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.
Study optimizes resource allocation in noisy systems for better control.
Optimal asset allocation strategy outperforms stochastic benchmark.
Develops a framework to analyze financial structures.
Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances to facilitate stochastic resource allocation tasks. By recognizing the central role of Lagrange multipliers, the unde…
This work tackles resource allocation in asynchronous and stochastic systems.
Investors face constraints in Heston's model; optimal allocation differs from naive capped strategy.
Optimizes portfolios with constraints and stochastic factors, deriving explicit solutions.
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…
Optimal online learning for joint pricing and resource allocation.
The Shapley value theory is used for risk allocation in non-orthogonal risk factors.
Mechanism designs for unknown agent values in stochastic bandit settings.
Study dynamic Pareto-optimal allocations in multi-period economies with time-consistent risk measures.
In this paper, we provide a representation theorem for dynamic capital allocation under It{ô}-L{é}vy model. We consider the representation of dynamic risk measures defined under Backward Stochastic Differential Equations (BSDE) with generators that grow quadratic-exponentially in the control variables. Dynamic capital …
This paper considers the design of optimal resource allocation policies in wireless communication systems which are generically modeled as a functional optimization problem with stochastic constraints. These optimization problems have the structure of a learning problem in which the statistical loss appears as a constr…
This paper explains CART random forests using stochastic control theory.
Under a Bayesian framework, we formulate the fully sequential sampling and selection decision in statistical ranking and selection as a stochastic control problem, and derive the associated Bellman equation. Using value function approximation, we derive an approximately optimal allocation policy. We show that this poli…
The aim of this paper is to compare two asset allocation methods for a pension scheme during the decumulation phase in the simplified portfolio selection between a risky asset following a geometric Brownian motion and a riskless asset. The two asset allocation criteria are the ruin probability of the insurance company …
Algorithm allocates budgets to tasks with semi-bandit feedback, achieving near-optimal regret bounds.
The paper analyzes how wealth affects investment strategies in incomplete markets.
The paper tackles online resource allocation with uncertain coefficients and chance constraints.
Scalar dynamic risk measures for univariate positions in continuous time are commonly represented as backward stochastic differential equations. In the multivariate setting, dynamic risk measures have been defined and studied as families of set-valued functionals in the recent literature. There are two possible extensi…
Reinforcement learning for continuous-time risk-sensitive asset allocation
We consider the problem of optimal budget allocation for crowdsourcing problems, allocating users to tasks to maximize our final confidence in the crowdsourced answers. Such an optimized worker assignment method allows us to boost the efficacy of any popular crowdsourcing estimation algorithm. We consider a mutual info…
This paper proposes a new clustering method based on Stochastic Dominance for asset allocation.
The paper extends game theory using Hodge theory on graphs.
High precision analytical approximation is proposed for variance-covariance based risk allocation in a portfolio of risky assets. A general case of a single-period multi-factor Merton-type model with stochastic recovery is considered. The accuracy of the approximation as well as its speed are compared to and shown to b…
New algorithm tackles resource allocation in multi-armed bandits to balance speed and throughput.
We study an asset allocation stochastic problem with restriction for a defined-contribution pension plan during the accumulation phase. We consider a financial market with stochastic interest rate, composed of a risk-free asset, a real zero coupon bond price, the inflation-linked bond and the risky asset. A plan member…
It is challenging to develop stochastic gradient based scalable inference for deep discrete latent variable models (LVMs), due to the difficulties in not only computing the gradients, but also adapting the step sizes to different latent factors and hidden layers. For the Poisson gamma belief network (PGBN), a recently …
EgalMAB solves fair resource allocation in stochastic bandits.
We consider the classical problem of sequential resource allocation where a decision maker must repeatedly divide a budget between several resources, each with diminishing returns. This can be recast as a specific stochastic optimization problem where the objective is to maximize the cumulative reward, or equivalently …
Low precision weights, activations, and gradients have been proposed as a way to improve the computational efficiency and memory footprint of deep neural networks. Recently, low precision networks have even shown to be more robust to adversarial attacks. However, typical implementations of low precision DNNs use unifor…
We introduce incremental variational inference and apply it to latent Dirichlet allocation (LDA). Incremental variational inference is inspired by incremental EM and provides an alternative to stochastic variational inference. Incremental LDA can process massive document collections, does not require to set a learning …
Overprocuring reserves can improve network efficiency by using excess reserves for congestion management.
Onflow optimizes portfolio allocation with gradient flows, robust to transaction fees.
New memory allocation scheme improves image generation performance.
The paper tackles resource allocation for arms with unknown and random rewards, achieving optimal regret bounds.
Optimal withdrawal strategy for DC pension plans maximizes total withdrawals while managing risk.
New framework for calculating multivariate risk measures using Wishart process.
Radio on Free Space Optics (RoFSO), as a universal platform for heterogeneous wireless services, is able to transmit multiple radio frequency signals at high rates in free space optical networks. This paper investigates the optimal design of power allocation for Wavelength Division Multiplexing (WDM) transmission in Ro…
New algorithm tackles unknown utility network resource allocation.
Unified framework for optimizing portfolios with distributions over weights, returns, and parameters.
Portfolio management problems are often divided into two types: active and passive, where the objective is to outperform and track a preselected benchmark, respectively. Here, we formulate and solve a dynamic asset allocation problem that combines these two objectives in a unified framework. We look to maximize the exp…
Modeling dynamic groundwater markets with price formation and trading strategies.
We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference. We used our algorithm to analyze a corpus of 1.2 million books (33 billion words) with thousands of topics. Our approach reduces the bias of variational infe…
A neural network approach solves optimal decumulation problems for pension plans.
Study dynamic asset allocation in incomplete markets using game theory and nonlocal BSDEs.
Latent Dirichlet allocation (LDA) is useful in document analysis, image processing, and many information systems; however, its generalization performance has been left unknown because it is a singular learning machine to which regular statistical theory can not be applied. Stochastic matrix factorization (SMF) is a res…