The lack of interpretability often makes black-box models difficult to be applied to many practical domains. For this reason, the current work, from the black-box model input port, proposes to incorporate data-based prior information into the black-box soft-margin SVM model to enhance its interpretability. The concept …
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48 results for “Data-Based Modeling”
This paper addresses the model-free nonlinear optimal problem with generalized cost functional, and a data-based reinforcement learning technique is developed. It is known that the nonlinear optimal control problem relies on the solution of the Hamilton-Jacobi-Bellman (HJB) equation, which is a nonlinear partial differ…
Study develops a data-based model for in-cylinder pressure and cyclic variations in RCCI engines.
problem Lack of models capturing cyclic variations in combustion concepts like RCCI.
method Combines Principle Component Decomposition and Gaussian Process Regression.
result Model predicts combustion measures with high accuracy, especially peak-pressure rise-rate.
Efficiently samples multimodal distributions using data-based initialization.
problem Sampling multimodal distributions with limited samples.
method Data-based initialization for Markov chains with spectral gap.
result Efficiently generates samples close to stationary distribution.
Proposes a model for generating survival trajectories and data.
problem Generating survival trajectories and data for new feature vectors.
method Autoencoder with specific structure, incorporating weighting scheme for robustness.
result Generates a prototype time-dependent trajectory for objects.
In an earlier work we identified the types and numbers of static equilibrium points of solids arising from fine, equidistant -discretrizations of smooth, convex surfaces. We showed that such discretizations carry equilibrium points on two scales: the local scale corresponds to the discretization, the global scale to…
This paper addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical system with continuous state space, continuous action space and unknown dynamics. This class of problems are typically addressed in stochastic adaptive control and reinforcement learning literature using model-b…
Early fault detection using instrumented sensor data is one of the promising application areas of machine learning in industrial facilities. However, it is difficult to improve the generalization performance of the trained fault-detection model because of the complex system configuration in the target diagnostic system…
Detects project management anti-patterns using code and issue data.
problem Detecting project management anti-patterns requires expert judgment and is expensive.
method Convert descriptions to detectable metrics, quantify deviations, and optimize patterns.
result Automatic calibration enhances pattern detection and severity assessment.
A Prior-Predictive Monte Carlo Framework for Pricing Complex Data Products in Data-Poor Marketsq-fin.CP
Framework for pricing data products in data-poor markets.
problem Challenges in pricing advanced data products due to lack of transaction data.
method Prior-predictive Monte Carlo framework for generating probabilistic price bands.
result Stable probabilistic price bands for data products in data-poor markets.
Adaptive Scalingstat.ML
Preprocessing data is an important step before any data analysis. In this paper, we focus on one particular aspect, namely scaling or normalization. We analyze various scaling methods in common use and study their effects on different statistical learning models. We will propose a new two-stage scaling method. First, w…
The paper highlights how machine learning calibrations can be biased by training data.
problem Machine learning calibrations can be biased by the training data, affecting downstream analyses.
method The paper examines simulation-based and data-based calibrations, highlighting their prior dependence and proposing solutions.
result A recently proposed Gaussian Ansatz approach can avoid some biases in simulation-based calibrations.
Improves data-driven reachability estimation for complex systems.
problem Estimating reachable states in complex dynamical systems with unknown parameters.
method Uses Christoffel functions and conformal prediction to improve sample efficiency and robustness.
result Guaranteed convergence to the true reach set with improved sample efficiency and robustness.
We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model us…
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systemsphysics.comp-ph
Data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems. The present paper offers a data-based, probablistic perspective that enables the quantification of predictive u…
Despite its popularity, it is widely recognized that the investigation of some theoretical aspects of clustering has been relatively sparse. One of the main reasons for this lack of theoretical results is surely the fact that, whereas for other statistical problems the theoretical population goal is clearly defined (as…
Discovering causal relations among observed variables in a given data set is a main topic in studies of statistics and artificial intelligence. Recently, some techniques to discover an identifiable causal structure have been explored based on non-Gaussianity of the observed data distribution. However, most of these are…
Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training. To date, however, this strategy has been most applicable to models of independent data. We propose an extension to state space models of time series data based on a novel generative model…
Digital twins improve single-arm trials by providing robust treatment effect estimates.
problem Lack of control arms in single-arm trials limits their gold-standard evidence.
method Outcome-model-based synthetic controls using machine learning models trained on historical data.
result Digital twins offer more robust treatment effect estimates and principled corrections.
This paper examines the intra-day seasonality of transacted limit and market orders in the DEM/USD foreign exchange market. Empirical analysis of completed transactions data based on the Dealing 2000-2 electronic inter-dealer broking system indicates significant evidence of intraday seasonality in returns and return vo…
We introduce a method to learn a hierarchy of successively more abstract representations of complex data based on optimizing an information-theoretic objective. Intuitively, the optimization searches for a set of latent factors that best explain the correlations in the data as measured by multivariate mutual informatio…
Study smooth hypersurfaces with prescribed curvature in Minkowski space.
problem Existence of smooth spacelike hypersurfaces with prescribed curvature.
method Proving existence based on estimates.
result Existence of smooth spacelike hypersurfaces with prescribed curvature.
This paper reviews deep learning methods for handling irregularly sampled medical time series data.
problem Handling irregularly sampled medical time series data for personalized treatment and precise diagnosis.
method Summarizes and compares deep learning methods categorized by technology and task.
result Achieved good results in data imputation and downstream tasks.
