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.
Paper tackles conditional expectation estimation using compactification operators.
FKEE estimates expectations without samples, using diffusion bridges and PINNs.
In this note we derive the backward (automatic) differentiation (adjoint [automatic] differentiation) for an algorithm containing a conditional expectation operator. As an example we consider the backward algorithm as it is used in Bermudan product valuation, but the method is applicable in full generality. The method …
Researchers approximate conditional expectation operators using kernel methods.
Adapts EGOP to multi-class setting and proposes a simple rough estimator.
We establish some subprincipal estimates for Berezin-Toeplitz operators on symplectic compact manifolds. From this, we construct a family of subprincipal symbol maps and we prove that these maps are the only ones satisfying some expected conditions.
We propose to learn a kernel-based message operator which takes as input all expectation propagation (EP) incoming messages to a factor node and produces an outgoing message. In ordinary EP, computing an outgoing message involves estimating a multivariate integral which may not have an analytic expression. Learning suc…
Extends expected value framework for cost-sensitive causal decision-making.
Study examines how risk tolerance impacts long-term investment returns.
The paper develops SGD for estimating operators from data.
Develops variance-reduced methods for solving generalized equations.
We introduce a statistical model for operational losses based on heavy-tailed distributions and bipartite graphs, which captures the event type and business line structure of operational risk data. The model explicitly takes into account the Pareto tails of losses and the heterogeneous dependence structures between the…
Neural network predicts electrochemical cell faults with 53% less error.
The expected utility operators introduced in a previous paper, offer a framework for a general risk aversion theory, in which risk is modelled by a fuzzy number . In this paper we formulate a coinsurance problem in the possibilistic setting defined by an expected utility operator . Some properties of the optimal …
Improves gradient estimation for discrete distributions with variance reduction techniques.
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…
DO-EM framework for quantum models improves generative tasks.
The article analyzes LCE in Hilbert space, deriving new formulas and regularisation methods.
We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output. This learned operator replaces the multivariate integral required in classical EP, which may not…
New method reduces sample complexity for robust reinforcement learning.
Study non-asymptotic estimation bounds for LTI models with Gaussian noise.
Efficiently predicts long-time dynamics of quantum spin models using MLP regression.
This paper introduces an intermediary between conditional expectation and conditional sublinear expectation, called R-conditioning. The R-conditioning of a random-vector in is defined as the best -estimate, given a -subalgebra and a degree of model uncertainty. When the random vector represents the payoff…
RP-WNO extends WNO with uncertainty quantification, useful for scientists and engineers.
Improved nested simulation for financial risk measurement.
Possibilistic risk theory starts from the hypothesis that risk is modelled by fuzzy numbers. In particular, in a possibilistic portfolio choice problem, the return of a risky asset will be a fuzzy number. The expected utility operators have been introduced in a previous paper to build an abstract theory of possibilisti…
We consider the problem of estimating the arithmetic average of a finite collection of real vectors stored in a distributed fashion across several compute nodes subject to a communication budget constraint. Our analysis does not rely on any statistical assumptions about the source of the vectors. This problem arises as…
A new method discovers equations from data using Bayesian and kernel techniques.
Manifold learning seeks a low dimensional representation that faithfully captures the essence of data. Current methods can successfully learn such representations, but do not provide a meaningful set of operations that are associated with the representation. Working towards operational representation learning, we endow…
A method for high-dimensional Bayesian optimization reduces dimensionality using EDR and Gaussian process.
A method makes particle filters differentiable without altering their forward pass.
Covariance is shown as a commutator in random variable calculus.
A new stochastic primal--dual algorithm for solving a composite optimization problem is proposed. It is assumed that all the functions/operators that enter the optimization problem are given as statistical expectations. These expectations are unknown but revealed across time through i.i.d. realizations. The proposed al…
In this paper, we present a method for the accurate estimation of the derivative (aka.~sensitivity) of expectations of functions involving an indicator function by combining a stochastic algorithmic differentiation and a regression. The method is an improvement of the approach presented in [Risk Magazine April 2018]. T…
Convolutional analysis operator learning (CAOL) enables the unsupervised training of (hierarchical) convolutional sparsifying operators or autoencoders from large datasets. One can use many training images for CAOL, but a precise understanding of the impact of doing so has remained an open question. This paper presents…
Study optimizes learning rates for conditional mean embedding estimates.
The hyperfinite -expectation is a nonstandard discrete analogue of -expectation (in the sense of Robinsonian nonstandard analysis). A lifting of a continuous-time -expectation operator is defined as a hyperfinite -expectation which is infinitely close, in the sense of nonstandard topology, to the continuous…
In portfolio optimization problems, the minimum expected investment risk is not always smaller than the expected minimal investment risk. That is, using a well-known approach from operations research, it is possible to derive a strategy that minimizes the expected investment risk, but this strategy does not always resu…
New algorithms reduce complexity for learning in MDPs with entropy regularization.
New unbiased gradient estimators for complex optimization problems.
Randomized algorithm solves vector-valued regression problems with low-rank operators.
A novel Bayesian computation method using importance weighting improves numerical stability and performance.
In this paper, we address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid; and the coefficients are governed by a …
New estimator for symmetric kernel expectations, robust to missing data.
The Kalman filter is the most powerful tool for estimation of the states of a linear Gaussian system. In addition, using this method, an expectation maximization algorithm can be used to estimate the parameters of the model. However, this algorithm cannot function in real time. Thus, we propose a new method that can be…
Paper proposes a new FRL algorithm for continuous sensitive attributes using EIPM.
Paper proposes a new estimator for nested expectations with faster convergence.
Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectations for one or more target functions - a computational pipeline which is inefficient when the target function(s) are known upfront. In this …