Search Results for author:
Found 17 papers, 7 papers with code
Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation
Our approach enforces sparsity directly by pruning the relevance propagation for the different layers.
Informed Spectral Normalized Gaussian Processes for Trajectory Prediction
Previous work has shown that using such informative priors to regularize probabilistic deep learning (DL) models increases their performance and data-efficiency.
Boosting Causal Additive Models
We present a boosting-based method to learn additive Structural Equation Models (SEMs) from observational data, with a focus on the theoretical aspects of determining the causal order among variables.
Heat increases experienced racial segregation in the United States
We use large-scale foot traffic data for millions of places in 315 US cities between 2018 and 2020 to estimate an index of experienced isolation in daily visits between whites and other ethnic groups.
The Deep Promotion Time Cure Model
We propose a novel method for predicting time-to-event in the presence of cure fractions based on flexible survivals models integrated into a deep neural network framework.
Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families
A theoretical analysis shows that dropout regularization prefers rare but important features in both the mean and dispersion, generalizing an earlier result for conventional generalized linear models.
Scalable Estimation for Structured Additive Distributional Regression
Recently, fitting probabilistic models have gained importance in many areas but estimation of such distributional models with very large data sets is a difficult task.
Informed Priors for Knowledge Integration in Trajectory Prediction
We show that our method outperforms both non-informed and informed learning methods, that are often used in the literature.
Distributional Adaptive Soft Regression Trees
Recently regression forests have been incorporated into the framework of distributional regression, a nowadays popular regression approach aiming at estimating complete conditional distributions rather than relating the mean of an output variable to input features only - as done classically.
Marginally calibrated response distributions for end-to-end learning in autonomous driving
To address this shortcoming we investigate efficient and scalable approximate inference for the implicit copula neural linear model of Klein, Nott and Smith (2021) in order to quantify uncertainty for the predictions of end-to-end learners.
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks.
Bayesian Conditional Transformation Models
A simulation study demonstrates the competitiveness of our approach against its likelihood-based counterpart but also Bayesian additive models of location, scale and shape and Bayesian quantile regression.
Methodology
Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices
Using data from the Australian National Electricity Market, we show that our deep time series models provide accurate short term probabilistic price forecasts, with the copula model dominating.
Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities
We propose a general framework to combine structured regression models and deep neural networks into a unifying network architecture.
bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)
At the core of the package are algorithms for highly-efficient Bayesian estimation and inference that can be applied to generalized additive models (GAMs) or generalized additive models for location, scale, and shape (GAMLSS), also known as distributional regression.
Marginally-calibrated deep distributional regression
The end result is a scalable distributional DNN regression method with marginally calibrated predictions, and our work complements existing methods for probability calibration.
Boosting Joint Models for Longitudinal and Time-to-Event Data
Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in clinical studies where longitudinal outcomes are recorded alongside event times.
