arXiv research

Study quantifies how LLMs capture higher-order statistical structure using cumulant expansion.

Bayesian methods improve inference for cumulative probit models on large datasets.

Neural networks can learn from higher-order cumulants efficiently, requiring quadratic samples.

CENNSurv models cumulative effects of time-dependent exposures on survival outcomes.

The paper identifies causal effects in latent variable models using higher-order cumulants.

Paper converts quantiles to cumulative distribution functions to simplify risk measures.

We introduce a new type of graphical model called a "cumulative distribution network" (CDN), which expresses a joint cumulative distribution as a product of local functions. Each local function can be viewed as providing evidence about possible orderings, or rankings, of variables. Interestingly, we find that the condi…

Paper proposes a new method to identify causal graphs with latent variables using higher-order cumulants.

The CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation, where the step size is adapted measuring the length of a so-called cumulative path. The cumulative path is a combination of the previous steps realized by the algorithm, where the importance of each step decreases with time. This article studies …

Using methods introduced by Scargle in 1978 we derive a cumulative version of the Lomb periodogram that exhibits frequency independent statistics when applied to cumulative noise. We show how this cumulative Lomb periodogram allows us to estimate the significance of log-periodic signatures in the S&P 500 anti-bubble th…

The paper revisits expected signatures in semimartingale models, providing new formulae and simplifying complexity.

Exponential dispersion model is a useful framework in machine learning and statistics. Primarily, thanks to the additive structure of the model, it can be achieved without difficulty to estimate parameters including mean. However, tight conditions on cumulant function, such as analyticity, strict convexity, and steepne…

New algorithms minimize simple and cumulative regret in contextual bandits.

Method determines credit transition matrix from cumulative default probabilities.

Develops regression trees for estimating cumulative incidence curves in competing risks.

Kernelized cumulants improve statistical analysis in high-dimensional spaces.

The paper calculates bounds for risk metrics and entropies under partial information constraints.

A new GAN loss function based on cumulant generating functions improves stability and robustness.

New method identifies causal structure in count data using cumulants and path analysis.

The problem of an arbitrary truncated Levy flight description using the method of cumulant approach has been solved. The set of cumulants of the truncated Levy distribution given the assumption of arbitrary truncation has been found. The influence of truncation shape on the truncated Levy flight properties in the Gauss…

We introduce three novel semi-parametric extensions of probabilistic canonical correlation analysis with identifiability guarantees. We consider moment matching techniques for estimation in these models. For that, by drawing explicit links between the new models and a discrete version of independent component analysis …

We consider a financial contract that delivers a single cash flow given by the terminal value of a cumulative gains process. The problem of modelling and pricing such an asset and associated derivatives is important, for example, in the determination of optimal insurance claims reserve policies, and in the pricing of r…

This research develops a dual-level reinforcement learning strategy to track daily VWAP accurately.

Diffusion models learn simple statistics before complex ones, revealing a sample complexity exponent.

We provide evidence that cumulative distributions of absolute normalized returns for the $100$ American companies with the highest market capitalization, uncover a critical behavior for different time scales $Δt$. Such cumulative distributions, in accordance with a variety of complex --and financial-- systems, can be m…

Bayesian approach for inhomogeneous Poisson process intensity estimation.

Study how neural networks learn from non-Gaussian data models.

Curiosity-Critic improves world model training by focusing on cumulative prediction error.

Bayesian algorithms minimize cumulative regret in decentralized multi-agent bandits.

New method identifies structural parameters without assuming uncorrelated errors.

In this article, inspired by Shi, et al. we investigate the optimal portfolio selection with one risk-free asset and one risky asset in a multiple period setting under cumulative prospect theory (CPT). Compared with their study, our novelty is that we consider a stochastic benchmark, and portfolio constraints. We test …

New method calibrates classifier probabilities with guaranteed coverage.

Directed acyclic graphs (DAGs) are a popular framework to express multivariate probability distributions. Acyclic directed mixed graphs (ADMGs) are generalizations of DAGs that can succinctly capture much richer sets of conditional independencies, and are especially useful in modeling the effects of latent variables im…

We show power-scaling behaviors for fluctuations in share volume, which no other studies have so far done. After analyzing a database of the daily transactions for all securities listed on the Tokyo Stock Exchange, we selected 1050 large companies that each had an unbroken series of daily trading activity from January …

Proposes a new sampling method for online learning with cumulative oversampling.

The paper tackles non-cumulative objectives in reinforcement learning and proposes modifications to existing algorithms.

Paper proposes compressive ICA algorithms for ICA model.

We investigate the problem of optimal dividend distribution for a company in the presence of regime shifts. We consider a company whose cumulative net revenues evolve as a Brownian motion with positive drift that is modulated by a finite state Markov chain, and model the discount rate as a deterministic function of the…

Proposes efficient estimators for weighted cumulative treatment effects in observational studies.

Paper tackles constrained bandit problems with a new learning framework.

Paper establishes no-regret property for practical EGO optimization.

Curriculum learning has been successfully used in reinforcement learning to accelerate the learning process, through knowledge transfer between tasks of increasing complexity. Critical tasks, in which suboptimal exploratory actions must be minimized, can benefit from curriculum learning, and its ability to shape explor…

We introduce the class of affine forward variance (AFV) models of which both the conventional Heston model and the rough Heston model are special cases. We show that AFV models can be characterized by the affine form of their cumulant generating function, which can be obtained as solution of a convolution Riccati equat…

Cumulant expansion is used to derive accurate closed-form approximation for Monthly Sum Options in case of constant volatility model. Payoff of Monthly Sum Option is based on sum of $N$ caped (and probably floored) returns. It is noticed, that $1/\sqrt{N}$ can be used as a small parameter in Edgeworth expansion. First …

This work extends reinforcement learning to handle non-cumulative objectives.

New method corrects bias in datasets using cumulative distribution functions.

SurvLIME-KS improves survival model explanations robustly.

The paper analyzes worst-case distortion risk metrics and weighted entropy under partial information.