Cavity Filling: Pseudo-Feature Generation for Multi-Class Imbalanced Data Problems in Deep Learningcs.LG
Herein, we generate pseudo-features based on the multivariate probability distributions obtained from the feature maps in layers of trained deep neural networks. Further, we augment the minor-class data based on these generated pseudo-features to overcome the imbalanced data problems. The proposed method, i.e., cavity …
We propose a mixture of latent trait models with common slope parameters (MCLT) for model-based clustering of high-dimensional binary data, a data type for which few established methods exist. Recent work on clustering of binary data, based on a -dimensional Gaussian latent variable, is extended by incorporating com…
The model is aimed to discriminate the 'good' and the 'bad' companies in Russian corporate sector based on their financial statements data based on Russian Accounting Standards. The data sample consists of 126 Russian public companies- issuers of Ruble bonds which represent about 36% of total number of corporate bonds …
Explains quantum cohomology of Grassmannians using tt* equations.
problem Relates quantum cohomology of complex Grassmannians to projective space.
method Uses tt* equations and Lie-theoretic connections.
result Illustrates relations between tt* equations and quantum cohomology.
In this paper, a machine learning-based simulation framework of general-purpose multibody dynamics is introduced. The aim of the framework is to generate a well-trained meta-model of multibody dynamics (MBD) systems. To this end, deep neural network (DNN) is employed to the framework so as to construct data-based meta-…
Models for sequential data such as the recurrent neural network (RNN) often implicitly model a sequence as having a fixed time interval between observations and do not account for group-level effects when multiple sequences are observed. We propose a model for grouped sequential data based on the RNN that accounts for …
Non-parametric approaches for analyzing network data based on exchangeable graph models (ExGM) have recently gained interest. The key object that defines an ExGM is often referred to as a graphon. This non-parametric perspective on network modeling poses challenging questions on how to make inference on the graphon und…
As regulators pay more attentions to losses rather than gains, we are able to derive a new class of risk statistics, named regulator-based risk statistics with scenario analysis in this paper. This new class of risk statistics can be considered as a kind of risk extension of risk statistics introduced by Kou et al. \ci…
New model improves neural network robustness against input manipulations.
problem Improving neural network robustness against input manipulations.
method Causal view and deep causal manipulation augmented model (deep CAMA) with data augmentation and test-time fine-tuning.
result Deep CAMA shows superior robustness against unseen manipulations compared to traditional models.
The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and to control overfitting. Augmentor is a software package, available in both Python and Julia versions, that provides a high level API for th…
This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs. The HONE framework is highly expressive and flexible with many interchangeable components. The experimental results demonstrate the effectiveness of learning higher-order network represe…
In light of the power problems of statistical tests and undisciplined use of alpha-based statistics to compare models, this paper proposes a unified set of distance-based performance metrics, derived as the square root of the sum of squared alphas and squared standard errors. The Bayesian investor views model performan…
We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various tradeoffs in the design of attacks. We demonstrate the effectiveness of thes…
Sparse non-Gaussian component analysis (SNGCA) is an unsupervised method of extracting a linear structure from a high dimensional data based on estimating a low-dimensional non-Gaussian data component. In this paper we discuss a new approach to direct estimation of the projector on the target space based on semidefinit…
On the use of Statistical Learning Theory for model selection in Structural Health Monitoringstat.ML
Paper uses SLT to improve model selection for SHM.
problem Model selection for SHM using data-based systems.
method Utilizes Statistical Learning Theory to rigorously estimate generalisation.
result Incorporating domain knowledge improves model generalisation.
Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods. Among those variational regularization models are one of the most popular approaches. We propose a new framework for applying data-driven approaches to inverse problems, using a neural network as a regular…
We propose two related unsupervised clustering algorithms which, for input, take data assumed to be sampled from a uniform distribution supported on a metric space , and output a clustering of the data based on the selection of a topological model for the connected components of . Both algorithms work by selectin…
New method visualizes decision boundaries of classification models.
problem Difficulty in understanding how classification models interpret data.
method Hybrid supervised-unsupervised technique for visualizing decision boundaries.
result Provides interpretable maps for qualitative and quantitative analysis.
Deep learning models trained using massive amounts of data tend to capture one view of the data and its associated mapping. Different deep learning models built on the same training data may capture different views of the data based on the underlying techniques used. For explaining the decisions arrived by blackbox dee…
Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of the agent-based models from empirical data instead of setting them artificially was suggested. We first revi…
New neural network models for functional data.
problem Handling non-linear functional data.
method Functional Direct Neural Network (FDNN) and Functional Basis Neural Network (FBNN) with gradient-based optimization.
result Demonstrated effectiveness in complex functional models.
The goal of the change-point detection is to discover changes of time series distribution. One of the state of the art approaches of the change-point detection are based on direct density ratio estimation. In this work we show how existing algorithms can be generalized using various binary classification and regression…
Flow models have recently made great progress at modeling ordinal discrete data such as images and audio. Due to the continuous nature of flow models, dequantization is typically applied when using them for such discrete data, resulting in lower bound estimates of the likelihood. In this paper, we introduce subset flow…
This paper provides a new unimodality test with application in hierarchical clustering methods. The proposed method denoted by signature test (Sigtest), transforms the data based on its statistics. The transformed data has much smaller variation compared to the original data and can be evaluated in a simple proposed un…
We study the possibility of completing data bases of a sample of governance, diversification and value creation variables by providing a well adapted method to reconstruct the missing parts in order to obtain a complete sample to be applied for testing the ownership-structure/diversification relationship. It consists o…