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.
SAA method solves insurance portfolio optimization with CVaR constraints.
Novel approach simplifies VI problems with faster performance.
VISA improves inference efficiency for complex models.
We explore the performance of sample average approximation in comparison with several other methods for stochastic optimization when there is information available on the underlying true probability distribution. The methods we evaluate are (a) bagging; (b) kernel smoothing; (c) maximum likelihood estimation (MLE); and…
The paper improves Monte Carlo methods for optimization problems.
The paper studies the convergence of SAA for systemic risk measures.
In this paper, we study a class of stochastic optimization problems, referred to as the \emph{Conditional Stochastic Optimization} (CSO), in the form of $\min_{x \in \mathcal{X}} \EE_ξf_ξ\Big({\EE_{η|ξ}[g_η(x,ξ)]}\Big)$, which finds a wide spectrum of applications including portfolio selection, reinforcement learning, …
New research shows SAA can outperform SA for Wasserstein barycenters.
Study integrates machine learning with SAA for optimizing decisions based on uncertain parameters and covariates.
New method approximates CVaR with less data for heavy-tailed risks.
Paper introduces DOO models to outperform SAA out-of-sample.
In this work, we propose a smart idea to couple importance sampling and Multilevel Monte Carlo (MLMC). We advocate a per level approach with as many importance sampling parameters as the number of levels, which enables us to compute the different levels independently. The search for parameters is carried out using samp…
Let F be a family of Borel measurable functions on a complete separable metric space. The gap (or fat-shattering) dimension of F is a combinatorial quantity that measures the extent to which functions f in F can separate finite sets of points at a predefined resolution gamma > 0. We establish a connection between the g…
Improved stochastic optimization outperforms standard methods.
This paper introduces sample-averaged Q-learning for better RL performance.
Adaptive importance sampling techniques are widely known for the Gaussian setting of Brownian driven diffusions. In this work, we want to extend them to jump processes. Our approach relies on a change of the jump intensity combined with the standard exponential tilting for the Brownian motion. The free parameters of ou…
Counterexamples show failure of uniform laws of large numbers for subdifferentials.
In this work, we propose an algorithm to price American options by directly solving the dual minimization problem introduced by Rogers. Our approach relies on approximating the set of uniformly square integrable martingales by a finite dimensional Wiener chaos expansion. Then, we use a sample average approximation tech…
The paper analyzes risk estimation methods and derives bounds for OCE risk.
This paper tackles constrained statistical learning problems by proposing a new approach.
New findings show ETO outperforms IEO in well-specified models with sufficient data.
This paper is devoted to the prediction of solutions to a stochastic discrete optimization problem. Through an application, we illustrate how we can use a state-of-the-art neural machine translation (NMT) algorithm to predict the solutions by defining appropriate vocabularies, syntaxes and constraints. We attend to app…
Robust algorithm for distributed optimization resistant to Byzantine failures.
We propose an computational framework for real-time risk assessment and prioritizing for random outcomes without prior information on probability distributions. The basic model is built based on satisficing measure (SM) which yields a single index for risk comparison. Since SM is a dual representation for a family of r…
This paper introduces time-uniform CLT-based confidence intervals for statistical inference.
Two signature-based methods solve optimal stopping in non-Markovian frameworks.
Neural networks approximate random utility models for choice prediction.
This study analyzes decision-making in diverse environments where past data may not predict future outcomes.
Adaptive SAA solves large-scale stochastic linear programs efficiently.
SIM-Shapley improves SV approximation efficiency and stability.
We investigate the accuracy of the two most common estimators for the maximum expected value of a general set of random variables: a generalization of the maximum sample average, and cross validation. No unbiased estimator exists and we show that it is non-trivial to select a good estimator without knowledge about the …
We propose a stochastic approximation method for approximating the efficient frontier of chance-constrained nonlinear programs. Our approach is based on a bi-objective viewpoint of chance-constrained programs that seeks solutions on the efficient frontier of optimal objective value versus risk of constraint violation. …
The method to derive uniform bounds with Gaussian and Rademacher complexities is extended to the case where the sample average is replaced by a nonlinear statistic. Tight bounds are obtained for U-statistics, smoothened L-statistics and error functionals of l2-regularized algorithms.
Distributionally robust optimization (DRO) problems are increasingly seen as a viable method to train machine learning models for improved model generalization. These min-max formulations, however, are more difficult to solve. We therefore provide a new stochastic gradient descent algorithm to efficiently solve this DR…
Paper improves TD(0) convergence rate with LFA, i.i.d. samples, and averaging.
DADVI improves ADVI by using deterministic approximation for faster, more accurate posterior estimation.
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. We introduce BoTorch, a modern programming framework for Bayesian optimization that combines Monte-Carlo (MC) acquisition functions,…
A new method calibrates scientific models by adding randomness to their predictions.
A new method uses GANs for robust optimization under uncertain data.
The paper proposes a method to infer Q-values online with Q-Learning.
We study statistical inference and distributionally robust solution methods for stochastic optimization problems, focusing on confidence intervals for optimal values and solutions that achieve exact coverage asymptotically. We develop a generalized empirical likelihood framework---based on distributional uncertainty se…
We consider distributed convex optimization problems originated from sample average approximation of stochastic optimization, or empirical risk minimization in machine learning. We assume that each machine in the distributed computing system has access to a local empirical loss function, constructed with i.i.d. data sa…
This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict expected tactical descriptions of operational solutions (TDOSs). The problem we address occurs in the context of two-stage stochastic programming where the…
The paper explains how importance sampling can be used for optimization of rare events.
Financial markets are prominent examples for highly non-stationary systems. Sample averaged observables such as variances and correlation coefficients strongly depend on the time window in which they are evaluated. This implies severe limitations for approaches in the spirit of standard equilibrium statistical mechanic…
Improved Thompson Sampling for Bayesian Optimization.
New optimization method corrects data-driven optimizer's curse.
This paper presents a new approach, called perturb-max, for high-dimensional statistical inference that is based on applying random perturbations followed by optimization. This framework injects randomness to maximum a-posteriori (MAP) predictors by randomly perturbing the potential function for the input. A classic re…