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.
A cased-based reasoning method predicts rare events on strategic sites using satellite imagery.
Novel financial time-series data representation improves industry sector classification.
A new explainable CBR system predicts financial risks with interpretability and good performance.
Paper develops a new similarity metric for predicting stock market returns.
New methods for explaining Random Forest predictions using case-based reasoning.
Study proposes a data-driven CBR system for improved bankruptcy prediction.
We present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. BCM brings the intuitive power of CBR to a Bayesian generative framework. The BCM learns prototypes, the "quintessential" observations that best represent clusters in a data…
In many contexts, it can be useful for domain experts to understand to what extent predictions made by a machine learning model can be trusted. In particular, estimates of trustworthiness can be useful for fraud analysts who process machine learning-generated alerts of fraudulent transactions. In this work, we present …
In recent years, huge amounts of unstructured textual data on the Internet are a big difficulty for AI algorithms to provide the best recommendations for users and their search queries. Since the Internet became widespread, a lot of research has been done in the field of Natural Language Processing (NLP) and machine le…
Introduction. Case Based Reasoning (CBR) is an emerg- ing decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of solved cases is provided, and every case is described through a set of attributes (inputs) and a label (output). Extracting…
Doctors often rely on their past experience in order to diagnose patients. For a doctor with enough experience, almost every patient would have similarities to key cases seen in the past, and each new patient could be viewed as a mixture of these key past cases. Because doctors often tend to reason this way, an efficie…
Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save fresh water. To finance this project, Conceptual cost models are important to accurately predict preliminary costs at the early stages of the project. The first step is to develop a conceptual cost model to identify key cost…
ALPODS AI diagnoses high-dimensional biomedical data with human-understandable explanations.
CBNNs model survival with time-varying interactions, outperforming other methods.
Statistical tests for fairness in admissions data reveal hidden patterns.
Despite the widespread usage of machine learning throughout organizations, there are some key principles that are commonly missed. In particular: 1) There are at least four main families for supervised learning: logical modeling methods, linear combination methods, case-based reasoning methods, and iterative summarizat…
Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability -- they are particularly difficult to understand because of their non-linear nature. As a result, neural networks are often treated as "black box" models, and in the past, have been trained purely to …
Binary classification is one of the most common problem in machine learning. It consists in predicting whether a given element belongs to a particular class. In this paper, a new algorithm for binary classification is proposed using a hypergraph representation. The method is agnostic to data representation, can work wi…
For an affine two factor model, we study the asymptotic properties of the maximum likelihood and least squares estimators of some appearing parameters in the so-called subcritical (ergodic) case based on continuous time observations. We prove strong consistency and asymptotic normality of the estimators in question.
Paper introduces Native Guide for generating time series counterfactual explanations.
Survey on principles and challenges of interpretable machine learning.
Study LCP structures on solvmanifolds, complete list up to 5 dimensions.
Topological quantum computation with Fibonacci anyons relies on the possibility of efficiently generating unitary transformations upon pseudoparticles braiding. The crucial fact that such set of braids has a dense image in the unitary operations space is well known; in addition, the Solovay-Kitaev algorithm allows to a…
Discretizes Hodge-Dirac operators on a torus.
Study non-convex matrix factorization using Riemannian geometry.
Paper defines a new invariant for surface immersions.
One of the major challenges in machine learning nowadays is to provide predictions with not only high accuracy but also user-friendly explanations. Although in recent years we have witnessed increasingly popular use of deep neural networks for sequence modeling, it is still challenging to explain the rationales behind …
Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or set of cases most similar to the query case. Describing a similarity measure analytically is challenging, even for domain exp…
The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D image data from particle physics experiments to classify signal from background…
In Statistical Learning, the Vapnik-Chervonenkis (VC) dimension is an important combinatorial property of classifiers. To our knowledge, no theoretical results yet exist for the VC dimension of edited nearest-neighbour (1NN) classifiers with reference set of fixed size. Related theoretical results are scattered in the …
Generative Adversarial Network (GAN) is a current focal point of research. The body of knowledge is fragmented, leading to a trial-error method while selecting an appropriate GAN for a given scenario. We provide a comprehensive summary of the evolution of GANs starting from its inception addressing issues like mode col…
Study -Einstein Sasakian structures on Lie algebras, dividing cases based on center dimension.
Proposes a deep hedging method for robust pricing and hedging under parameter uncertainty.
Uniform proof of -injectivity for certain maps in low dimensions.
Unified framework for Bayes-optimal classifiers under group fairness.
LaTRO optimizes latent reasoning in LLMs without external reward.
Auto-CEI improves LLM reasoning by balancing assertiveness and conservativeness.
A framework isolates VQA reasoning from perception for better model evaluation.
A new method for math reasoning that allows for iterative correction.
Proposes adjusting neural network errors for time series forecasting.
This paper optimizes Bayesian estimation for log-concave models using Langevin Monte-Carlo.
Inferring new facts from existing knowledge graphs (KG) with explainable reasoning processes is a significant problem and has received much attention recently. However, few studies have focused on relation types unseen in the original KG, given only one or a few instances for training. To bridge this gap, we propose Co…
Transformers learn multi-step reasoning through gradient descent.
Transformers with CoT don't enhance reasoning power across all tasks.
Early stopping methods reduce unnecessary reasoning steps in LLMs by monitoring uncertainty signals.
Achieving artificial visual reasoning - the ability to answer image-related questions which require a multi-step, high-level process - is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, structured reasoning process over images from language. Stand…
FinTradeBench benchmarks LLMs for financial reasoning combining company fundamentals and market signals.
Study Heisenberg homology on surface configurations, revealing new representations of mapping class groups.