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 method improves Robbins-Monro algorithm convergence with prior information.
The need for parameter estimation with massive datasets has reinvigorated interest in stochastic optimization and iterative estimation procedures. Stochastic approximations are at the forefront of this recent development as they yield procedures that are simple, general, and fast. However, standard stochastic approxima…
We formulate simple assumptions, implying the Robbins-Monro conditions for the -learning algorithm with the local learning rate, depending on the number of visits of a particular state-action pair (local clock) and the number of iteration (global clock). It is assumed that the Markov decision process is communicatin…
The paper analyzes two ISGD modes for statistical inference, deriving error bounds and confidence intervals.
Paper develops efficient methods for estimating Hessian inverses in stochastic optimization.
Improved analysis for fair federated learning reduces dependence on noise floor.
TCP provides well-calibrated prediction intervals for nonstationary time series.
Paper proves SHB convergence with biased gradients and approximate step sizes.
Though with progress, model learning and performing posterior inference still remains a common challenge for using deep generative models, especially for handling discrete hidden variables. This paper is mainly concerned with algorithms for learning Helmholz machines, which is characterized by pairing the generative mo…
Improved sampling efficiency with Random Reshuffling for Langevin dynamics.
Online (also called "recursive" or "adaptive") estimation of fixed model parameters in hidden Markov models is a topic of much interest in times series modelling. In this work, we propose an online parameter estimation algorithm that combines two key ideas. The first one, which is deeply rooted in the Expectation-Maxim…
Gonogo offers tools for sensitivity experiments in R.
Paper proposes a new algorithm to reduce derivative pricing computation time.
New streaming methods improve convergence rates for optimization problems.
Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) are two risk measures which are widely used in the practice of risk management. This paper deals with the problem of computing both VaR and CVaR using stochastic approximation (with decreasing steps): we propose a first Robbins-Monro procedure based on Rockaffela…
New method uses joint stochastic approximation to improve learning of discrete latent models.
Unified algorithm for stochastic optimization with time-varying momentum converges under general conditions.
Information geometry applies concepts in differential geometry to probability and statistics and is especially useful for parameter estimation in exponential families where parameters are known to lie on a Riemannian manifold. Connections between the geometric properties of the induced manifold and statistical properti…
Marginal maximum likelihood (MML) estimation is the preferred approach to fitting item response theory models in psychometrics due to the MML estimator's consistency, normality, and efficiency as the sample size tends to infinity. However, state-of-the-art MML estimation procedures such as the Metropolis-Hastings Robbi…