A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groups

• URL: http://arxiv.org/abs/2205.15699v1
• Date: Tue, 31 May 2022 11:30:20 GMT
• Title: A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groups
• Abstract summary: We introduce a novel methodology to model rating transitions with a calibrated process. We show how Ito-SDEs in R will generate the desired model for rating transitions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce a novel methodology to model rating transitions with a stochastic process. To introduce stochastic processes, whose values are valid rating matrices, we noticed the geometric properties of stochastic matrices and its link to matrix Lie groups. We give a gentle introduction to this topic and demonstrate how It\^o-SDEs in R will generate the desired model for rating transitions. To calibrate the rating model to historical data, we use a Deep-Neural-Network (DNN) called TimeGAN to learn the features of a time series of historical rating matrices. Then, we use this DNN to generate synthetic rating transition matrices. Afterwards, we fit the moments of the generated rating matrices and the rating process at specific time points, which results in a good fit. After calibration, we discuss the quality of the calibrated rating transition process by examining some properties that a time series of rating matrices should satisfy, and we will see that this geometric approach works very well.

Related papers

• The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning [14.724143004818961]
  In this work, we identify intrinsic geometry as a key latent factor governing model effectiveness.<n>We introduce a curvature-stratified evaluation framework that partitions datasets into positive, negative, and near-zero curvature regimes.<n>We find that model rankings are highly stable within each curvature regime but shift significantly across regimes, indicating that performance is fundamentally geometry-dependent.
  arXiv  Detail & Related papers  (2026-06-04T17:05:36Z)
• Cost Trade-offs in Matrix Inversion Updates for Streaming Outlier Detection [0.479839492673697]
  This technical note aims to compare three different updating methods: Direct Inversion (DI), Iterative Sherman-Morrison (ISM), and Woodbury Matrix Identity (WMI)<n>We propose a simple, quantitative, and easy-to-remember rule that can be stated qualitatively as follows: ISM is optimal for rank-1 updates, WMI excels for small updates relative to matrix size, and DI is preferable otherwise.
  arXiv  Detail & Related papers  (2026-03-17T15:51:18Z)
• Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning [52.26396748560348]
  We provide an overview of high dimensional dynamical systems driven by random matrices.<n>We focus on applications to simple models of learning and generalization in machine learning theory.
  arXiv  Detail & Related papers  (2026-01-03T00:12:32Z)
• Measuring the stability and plasticity of recommender systems [0.4551615447454769]
  We propose a methodology to study how recommendation models behave when they are retrained.<n>The idea is to profile algorithms according to their ability to retain past patterns.<n>Preliminary results show different stability and plasticity profiles depending on the algorithmic technique.
  arXiv  Detail & Related papers  (2025-08-05T22:15:43Z)
• Best Transition Matrix Esitimation or Best Label Noise Robustness Classifier? Two Possible Methods to Enhance the Performance of T-revision [1.53744306569115]
  Label noise refers to incorrect labels in a dataset caused by human errors or collection defects. This report explores how to estimate noise transition matrices and construct deep learning classifiers that are robust against label noise.
  arXiv  Detail & Related papers  (2025-01-02T18:27:30Z)
• Attribute-to-Delete: Machine Unlearning via Datamodel Matching [65.13151619119782]
  Machine unlearning -- efficiently removing a small "forget set" training data on a pre-divertrained machine learning model -- has recently attracted interest. Recent research shows that machine unlearning techniques do not hold up in such a challenging setting.
  arXiv  Detail & Related papers  (2024-10-30T17:20:10Z)
• MUSO: Achieving Exact Machine Unlearning in Over-Parameterized Regimes [19.664090734076712]
  Machine unlearning (MU) makes a well-trained model behave as if it had never been trained on specific data. We propose an alternating optimization algorithm that unifies the tasks of unlearning and relabeling. The algorithm's effectiveness, confirmed through numerical experiments, highlights its superior performance in unlearning across various scenarios.
