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Found 13 papers, 2 papers with code
Algorithmic syntactic causal identification
Our description is given entirely in terms of the non-parametric ADMG structure specifying a causal model and the algebraic signature of the corresponding monoidal category, to which a sequence of manipulations is then applied so as to arrive at a modified monoidal category in which the desired, purely syntactic interventional causal model, is obtained.
An efficient, provably exact, practical algorithm for the 0-1 loss linear classification problem
We demonstrate the effectiveness of this algorithm on synthetic and real-world datasets, showing optimal accuracy both in and out-of-sample, in practical computational time.
Patient-Specific Game-Based Transfer Method for Parkinson's Disease Severity Prediction
Therefore, this paper proposes a patient-specific game-based transfer (PSGT) method for PD severity prediction.
Dynamic programming by polymorphic semiring algebraic shortcut fusion
Dynamic programming (DP) is an algorithmic design paradigm for the efficient, exact solution of otherwise intractable, combinatorial problems.
Few-shot time series segmentation using prototype-defined infinite hidden Markov models
We propose a robust framework for interpretable, few-shot analysis of non-stationary sequential data based on flexible graphical models to express the structured distribution of sequential events, using prototype radial basis function (RBF) neural network emissions.
Detecting Parkinson's Disease From an Online Speech-task
In this paper, we envision a web-based framework that can help anyone, anywhere around the world record a short speech task, and analyze the recorded data to screen for Parkinson's disease (PD).
Controlling for sparsity in sparse factor analysis models: adaptive latent feature sharing for piecewise linear dimensionality reduction
Ubiquitous linear Gaussian exploratory tools such as principle component analysis (PCA) and factor analysis (FA) remain widely used as tools for: exploratory analysis, pre-processing, data visualization and related tasks.
Causal bootstrapping
However, these techniques are often incompatible with modern, nonparametric machine learning algorithms since they typically require explicit probabilistic models.
Adaptive probabilistic principal component analysis
Using the linear Gaussian latent variable model as a starting point we relax some of the constraints it imposes by deriving a nonparametric latent feature Gaussian variable model.
Bayesian Pitch Tracking Based on the Harmonic Model
In this paper, a fully Bayesian fundamental frequency tracking algorithm based on the harmonic model and a first-order Markov process model is proposed.
High Frequency Remote Monitoring of Parkinson's Disease via Smartphone: Platform Overview and Medication Response Detection
Objective: The aim of this study is to develop a smartphone-based high-frequency remote monitoring platform, assess its feasibility for remote monitoring of symptoms in Parkinson's disease, and demonstrate the value of data collected using the platform by detecting dopaminergic medication response.
Computers and Society
Simple approximate MAP Inference for Dirichlet processes
This is a well-posed approximation to the MAP solution of the probabilistic DPM model.
Highly comparative time-series analysis: The empirical structure of time series and their methods
This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines, and automate the selection of useful methods for time-series classification and regression tasks.
