method Proposes iDLG, a simple approach to synthesize accurate labels from gradients.
result iDLG reliably extracts ground-truth labels from gradients, unlike DLG.