Daniel F. Schmidt

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 40

Top Topics

Maximum Likelihood Estimation

Hierarchical Bayesian Models

Ridge Regression

Domain Adaptation

Top Venues

Australasian Conference on Artificial Intelligence

Data Min. Knowl. Discov.

Publications

Angus Dempster, Geoffrey I. Webb, Daniel F. Schmidt
Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data. CoRR (2024)
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
quant: a minimalist interval method for time series classification. Data Min. Knowl. Discov. 38 (4) (2024)
Xueying Long, Daniel F. Schmidt, Christoph Bergmeir, Slawek Smyl
Fast Gibbs sampling for the local and global trend Bayesian exponential smoothing model. CoRR (2024)
Xueying Long, Quang Bui, Grady Oktavian, Daniel F. Schmidt, Christoph Bergmeir, Rakshitha Godahewa, Seong Per Lee, Kaifeng Zhao, Paul Condylis
Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approach. CoRR (2023)
Enes Makalic, Daniel F. Schmidt
Maximum likelihood estimation of the Weibull distribution with reduced bias. Stat. Comput. 33 (3) (2023)
Loong Kuan Lee, Geoffrey I. Webb, Daniel F. Schmidt, Nico Piatkowski
Computing Marginal and Conditional Divergences between Decomposable Models with Applications. ICDM (2023)
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
QUANT: A Minimalist Interval Method for Time Series Classification. CoRR (2023)
Benjamin Lucas, Charlotte Pelletier, Daniel F. Schmidt, Geoffrey I. Webb, François Petitjean
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping. Mach. Learn. 112 (6) (2023)
Mario Boley, Felix Luong, Simon Teshuva, Daniel F. Schmidt, Lucas Foppa, Matthias Scheffler
From Prediction to Action: The Critical Role of Proper Performance Estimation for Machine-Learning-Driven Materials Discovery. CoRR (2023)
Slawek Smyl, Christoph Bergmeir, Alexander Dokumentov, Erwin Wibowo, Daniel F. Schmidt
Local and Global Trend Bayesian Exponential Smoothing Models. CoRR (2023)
Shu Yu Tew, Mario Boley, Daniel F. Schmidt
Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization. NeurIPS (2023)
Ali Ismail-Fawaz, Angus Dempster, Chang Wei Tan, Matthieu Herrmann, Lynn Miller, Daniel F. Schmidt, Stefano Berretti, Jonathan Weber, Maxime Devanne, Germain Forestier, Geoffrey I. Webb
An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set. CoRR (2023)
Rakshitha Godahewa, Geoffrey I. Webb, Daniel F. Schmidt, Christoph Bergmeir
SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting. Mach. Learn. 112 (7) (2023)
Enes Makalic, Daniel F. Schmidt
Minimum Message Length Inference of the Weibull Distribution with Complete and Censored Data. AI (1) (2023)
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
Hydra: competing convolutional kernels for fast and accurate time series classification. Data Min. Knowl. Discov. 37 (5) (2023)
Loong Kuan Lee, Geoffrey I. Webb, Daniel F. Schmidt, Nico Piatkowski
Computing Marginal and Conditional Divergences between Decomposable Models with Applications. CoRR (2023)
Yun Zhao, Felix Luong, Simon Teshuva, Andria Pelentritou, William Woods, David T. J. Liley, Daniel F. Schmidt, Mario Boley, Levin Kuhlmann
Improved Neurophysiological Process Imaging Through Optimization of Kalman Filter Initial Conditions. Int. J. Neural Syst. 33 (5) (2023)
Shu Yu Tew, Mario Boley, Daniel F. Schmidt
Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization. CoRR (2023)
Shu Yu Tew, Daniel F. Schmidt, Enes Makalic
Sparse Horseshoe Estimation via Expectation-Maximisation. ECML/PKDD (5) (2022)
Enes Makalic, Daniel F. Schmidt
) for generating random correlation matrices. Commun. Stat. Simul. Comput. 51 (5) (2022)
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
HYDRA: Competing convolutional kernels for fast and accurate time series classification. CoRR (2022)
Rakshitha Godahewa, Geoffrey I. Webb, Daniel F. Schmidt, Christoph Bergmeir
SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting. CoRR (2022)
Shu Yu Tew, Daniel F. Schmidt, Enes Makalic
Sparse Horseshoe Estimation via Expectation-Maximisation. CoRR (2022)
Gautier Pialla, Hassan Ismail Fawaz, Maxime Devanne, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller, Christoph Bergmeir, Daniel F. Schmidt, Geoffrey I. Webb, Germain Forestier
Smooth Perturbations for Time Series Adversarial Attacks. PAKDD (1) (2022)
Lloyd Allison, Arun Siddharth Konagurthu, Daniel F. Schmidt
On Universal Codes for Integers: Wallace Tree, Elias Omega and Beyond. DCC (2021)
