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 new method detects interactions in machine learning models.
Explains how machine learning models can be biased and presents interactive plots to visualize bias.
Proposes ICE-based metric for better understanding interactions in black-box models.
CDPs visualize causal dependencies in AI models.
This paper describes structuring data and constructing plots to explore forest classification models interactively. A forest classifier is an example of an ensemble, produced by bagging multiple trees. The process of bagging and combining results from multiple trees, produces numerous diagnostics which, with interactiv…
We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and regression, which yields a good trade-off between robustness (low variance) and adaptiveness (low bias). Direct interpretation of a RF model…
Characterizing the dynamic interactive patterns of complex systems helps gain in-depth understanding of how components interrelate with each other while performing certain functions as a whole. In this study, we present a novel multimodal data fusion approach to construct a complex network, which models the interaction…
Unified local and global explanations through functional decomposition of low dimensional structures.
We present a new package in R implementing Bayesian additive regression trees (BART). The package introduces many new features for data analysis using BART such as variable selection, interaction detection, model diagnostic plots, incorporation of missing data and the ability to save trees for future prediction. It is …
Automate residual plot assessment with R package and Shiny application
Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. f…
This research optimizes Andrews plots for better visual clarity in high-dimensional data.
Model-agnostic interpretation methods can mislead if not used carefully.
A new tree-based estimator, FastPD, efficiently estimates PD functions for machine learning models.
Proposes a new method for interpreting feature importance and effects in dependent feature models.
Automates selection and visualization of model responses in various directions.
3D filament plots visualize curves in datasets, avoiding visual clutter.
GADGET framework decomposes global feature effects using recursive partitioning.
One aim of data mining is the identification of interesting structures in data. For better analytical results, the basic properties of an empirical distribution, such as skewness and eventual clipping, i.e. hard limits in value ranges, need to be assessed. Of particular interest is the question of whether the data orig…
Used to investigate the presence of distinctive recurrent behaviours in natural processes, the recurrence plots can be applied to the analysis of economic data, and, in particular, to the characterization of exchange rates of currencies too. In this paper, we will show that these plots are able to characterize the peri…
Pareto distributions, and power laws in general, have demonstrated to be very useful models to describe very different phenomena, from physics to finance. In recent years, the econophysical literature has proposed a large amount of papers and models justifying the presence of power laws in economic data. Most of the ti…
Paper exposes vulnerabilities in interpreting machine learning models using adversarial attacks on PD plots.
Introduces PIT-plot for prioritizing projects based on their impact.
Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques t…
PLOT uses optimal transport to find neural site handles for causal abstraction.
Develops monotone tree-based GAMI models using XGBoost.
Develops Austen plots for assessing bias from unobserved confounding in observational studies.
Interpreting a nonparametric regression model with many predictors is known to be a challenging problem. There has been renewed interest in this topic due to the extensive use of machine learning algorithms and the difficulty in understanding and explaining their input-output relationships. This paper develops a unifie…
Plots show miscalibration directly as slopes of secant lines.
Computer vision model automates residual plot assessment for diagnosing model assumptions.
Study predicts synchronization state of financial time series using cross-recurrence plots.
Deep Learning has managed to push boundaries in a wide variety of tasks. One area of interest is to tackle problems in reasoning and understanding, with an aim to emulate human intelligence. In this work, we describe a deep learning model that addresses the reasoning task of question-answering on categorical plots. We …
KM-GPT automates IPD reconstruction from KM plots with high accuracy and scalability.
The size distribution of land plots is a result of land allocation processes in the past. In the absence of regulation this is a Markov process leading an equilibrium described by a probabilistic equation used commonly in the insurance and financial mathematics. We support this claim by analyzing the distribution of tw…
The paper contains a short review of techniques examining regional wealth inequalities based on recently published research work but is also presenting unpublished features. The data pertains to Italy (IT), over the period 2007-2011: the number of cities in regions, the number of inhabitants in cities and in regions, a…
MicroRNAs (miRNAs) are small RNA molecules composed of 19-22 nt, which play important regulatory roles in post-transcriptional gene regulation by inhibiting the translation of the mRNA into proteins or otherwise cleaving the target mRNA. Inferring miRNA targets provides useful information for understanding the roles of…
stCEG models spatial events using Chain Event Graphs in R.
This text discusses several popular explanatory methods that go beyond the error measurements and plots traditionally used to assess machine learning models. Some of the explanatory methods are accepted tools of the trade while others are rigorously derived and backed by long-standing theory. The methods, decision tree…
This paper compares imputation and direct parameter estimation methods for missing data in correlation matrix visualization.
Visualizes classification accuracy and label bias in neural nets and trees.
RAMs improve GAMs' accuracy by fitting components to subregions of feature space.
Study compares Bitcoin and Ethereum tail behavior using Q-Q plots.
We propose a procedure for supervised classification that is based on potential functions. The potential of a class is defined as a kernel density estimate multiplied by the class's prior probability. The method transforms the data to a potential-potential (pot-pot) plot, where each data point is mapped to a vector of …
Recently, our group has published two papers that have received some attention in the finance community. One is about the profitability of trend following strategies over 200 years, the second is about the correlation between the profitability of "Risk Premia" and their skewness. In this short note, we present two addi…
nn2poly converts neural networks into interpretable polynomial models.
Paper quantifies how much machine learning models can be explained.
Two-dimensional embeddings remain the dominant approach to visualize high dimensional data. The choice of embeddings ranges from highly non-linear ones, which can capture complex relationships but are difficult to interpret quantitatively, to axis-aligned projections, which are easy to interpret but are limited to biva…
According to the Loss Distribution Approach, the operational risk of a bank is determined as 99.9% quantile of the respective loss distribution, covering unexpected severe events. The 99.9% quantile can be considered a tail event. As supported by the Pickands-Balkema-de Haan Theorem, tail events exceeding some high thr…