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.
Analyzes premium data of Indian non-life insurers, finding GEV distribution best fits Lognormal and GEV extremes.
CNN improves neutrino event reconstruction in IceCube DeepCore.
Paper introduces SPADE method to protect classifiers from OOD and adversarial samples.
We study constrained nonconvex optimization problems in machine learning, signal processing, and stochastic control. It is well-known that these problems can be rewritten to a minimax problem in a Lagrangian form. However, due to the lack of convexity, their landscape is not well understood and how to find the stable e…
This paper presents a cross-country comparison of significant predictors of small business failure between Italy and the UK. Financial measures of profitability, leverage, coverage, liquidity, scale and non-financial information are explored, some commonalities and differences are highlighted. Several models are consid…
Efficiently estimates GEV distribution parameters using neural networks.
Alt-GNNs improve travel mode choice modeling by integrating graph neural networks with GEV models.
Classification tasks usually assume that all possible classes are present during the training phase. This is restrictive if the algorithm is used over a long time and possibly encounters samples from unknown classes. The recently introduced extreme value machine, a classifier motivated by extreme value theory, addresse…
Recent literature on deep neural networks for tagging of highly energetic jets resulting from top quark decays has focused on image based techniques or multivariate approaches using high-level jet substructure variables. Here, a sequential approach to this task is taken by using an ordered sequence of jet constituents …
New methods model gamma-ray data to better understand Galactic emissions.
Paper proposes a statistical model for detecting mu-suppression in EEG signals.
ALICE uses ML for particle identification across a wide momentum range.
We study a stylized dynamic assortment planning problem during a selling season of finite length . At each time period, the seller offers an arriving customer an assortment of substitutable products and the customer makes the purchase among offered products according to a discrete choice model. The goal of the selle…
SHIFT improves robustness in estimating dose-response functions with heavy-tailed contamination.
A flexible machine learning model infers the morphology of the Galactic Center Excess.