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification. KDD (2021)
Benjamin Lucas, Charlotte Pelletier, Daniel F. Schmidt, Geoffrey I. Webb, François Petitjean
A Bayesian-inspired, deep learning, semi-supervised domain adaptation technique for land cover mapping. CoRR (2020)
Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier, Charlotte Pelletier, Daniel F. Schmidt, Jonathan Weber, Geoffrey I. Webb, Lhassane Idoumghar, Pierre-Alain Muller, François Petitjean
InceptionTime: Finding AlexNet for time series classification. Data Min. Knowl. Discov. 34 (6) (2020)
Benjamin Lucas, Charlotte Pelletier, Daniel F. Schmidt, Geoffrey I. Webb, François Petitjean
Unsupervised Domain Adaptation Techniques for Classification of Satellite Image Time Series. IGARSS (2020)
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification. CoRR (2020)
Daniel F. Schmidt, Enes Makalic
Bayesian Generalized Horseshoe Estimation of Generalized Linear Models. ECML/PKDD (2) (2019)
Benjamin Lucas, Charlotte Pelletier, Jordi Inglada, Daniel F. Schmidt, Geoffrey I. Webb, François Petitjean
Exploring Data Quantity Requirements for Domain Adaptation in the Classification of Satellite Image Time Series. MultiTemp (2019)
Lloyd Allison, Arun Siddharth Konagurthu, Daniel F. Schmidt
On Universal Codes for Integers: Wallace Tree, Elias Omega and Variations. CoRR (2019)
Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier, Charlotte Pelletier, Daniel F. Schmidt, Jonathan Weber, Geoffrey I. Webb, Lhassane Idoumghar, Pierre-Alain Muller, François Petitjean
InceptionTime: Finding AlexNet for Time Series Classification. CoRR (2019)
Daniel F. Schmidt, Enes Makalic
Adaptive Bayesian Shrinkage Estimation Using Log-Scale Shrinkage Priors. CoRR (2018)
Daniel F. Schmidt, Enes Makalic
Robust Lasso Regression with Student-t Residuals. Australasian Conference on Artificial Intelligence (2017)
Enes Makalic, Daniel F. Schmidt, John L. Hopper
Bayesian Robust Regression with the Horseshoe+ Estimator. Australasian Conference on Artificial Intelligence (2016)
Daniel F. Schmidt, Enes Makalic, John L. Hopper
Approximating Message Lengths of Hierarchical Bayesian Models Using Posterior Sampling. Australasian Conference on Artificial Intelligence (2016)
Zemei Xu, Daniel F. Schmidt, Enes Makalic, Guoqi Q. Qian, John L. Hopper
Bayesian Grouped Horseshoe Regression with Application to Additive Models. Australasian Conference on Artificial Intelligence (2016)
Enes Makalic, Daniel F. Schmidt
A Simple Sampler for the Horseshoe Estimator. IEEE Signal Process. Lett. 23 (1) (2016)
Benjamin Goudey, Mani Abedini, John L. Hopper, Michael Inouye, Enes Makalic, Daniel F. Schmidt, John Wagner, Zeyu Zhou, Justin Zobel, Matthias Reumann
High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies. Health Inf. Sci. Syst. 3 (1) (2015)
Daniel F. Schmidt, Enes Makalic
Minimum Message Length Ridge Regression for Generalized Linear Models. Australasian Conference on Artificial Intelligence (2013)
Enes Makalic, Daniel F. Schmidt, John L. Hopper
DEPTH: A Novel Algorithm for Feature Ranking with Application to Genome-Wide Association Studies. Australasian Conference on Artificial Intelligence (2013)
Daniel F. Schmidt, Enes Makalic
Minimum Message Length Inference and Mixture Modelling of Inverse Gaussian Distributions. Australasian Conference on Artificial Intelligence (2012)
Enes Makalic, Daniel F. Schmidt
MML Logistic Regression with Translation and Rotation Invariant Priors. Australasian Conference on Artificial Intelligence (2012)
Matthias Reumann, Enes Makalic, Benjamin W. Goudey, Michael Inouye, Adrian Bickerstaffe, Minh Bui, Daniel J. Park, Miroslaw K. Kapuscinski, Daniel F. Schmidt, Zeyu Zhou, Guoqi Q. Qian, Justin Zobel, John Wagner, John L. Hopper
Supercomputing enabling exhaustive statistical analysis of genome wide association study data: Preliminary results. EMBC (2012)
Enes Makalic, Daniel F. Schmidt
Minimum Message Length Analysis of the Behrens-Fisher Problem. Algorithmic Probability and Friends (2011)
Daniel F. Schmidt
Minimum Message Length Order Selection and Parameter Estimation of Moving Average Models. Algorithmic Probability and Friends (2011)
Daniel F. Schmidt, Andrew P. Paplinski, Gordon S. Lowe
Adaptive Control of Hydraulic Systems with MML Inferred RBF Networks. ICRA (2005)
Daniel F. Schmidt, Gordon S. Lowe, Andrew P. Paplinski
On the Design of a Hydraulically Actuated Finger for Dexterous Manipulation. ICRA (2004)