arXiv research

Paper calculates volatility distribution for cumulative production.

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…

This paper describes an agent-based model of interacting firms, in which interacting firm agents rationally invest capital and labor in order to maximize payoff. Both transactions and production are taken into account in this model. First, the performance of individual firms on a real transaction network was simulated.…

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…

We develop an algebraic framework for the description and analysis of financial behaviours, that is, behaviours that consist of transferring certain amounts of money at planned times. To a large extent, analysis of financial products amounts to analysis of such behaviours. We formalize the cumulative interest compliant…

One approach for constructing copula functions is by multiplication. Given that products of cumulative distribution functions (CDFs) are also CDFs, an adjustment to this multiplication will result in a copula model, as discussed by Liebscher (J Mult Analysis, 2008). Parameterizing models via products of CDFs has some a…

Improved product recommendations using deep learning.

Are large scale research programs that include many projects more productive than smaller ones with fewer projects? This problem of economy of scale is particularly relevant for understanding recent mergers in particular in the pharmaceutical industry. We present a quantitative theory based on the characterization of d…

A new pricing strategy maximizes revenue in high-dimensional product spaces with varying customer preferences.

Novel approach to contextual bandits using self-supervised learning.

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

This work studies the chord length distribution, in the case where both ends lie on a $N$-dimensional hypersphere ($N \geq 2$). Actually, after connecting this distribution to the recently estimated surface of a hyperspherical cap \cite{SLi11}, closed-form expressions of both the probability density function and the cu…

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…

A large number of online services provide automated recommendations to help users to navigate through a large collection of items. New items (products, videos, songs, advertisements) are suggested on the basis of the user's past history and --when available-- her demographic profile. Recommendations have to satisfy the…

Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc. This paper investigates modelling of ordinal data with Gaussian restricted Boltzmann machines (RBMs). In particular, we present the model architecture, learning and inference procedures for both vector-variate and …

Kernelized cumulants improve statistical analysis in high-dimensional spaces.

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

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

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

Proposes a curriculum learning algorithm to maximize cumulative return in reinforcement learning.

We analyze the cumulative distribution of total personal income of USA counties, and gross domestic product of Brazilian, German and United Kingdom counties, and also of world countries. We verify that generalized exponential distributions, related to nonextensive statistical mechanics, describe almost the whole spectr…

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

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…

New method learns stochastic thermodynamics from system currents.

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

HOFLON automates process start-ups and grade-changes using offline RL and online optimization.

A novel bandit problem with delayed arms, showing optimal strategies and lower bounds.

It is generally recognized that economical systems, and more in general complex systems, are characterized by power law distributions. Sometime, these distributions show a changing of the slope in the tail so that, more appropriately, they show a multi-power law behavior. We present a method to derive analytically a tw…

New algorithms minimize simple and cumulative regret in contextual bandits.

Method determines credit transition matrix from cumulative default probabilities.

New method calibrates classifier probabilities with guaranteed coverage.

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

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

IPO optimizes reinforcement learning with constraints for better performance.

A new model relaxes constraints on exponential dispersion models.

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

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

New algorithm reduces regret in dynamic assortment selection.

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.

Sandpile Economics explains how economies can be prone to large crises from small shocks.

New method corrects bias in datasets using cumulative distribution functions.

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

Develops a test for strict stationarity in stochastic processes.

New algorithm reduces regret with diverse contexts in bandits.

We consider online optimization in the 1-lookahead setting, where the objective does not decompose additively over the rounds of the online game. The resulting formulation enables us to deal with non-stationary and/or long-term constraints , which arise, for example, in online display advertising problems. We propose a…