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.
New optimization method for portfolio management maximizing wealth and utility with risk control.
Stochastic Gradient Descent has been widely studied with classification accuracy as a performance measure. However, these stochastic algorithms cannot be directly used when non-decomposable pairwise performance measures are used such as Area under the ROC curve (AUC) which is a common performance metric when the classe…
Study aims to optimize financial investments by balancing risk and reward efficiently.
We prove that if a geodesic metric measure space satisfies a comparison condition for isoperimetric profile and if the observable variance is maximal, then the space is foliated by minimal geodesics, where the observable variance is defined to be the supremum of the variance of 1-Lipschitz functions on the space. Our r…
Designs efficient algorithms to maximize the expectation of Gaussian random variables.
The paper characterizes optimal dynamic portfolios for a modified mean-variance utility.
MaxVA improves Adam's step sizes by maximizing gradient variance.
Two derivations of PCA for distributional data.
Paper improves tree probability estimation using stochastic optimization and variance reduction.
msPCA solves sparse PCA for multiple components efficiently.
In markets for online advertising, some advertisers pay only when users respond to ads. So publishers estimate ad response rates and multiply by advertiser bids to estimate expected revenue for showing ads. Since these estimates may be inaccurate, the publisher risks not selecting the ad for each ad call that would max…
The paper solves an insurance problem using mean-variance and rank-dependent utility theory.
We address the problem of defining a group sparse formulation for Principal Components Analysis (PCA) - or its equivalent formulations as Low Rank approximation or Dictionary Learning problems - which achieves a compromise between maximizing the variance explained by the components and promoting sparsity of the loading…
We propose a Bayesian expectation-maximization (EM) algorithm for reconstructing Markov-tree sparse signals via belief propagation. The measurements follow an underdetermined linear model where the regression-coefficient vector is the sum of an unknown approximately sparse signal and a zero-mean white Gaussian noise wi…
EVA adapts LoRA for faster, more efficient fine-tuning.
Proposes -PCA to learn identifiable linear transformations without whitening.
This work tackles risk-sensitive deep RL by optimizing policies with variance constraints.
In this paper, we use replica analysis to determine the investment strategy that can maximize the net present value for portfolios containing multiple development projects. Replica analysis was developed in statistical mechanical informatics and econophysics to evaluate disordered systems, and here we use it to formula…
Active learning aims to train a classifier as fast as possible with as few labels as possible. The core element in virtually any active learning strategy is the criterion that measures the usefulness of the unlabeled data based on which new points to be labeled are picked. We propose a novel approach which we refer to …
This paper investigates optimal portfolio strategies in a financial market where the drift of the stock returns is driven by an unobserved Gaussian mean reverting process. Information on this process is obtained from observing stock returns and expert opinions. The latter provide at discrete time points an unbiased est…
It has often been stated that, within the class of continuous stochastic volatility models calibrated to vanillas, the price of a VIX future is maximized by the Dupire local volatility model. In this article we prove that this statement is incorrect: we build a continuous stochastic volatility model in which a VIX futu…
Optimizes reserve prices for first-price auctions to maximize revenue.
New method estimates latent gene expression factors without overlap with known confounders.
Optimal insurance contract limits insurer's risk exposure variance.
Given a multivariate data set, sparse principal component analysis (SPCA) aims to extract several linear combinations of the variables that together explain the variance in the data as much as possible, while controlling the number of nonzero loadings in these combinations. In this paper we consider 8 different optimiz…
Closed-form optimal portfolios for exponential utility in small/large markets.
Sparse linear (or generalized linear) models combine a standard likelihood function with a sparse prior on the unknown coefficients. These priors can conveniently be expressed as a maximization over zero-mean Gaussians with different variance hyperparameters. Standard MAP estimation (Type I) involves maximizing over bo…
A new method reduces variance in PG methods for RL, improving efficiency and convergence.
Boundary effects inflate variance in Gaussian processes, leading to acquisition bias.
We consider a market impact game for risk-averse agents that are competing in a market model with linear transient price impact and additional transaction costs. For both finite and infinite time horizons, the agents aim to minimize a mean-variance functional of their costs or to maximize the expected exponential u…
New approach to optimal dividend control with mean-variance criterion.
New method relaxes PCA orthogonality constraints using explained variance of correlated components.
Importance weighted variational inference (Burda et al., 2015) uses multiple i.i.d. samples to have a tighter variational lower bound. We believe a joint proposal has the potential of reducing the number of redundant samples, and introduce a hierarchical structure to induce correlation. The hope is that the proposals w…
Our paper improves uplift model evaluation on randomized controlled trials (RCT) data.
The lasso has been studied extensively as a tool for estimating the coefficient vector in the high-dimensional linear model; however, considerably less is known about estimating the error variance in this context. In this paper, we propose the natural lasso estimator for the error variance, which maximizes a penalized …
New method optimizes PCA for better prediction and variance.
Method estimates noise variance in Gaussian process regression.
The paper examines utility maximization in markets with hidden Gaussian drift, finding restrictions on model parameters.
cGAN learns a distribution for causal inference without specifying P.
Anchor PCA improves robustness in multi-domain PCA.
Reward models need more than just accuracy for effective RLHF.
Optimizes dividend payments to balance risk and reward.
We present a new algorithm, truncated variance reduction (TruVaR), that treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian processes in a unified fashion. The algorithm greedily shrinks a sum of truncated variances within a set of potential maximizers (BO) or unclassified points (LSE), which…
In this paper we estimate the mean-variance portfolio in the high-dimensional case using the recent results from the theory of random matrices. We construct a linear shrinkage estimator which is distribution-free and is optimal in the sense of maximizing with probability the asymptotic out-of-sample expected utilit…
Influence maximization (IM) is the problem of finding for a given a set of nodes in a network with maximum influence. With stochastic diffusion models, the influence of a set of seed nodes is defined as the expectation of its reachability over simulations, where each simulation specifies a det…
A new decentralized method solves minimax problems with reduced communication and sample complexity.
Given a training set with binary classification, the Support Vector Machine identifies the hyperplane maximizing the margin between the two classes of training data. This general formulation is useful in that it can be applied without regard to variance differences between the classes. Ignoring these differences is not…
The Kelly rule fails to maximize growth in a time-changed return setting.