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.
New algorithms reduce regret for convex bandits with small comparator norms.
The study optimizes bounds for comparing training and population loss.
Interactive DR framework for comparing datasets.
This paper describes a new online convex optimization method which incorporates a family of candidate dynamical models and establishes novel tracking regret bounds that scale with the comparator's deviation from the best dynamical model in this family. Previous online optimization methods are designed to have a total a…
New algorithm reduces dynamic regret by adapting to comparator complexity.
In this report we describe a tool for comparing the performance of graphical causal structure learning algorithms implemented in the TETRAD freeware suite of causal analysis methods. Currently the tool is available as package in the TETRAD source code (written in Java). Simulations can be done varying the number of run…
Study compares market microstructure between two South African exchanges.
A new method compares unaligned datasets using log-Euclidean signatures of SPD matrices.
New algorithms adapt to both gradient norms and comparator norms in online learning.
Study compares LLMs vs classical models for financial sentiment analysis.
Parallel sentences are a relatively scarce but extremely useful resource for many applications including cross-lingual retrieval and statistical machine translation. This research explores our methodology for mining such data from previously obtained comparable corpora. The task is highly practical since non-parallel m…
In this article, we prove a theorem comparing the dihedral angles of simplices in the hyperbolic, spherical and Euclidean geometries.
We consider online learning with linear models, where the algorithm predicts on sequentially revealed instances (feature vectors), and is compared against the best linear function (comparator) in hindsight. Popular algorithms in this framework, such as Online Gradient Descent (OGD), have parameters (learning rates), wh…
New fairness notion helps identify fair auditors for evaluating decision-support systems.
Researchers describe and compare decompositions of Poincaré duality pairs.
MSA compares neural representations' intrinsic geometry for better understanding.
Study compares thimbles to Morse theory on Lie theory models.
Comparative statistical properties of Parkinson, Garman-Klass, Roger-Satchell and bridge oscillation estimators are discussed. Point and interval estimations, related with mentioned estimators are considered
Study compares machine learning models and BERT on SQuAD dataset.
Formula compares metrics on branched coverings of line bundles.
The ability to represent and compare machine learning models is crucial in order to quantify subtle model changes, evaluate generative models, and gather insights on neural network architectures. Existing techniques for comparing data distributions focus on global data properties such as mean and covariance; in that se…
Algorithm compares Legendrian knots efficiently in some cases.
Proposes a score to compare rule-based algorithms' interpretability.
This paper considers extractive summarisation in a comparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups. We formulate a set of new object…
Scientists have used many different classification methods to solve the problem of music classification. But the efficiency of each classification is different. In this paper, we propose two compared methods on the task of music style classification. More specifically, feature extraction for representing timbral textur…
New framework compares credal sets for hypothesis testing with epistemic uncertainty.
New metric compares noisy neural trajectories using optimal transport.
Study uses LLM to extract and compare segment disclosures from financial filings.
In machine learning, a nonparametric forecasting algorithm for time series data has been proposed, called the kernel spectral hidden Markov model (KSHMM). In this paper, we propose a technique for short-term wind-speed prediction based on KSHMM. We numerically compared the performance of our KSHMM-based forecasting tec…
Comparative learning combines realizable and agnostic settings for two hypothesis classes, reducing sample complexity.
ModelDiff compares learning algorithms by identifying feature transformations.
Study compares LSTM and ANN architectures for forex prediction, finding ANN more efficient.
Inference of hidden classes in stochastic block model is a classical problem with important applications. Most commonly used methods for this problem involve naïve mean field approaches or heuristic spectral methods. Recently, belief propagation was proposed for this problem. In this contribution we perform a comparati…
Paper finds algorithms with both low regret and high exploitation.
Algorithm provides online learning guarantees against general comparators in full and bandit feedback.
We introduce and compare new variability measures based on risk quantiles.
Parallel sentences are a relatively scarce but extremely useful resource for many applications including cross-lingual retrieval and statistical machine translation. This research explores our new methodologies for mining such data from previously obtained comparable corpora. The task is highly practical since non-para…
Paper presents a faster method for computing cost of equity and performing comparable company analysis.
A general definition of a bimodule connection in noncommutative geometry has been recently proposed. For a given algebra this definition is compared with the ordinary definition of a connection on a left module over the associated enveloping algebra. The corresponding curvatures are also compared.
Conditional forecasts of risk measures play an important role in internal risk management of financial institutions as well as in regulatory capital calculations. In order to assess forecasting performance of a risk measurement procedure, risk measure forecasts are compared to the realized financial losses over a perio…
Proposes GDTW for aligning time series on different, incomparable spaces.
We analyse an issue when comparing survival curves between two subgroups. We show that there is a direct relationship between estimates of subgroups' survival at a time point and positive and negative predictive values in the binary classification settings. Our findings present a case where current methods of comparing…
Approach selects variables and time intervals for comparing high-dimensional time-series data.
Classifiers are among the most widely used supervised machine learning algorithms. Many classification models exist, and choosing the right one for a given task is difficult. During model selection and debugging, data scientists need to assess classifiers' performances, evaluate their learning behavior over time, and c…
Deep learning models compare performance on Limit Order Book tasks.
This study compares the largest claims from two insurance portfolios using stochastic orderings.
We compare the flat geometry associated to a quadratic differential with the hyperbolic geometry associated to the underlying Riemann surface. We show that if a curve is contained in a thick subsurface, then its hyperbolic length is comparable to its flat length times the flat size of the subsurface.
As machine learning (ML) models, trained on real-world datasets, become common practice, it is critical to measure and quantify their potential biases. In this paper, we focus on renal failure and compare a commonly used traditional risk score, Tangri, with a more powerful machine learning model, which has access to a …