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.
Mack's estimator improves chain ladder prediction for large exposure insurance models.
A method for analysing the risk of taking a too low reserve level by use of Chain Ladder method is developed. We give an answer to the question of how much safety loading in terms of the Chain Ladder standard error has to be added to the Chain Ladder reserve in order to reach a specified security level in loss reservin…
Chain-ladder reserving is sensitive to outliers, leading to unreliable estimates.
New method simplifies individual claims reserving.
Paper proposes robust methods to detect and treat outliers in multivariate loss reserving.
New method for individual claims reserving using machine learning.
The intention of this paper is to estimate a Bayesian distribution-free chain ladder (DFCL) model using approximate Bayesian computation (ABC) methodology. We demonstrate how to estimate quantities of interest in claims reserving and compare the estimates to those obtained from classical and credibility approaches. In …
Characterizes quasiconformally homogeneous ladder surfaces.
We examine spaces of connected tri-/univalent graphs subject to local relations which are motivated by the theory of Vassiliev invariants. It is shown that the behaviour of ladder-like subgraphs is strongly related to the parity of the number of rungs: there are similar relations for ladders of even and odd lengths, re…
Study gluing of Lorentzian length spaces and their causal ladder properties.
Talked about the Veech group of a specific surface.
We used the Ladder Network [Rasmus et al. (2015)] to perform Hyperspectral Image Classification in a semi-supervised setting. The Ladder Network distinguishes itself from other semi-supervised methods by jointly optimizing a supervised and unsupervised cost. In many settings this has proven to be more successful than o…
We classify all groups which can occur as the orientation preserving topological symmetry group of some embedding of a Möbius ladder graph in .
Parallel transport is an important step in many discrete algorithms for statistical computing on manifolds. Numerical methods based on Jacobi fields or geodesics parallelograms are currently used in geometric data processing. In this last class, pole ladder is a simplification of Schild's ladder for the parallel transp…
Semi-supervised learning (SSL) partially circumvents the high cost of labeling data by augmenting a small labeled dataset with a large and relatively cheap unlabeled dataset drawn from the same distribution. This paper offers a novel interpretation of two deep learning-based SSL approaches, ladder networks and virtual …
New ladder methods improve numerical accuracy in parallel transport on manifolds.
The paper finds pseudo-Anosov-like maps on an infinite ladder surface.
The goal of this paper is to deal with a data scarcity scenario where deep learning techniques use to fail. We compare the use of two well established techniques, Restricted Boltzmann Machines and Variational Auto-encoders, as generative models in order to increase the training set in a classification framework. Essent…
Polynomial mixing times for simulated tempering in mixture sampling problems.
The full causal ladder of spacetimes is constructed, and their updated main properties are developed. Old concepts and alternative definitions of each level of the ladder are revisited, with emphasis in minimum hypotheses. The implications of the recently solved ``folk questions on smoothability'', and alternative prop…
Method solves Gaussian graphical models on ladder graphs efficiently.
We propose a recurrent extension of the Ladder networks whose structure is motivated by the inference required in hierarchical latent variable models. We demonstrate that the recurrent Ladder is able to handle a wide variety of complex learning tasks that benefit from iterative inference and temporal modeling. The arch…
This paper introduces a new market making approach using scaled beta distributions.
New causal distances improve evaluation of causal discovery algorithms.
Variational Autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models. We propose a new inference model, the Ladder Variational Autoencoder, that…
The article derives a formula for predicting claims uncertainty using the GCC method.
We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on the Ladder network proposed by Valpola (…
We prove that the HOMFLYPT polynomial of a link, colored by partitions with a fixed number of rows is a -holonomic function. Specializing to the case of knots colored by a partition with a single row, it proves the existence of an super-polynomial of knots in 3-space, as was conjectured by string theorists. …
Over the past half-century, the empirical finance community has produced vast literature on the advantages of the equally weighted S\&P 500 portfolio as well as the often overlooked disadvantages of the market capitalization weighted Standard and Poor's (S\&P 500) portfolio (see \cite{Bloom}, \cite{Uppal}, \cite{Jacobs…
The leaderboard in machine learning competitions is a tool to show the performance of various participants and to compare them. However, the leaderboard quickly becomes no longer accurate, due to hack or overfitting. This article gives two pieces of advice to prevent easy hack or overfitting. By following these advice,…
The use of unsupervised data in addition to supervised data in training discriminative neural networks has improved the performance of this clas- sification scheme. However, the best results were achieved with a training process that is divided in two parts: first an unsupervised pre-training step is done for initializ…
This work defines a new function space for multi-layer neural networks.
DHRL learns interpretable features from visual data.
We describe the geometrical ladder of equations for Abelian bundles and gerbes, as well as higher generalisations, in terms of the cohomology of an operator that combines de Rham and Cech cohomology.
We present the Video Ladder Network (VLN) for efficiently generating future video frames. VLN is a neural encoder-decoder model augmented at all layers by both recurrent and feedforward lateral connections. At each layer, these connections form a lateral recurrent residual block, where the feedforward connection repres…
The paper explores Finsler-type objects and their variational problems on spacetimes.
Transformers learn to integrate information from past positions incrementally, specializing heads in distinct patterns.
The paper introduces a test to distinguish spatial graphs based on their knot diagrams.
In clustering we normally output one cluster variable for each datapoint. However it is not necessarily the case that there is only one way to partition a given dataset into cluster components. For example, one could cluster objects by their colour, or by their type. Different attributes form a hierarchy, and we could …
Paper proposes a new reserving model using machine learning techniques.
This paper finds minimal sets of generators for mapping class groups of specific surfaces.
LLMs help automate extraction of actuarial variables from unstructured claims data.
The paper proposes an original methodology for constructing quantitative statistical models based on multidimensional distribution functions constructed on the basis of the insurance companies' data on inshurance policies (including policies with deductible) and claims incurred. Real data of some Russian insurance comp…
New theory explains contrastive learning via overlapping augmented views.
Novel power transform unifies various mathematical functions.
We show that the minimum number of sticks required to construct a non-paneled knotless embedding of is 9 and of is 12 or 13. We use our results about to show that the probability that a random linear embedding of in a cube is in the form of a Möbius ladder is , and offer …
CGDL improves open set recognition by learning conditional Gaussian distributions.
A new algorithm for parallel transport on shape spaces is presented and compared to existing methods.