arXiv research

Optimal risk sharing without convex preferences using aggregate convexity.

The paper examines how share buybacks impact a company's earnings per share.

Paper finds a method to compute fair risk-sharing rules.

There is a growing interest in joint multi-subject fMRI analysis. The challenge of such analysis comes from inherent anatomical and functional variability across subjects. One approach to resolving this is a shared response factor model. This assumes a shared and time synchronized stimulus across subjects. Such a model…

The paper proposes methods to find a shared active subspace for multivariate vector-valued functions.

New model extracts shared brain activity patterns from fMRI data.

The paper extends deep multi-task learning to diverse domains, finding shared functionality.

We use the theory of coherent measures to look at the problem of surplus sharing in an insurance business. The surplus share of an insured is calculated by the surplus premium in the contract. The theory of coherent risk measures and the resulting capital allocation gives a way to divide the surplus between the insured…

The paper addresses risk sharing and variability measures among agents with general risk preferences.

Share prices of financial companies from the S&P 500 list have been modeled by a linear function of consumer price indices in the USA. The Johansen and Engle-Granger tests for cointegration both demonstrated the presence of an equilibrium long-term relation between observed and predicted time series. Econometrically, t…

PPG separates policy and value function training phases for better reinforcement learning efficiency.

Algorithm reduces regret in distributed kernel bandits with shared randomness.

Modeling shared mobility demand considering supply limitations.

Paper investigates Lambda Value-at-Risk under ambiguity and risk sharing.

The paper studies an oligopolistic equilibrium model of financial agents who aim to share their random endowments. The risk-sharing securities and their prices are endogenously determined as the outcome of a strategic game played among all the participating agents. In the complete-market setting, each agent's set of st…

Improved AutoDML estimator for causal inference using outcome-adapted shared covariate representation.

Generalizes risk sharing models to a continuum of agents.

Unified multitask learning framework for mixed-type outcomes.

This work formalizes and extends parameter sharing in multi-agent reinforcement learning.

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…

Securely share encrypted data for machine learning training.

Developed policy gradient methods for stochastic control with exit time, outperforming traditional techniques in share repurchase pricing.

Theoretical analysis of CNNs' inductive biases and their efficiency in approximating functions.

A competing market model with a polyvariant profit function that assumes "zeitnot" stock behavior of clients is formulated within the banking portfolio medium and then analyzed from the perspective of devising optimal strategies. An associated Markov process method for finding an optimal choice strategy for monovariant…

Improves shared encoder representations for better multi-task learning performance.

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…

Paper provides new bounds for risk aggregation and sharing.

Optimizes risk sharing with multiple models under uncertainty.

Study optimizes shared singular subspace estimation from noisy matrices.

Develops a machine-learning framework for optimal share repurchase hedging.

We propose a novel classification model for weak signal data, building upon a recent model for Bayesian multi-view learning, Group Factor Analysis (GFA). Instead of assuming all data to come from a single GFA model, we allow latent clusters, each having a different GFA model and producing a different class distribution…

SHA improves fMRI alignment for cognitive state discovery.

Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tas…

New concept of attitude towards probability introduced in risk sharing problems.

The study examines how weight sharing, equivariance, and locality affect the sample complexity of neural networks.

NPAS trains neural networks with a fixed parameter budget, improving performance and compactness.

A new meta-learning method using shared variational inference.

Decentralized detection avoids sharing data, controls false discoveries.

As a highlighting research topic in the multimedia area, cross-media retrieval aims to capture the complex correlations among multiple media types. Learning better shared representation and distance metric for multimedia data is important to boost the cross-media retrieval. Motivated by the strong ability of deep neura…

We study the Merton problem of optimal consumption-investment for the case of two investors sharing a final wealth. The typical example would be a husband and wife sharing a portfolio looking to optimize the expected utility of consumption and final wealth. Each agent has different utility function and discount factor.…

MAML optimizes shared priors for subtasks in a nonconvex meta-objective.

Improved supervised EM learning for shared kernel models with feature space partitioning.

BIDIFAC integrates multi-platform, multi-cohort data for shared and unique patterns.

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…

BASIS improves LLM reasoning by sharing batchwise rollout info, reducing MSE by 69%.

Toehold purchase, defined here as purchase of one share in a firm by an investor preparing a tender offer to acquire majority of shares in it, reduces by one the number of shares this investor needs for majority. In the paper we construct mathematical models for the toehold and no-toehold strategies and compare the exp…

New method learns shared structures in non-linear tasks.

New knots share same Upsilon invariant despite different Alexander polynomials.