  arXiv  Detail & Related papers  (2024-10-11T06:17:17Z)
• Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning [18.688802981093644]
  We propose a new utilization method based on resampling, coined RENT. Rent consistently outperforms existing transition matrix utilization methods, which includes reweighting, on various benchmark datasets.
  arXiv  Detail & Related papers  (2024-03-05T06:20:49Z)
• Dynamic Collaborative Filtering for Matrix- and Tensor-based Recommender Systems [5.1148288291550505]
  We introduce a novel collaborative filtering model for sequential problems known as TIRecA. TIRecA efficiently updates its parameters using only the new data segment, allowing incremental addition of new users and items to the recommender system. Our comparison with general matrix and tensor-based baselines in terms of prediction quality and computational time reveals that TIRecA achieves comparable quality to the baseline methods, while being 10-20 times faster in training time.
  arXiv  Detail & Related papers  (2023-12-04T20:45:51Z)
• Learning Evaluation Models from Large Language Models for Sequence Generation [61.8421748792555]
  We propose a three-stage evaluation model training method that utilizes large language models to generate labeled data for model-based metric development. Experimental results on the SummEval benchmark demonstrate that CSEM can effectively train an evaluation model without human-labeled data.
  arXiv  Detail & Related papers  (2023-08-08T16:41:16Z)
• Consensus-Adaptive RANSAC [104.87576373187426]
  We propose a new RANSAC framework that learns to explore the parameter space by considering the residuals seen so far via a novel attention layer. The attention mechanism operates on a batch of point-to-model residuals, and updates a per-point estimation state to take into account the consensus found through a lightweight one-step transformer.
  arXiv  Detail & Related papers  (2023-07-26T08:25:46Z)
• The Ordered Matrix Dirichlet for Modeling Ordinal Dynamics [54.96229007229786]
  We propose the Ordered Matrix Dirichlet (OMD) to map latent states to observed action types. Models built on the OMD recover interpretable latent states and show superior forecasting performance in few-shot settings.
  arXiv  Detail & Related papers  (2022-12-08T08:04:26Z)
• Rating Triggers for Collateral-Inclusive XVA via Machine Learning and SDEs on Lie Groups [0.0]
  We model the rating process of an entity by using a geometrical approach. We show how the classical Girsanov theorem can be applied in the Lie group setting.
  arXiv  Detail & Related papers  (2022-11-01T08:26:25Z)
• Towards Similarity-Aware Time-Series Classification [51.2400839966489]
  We study time-series classification (TSC), a fundamental task of time-series data mining. We propose Similarity-Aware Time-Series Classification (SimTSC), a framework that models similarity information with graph neural networks (GNNs)
  arXiv  Detail & Related papers  (2022-01-05T02:14:57Z)
• Multimodal Meta-Learning for Time Series Regression [3.135152720206844]
  We will explore the idea of using meta-learning for quickly adapting model parameters to new short-history time series. We show empirically that our proposed meta-learning method learns TSR with few data fast and outperforms the baselines in 9 of 12 experiments.
  arXiv  Detail & Related papers  (2021-08-05T20:50:18Z)
• Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modelling and Automation [68.8204255655161]
  A Bayesian warping technique has been proposed to reshape modeled surfaces based on geochemical and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning in this warping framework to make the likelihood generalizable. Its foundation is laid by a Bayesian computation in which the geological domain likelihood given the chemistry, p(g|c) plays a similar role to p(y(c)|g.
  arXiv  Detail & Related papers  (2021-02-15T10:37:52Z)
• Matrix Smoothing: A Regularization for DNN with Transition Matrix under Noisy Labels [54.585681272543056]
  Training deep neural networks (DNNs) in the presence of noisy labels is an important and challenging task. Recent probabilistic methods directly apply transition matrix to DNN, neglect DNN's susceptibility to overfitting. We propose a novel method, in which a smoothed transition matrix is used for updating DNN, to restrict the overfitting.
  arXiv  Detail & Related papers  (2020-03-26T13:49:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.