arXiv research

Algorithm identifies best arm with prior info in structured bandits.

The paper highlights how machine learning calibrations can be biased by training data.

We consider fundamental questions of arbitrage pricing arising when the uncertainty model is given by a set of possible mutually singular probability measures. With a single probability model, essential equivalence between the absence of arbitrage and the existence of an equivalent martingale measure is a folk theorem,…

Enhances RL with function approximation, improving regret bounds.

We consider the stochastic multi-armed bandit problem with a prior distribution on the reward distributions. We are interested in studying prior-free and prior-dependent regret bounds, very much in the same spirit as the usual distribution-free and distribution-dependent bounds for the non-Bayesian stochastic bandit. B…

Bayesian model averaging (BMA) is the state of the art approach for overcoming model uncertainty. Yet, especially on small data sets, the results yielded by BMA might be sensitive to the prior over the models. Credal Model Averaging (CMA) addresses this problem by substituting the single prior over the models by a set …

This paper introduces a novel theoretically sound approach for the celebrated CMA-ES algorithm. Assuming the parameters of the multi variate normal distribution for the minimum follow a conjugate prior distribution, we derive their optimal update at each iteration step. Not only provides this Bayesian framework a justi…

Spike-and-slab priors are popular Bayesian solutions for high-dimensional linear regression problems. Previous theoretical studies on spike-and-slab methods focus on specific prior formulations and use prior-dependent conditions and analyses, and thus can not be generalized directly. In this paper, we propose a class o…

A novel minimax classifier tackles imbalanced datasets with few minority samples.

MetaTS learns to explore better by meta-learning prior from bandit instances.

This paper explores minimax-Bayes solutions for reinforcement learning problems.

In this note we present a generative model of natural images consisting of a deep hierarchy of layers of latent random variables, each of which follows a new type of distribution that we call rectified Gaussian. These rectified Gaussian units allow spike-and-slab type sparsity, while retaining the differentiability nec…

MixTS uses a mixture prior to analyze Thompson Sampling in multi-task learning.

The paper optimizes DIA purchase policies using lifecycle models and asset allocation.

Study optimizes resource allocation in noisy systems for better control.

The paper proposes an asset allocation strategy using the Sortino ratio for better performance.

This paper tackles post-trade allocation inefficiencies and presents a uniform return allocation method.

This paper examines allocation mechanisms in markets with transfer costs, showing how these costs affect economic efficiency.

Novel Bayesian approach for non-stationary linear contextual bandits.

Paper introduces a new method for allocating capital based on risk measures from ruin theory.

The aims of this study are twofold. First, we consider an optimal risk allocation problem with non-convex preferences. By establishing an infimal representation for distortion risk measures, we give some necessary and sufficient conditions for the existence of optimal and asymptotic optimal allocations. We will show th…

Framework uses hindsight regret to audit marketing budget allocations.

New method allocates capital based on tail central moments for financial risk assessment.

The paper explores capital allocation using Euler formula with VaR and ES, revealing non-monotonicity and providing estimation methods.

While Bayesian methods are praised for their ability to incorporate useful prior knowledge, in practice, convenient priors that allow for computationally cheap or tractable inference are commonly used. In this paper, we investigate the following question: for a given model, is it possible to compute an inference result…

Capital allocation principles are used in various contexts in which a risk capital or a cost of an aggregate position has to be allocated among its constituent parts. We study capital allocation principles in a performance measurement framework. We introduce the notation of suitability of allocations for performance me…

The financial crisis showed the importance of measuring, allocating and regulating systemic risk. Recently, the systemic risk measures that can be decomposed into an aggregation function and a scalar measure of risk, received a lot of attention. In this framework, capital allocations are added after aggregation and can…

The paper analyzes insurance pricing and capital allocation in imperfect markets.

New optimal prior avoids bias in complex models with limited data.

Optimal resource allocation in censored semi-bandits with unknown thresholds.

New risk-sharing rules induced by capital allocation principles.

We study the problem of allocating stocks to dark pools. We propose and analyze an optimal approach for allocations, if continuous-valued allocations are allowed. We also propose a modification for the case when only integer-valued allocations are possible. We extend the previous work on this problem to adversarial sce…

The European insurance sector will soon be faced with the application of Solvency 2 regulation norms. It will create a real change in risk management practices. The ORSA approach of the second pillar makes the capital allocation an important exercise for all insurers and specially for groups. Considering multi-branches…

In this paper we develop a novel methodology for estimation of risk capital allocation. The methodology is rooted in the theory of risk measures. We work within a general, but tractable class of law-invariant coherent risk measures, with a particular focus on expected shortfall. We introduce the concept of fair capital…

This work reviews and tests risk allocation strategies in finance, highlighting Shapley allocation's advantages.

Enhances topic models to better handle polysemous words.

Optimal resource allocation is a fundamental challenge for dense and heterogeneous wireless networks with massive wireless connections. Because of the non-convex nature of the optimization problem, it is computationally demanding to obtain the optimal resource allocation. Recently, deep reinforcement learning (DRL) has…

Study resource allocation strategies in sequential decisions with unknown rewards.

Facing the FRTB, banks need to allocate their capital to each business units or risk positions to evaluate the capital efficiency of their strategies. This paper proposes two computationally efficient allocation methods which are weighted according to liquidity horizon. Both methods provide more stable and less negativ…

A Nash game theory approach allocates capital requirements among financial institutions.

The paper evaluates index-based allocation policies using data from randomized control trials.

Develops a framework to analyze financial structures.

Paper uses Simulated Bifurcation for quick asset allocation optimization.

The minimization of some multivariate risk indicators may be used as an allocation method, as proposed in Cénac et al. [6]. The aim of capital allocation is to choose a point in a simplex, according to a given criterion. In a previous paper [17] we proved that the proposed allocation technique satisfies a set of cohere…

Model predicts global financial market risks and asset allocation.

Enhanced synthetic dataset improves asset allocation analysis.

Study systemic risk measures and capital allocation rules, showing commonalities.

Paper uses LLMs for sector allocation, showing better returns.