Daniel F. Schmidt

64 papers A* 5B 1C 1Journal 36Unranked 21
YearRankTypeTitle / Venue / Authors
2025 J jnl
Efficient Parameter Estimation for Bayesian Network Classifiers using Hierarchical Linear Smoothing.
CoRR
Connor Cooper, Geoffrey I. Webb, Daniel F. Schmidt
2025 J jnl
Fast Gibbs sampling for the local-seasonal-global trend Bayesian exponential smoothing model.
Stat. Comput.
Xueying Long, Daniel F. Schmidt, Christoph Bergmeir, Slawek Smyl
2025 J jnl
GRASP: Grouped Regression with Adaptive Shrinkage Priors.
CoRR
Shu Yu Tew, Daniel F. Schmidt, Mario Boley
2025 J jnl
Improving Random Forests by Smoothing.
CoRR
Ziyi Liu, Phuc Luong, Mario Boley, Daniel F. Schmidt
2025 J jnl
MONSTER: Monash Scalable Time Series Evaluation Repository.
CoRR
Angus Dempster, Navid Mohammadi Foumani, Chang Wei Tan, Lynn Miller, Amish Mishra, Mahsa Salehi, Charlotte Pelletier, Daniel F. Schmidt, Geoffrey I. Webb
2025 J jnl
MONSTER: Monash Scalable Time Series Evaluation Repository.
J. Data-centric Mach. Learn. Res.
Angus Dempster, Navid Mohammadi Foumani, Chang Wei Tan, Lynn Miller, Amish Mishra, Mahsa Salehi, Charlotte Pelletier, Daniel F. Schmidt, Geoffrey I. Webb
2025 — conf
Minimum Message Length t-Test.
AI (2)
Enes Makalic, Daniel F. Schmidt
2025 — conf
New Multiple Sclerosis Lesion Segmentation via Calibrated Inter-patch Blending.
MICCAI (16)
Jin Ye, Son Duy Dao, Yicheng Wu, Yasmeen M. George, Thanh Nguyen-Duc, Daniel F. Schmidt, Hengcan Shi, Winston Chong, Jianfei Cai
2025 J jnl
SPINE: Token-Selective Test-Time Reinforcement Learning with Entropy-Band Regularization.
CoRR
Jianghao Wu, Yasmeen M. George, Jin Ye, Yicheng Wu, Daniel F. Schmidt, Jianfei Cai
2025 J jnl
Time series adversarial attacks: an investigation of smooth perturbations and defense approaches.
Int. J. Data Sci. Anal.
Gautier Pialla, Hassan Ismail Fawaz, Maxime Devanne, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller, Christoph Bergmeir, Daniel F. Schmidt, Geoffrey I. Webb, Germain Forestier
2024 — conf
Bayesian Parametric Proportional Hazards Regression with the Fused Lasso.
AI (2)
Enes Makalic, Daniel F. Schmidt
2024 J jnl
Computing marginal and conditional divergences between decomposable models with applications in quantum computing and earth observation.
Knowl. Inf. Syst.
Loong Kuan Lee, Geoffrey I. Webb, Daniel F. Schmidt, Nico Piatkowski
2024 J jnl
Fast Gibbs sampling for the local and global trend Bayesian exponential smoothing model.
CoRR
Xueying Long, Daniel F. Schmidt, Christoph Bergmeir, Slawek Smyl
2024 — conf
Highly Scalable Time Series Classification for Very Large Datasets.
AALTD@ECML/PKDD
Angus Dempster, Chang Wei Tan, Lynn Miller, Navid Mohammadi Foumani, Daniel F. Schmidt, Geoffrey I. Webb
2024 — conf
PADMr: Patch-Based Denoising Diffusion Probabilistic Model for Magnetic Resonance Imaging Reconstruction.
ISBI
Mengting Huang, Thanh Nguyen-Duc, Martin Soellradl, Daniel F. Schmidt, Roland Bammer
2024 J jnl
Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data.
CoRR
Angus Dempster, Geoffrey I. Webb, Daniel F. Schmidt
2024 J jnl
quant: a minimalist interval method for time series classification.
Data Min. Knowl. Discov.
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
2023 J jnl
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping.
Mach. Learn.
Benjamin Lucas, Charlotte Pelletier, Daniel F. Schmidt, Geoffrey I. Webb, François Petitjean
2023 J jnl
An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set.
CoRR
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
2023 A* conf
Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization.
NeurIPS
Shu Yu Tew, Mario Boley, Daniel F. Schmidt
2023 J jnl
Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization.
CoRR
Shu Yu Tew, Mario Boley, Daniel F. Schmidt
2023 A* conf
Computing Marginal and Conditional Divergences between Decomposable Models with Applications.
ICDM
Loong Kuan Lee, Geoffrey I. Webb, Daniel F. Schmidt, Nico Piatkowski
2023 J jnl
Computing Marginal and Conditional Divergences between Decomposable Models with Applications.
CoRR
Loong Kuan Lee, Geoffrey I. Webb, Daniel F. Schmidt, Nico Piatkowski
2023 J jnl
From Prediction to Action: The Critical Role of Proper Performance Estimation for Machine-Learning-Driven Materials Discovery.
CoRR
Mario Boley, Felix Luong, Simon Teshuva, Daniel F. Schmidt, Lucas Foppa, Matthias Scheffler
2023 J jnl
Hydra: competing convolutional kernels for fast and accurate time series classification.
Data Min. Knowl. Discov.
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
2023 J jnl
Improved Neurophysiological Process Imaging Through Optimization of Kalman Filter Initial Conditions.
Int. J. Neural Syst.
Yun Zhao, Felix Luong, Simon Teshuva, Andria Pelentritou, William Woods, David T. J. Liley, Daniel F. Schmidt, Mario Boley, Levin Kuhlmann
2023 J jnl
Local and Global Trend Bayesian Exponential Smoothing Models.
CoRR
Slawek Smyl, Christoph Bergmeir, Alexander Dokumentov, Erwin Wibowo, Daniel F. Schmidt
2023 J jnl
Maximum likelihood estimation of the Weibull distribution with reduced bias.
Stat. Comput.
Enes Makalic, Daniel F. Schmidt
2023 — conf
Minimum Message Length Inference of the Weibull Distribution with Complete and Censored Data.
AI (1)
Enes Makalic, Daniel F. Schmidt
2023 J jnl
QUANT: A Minimalist Interval Method for Time Series Classification.
CoRR
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
2023 J jnl
SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting.
Mach. Learn.
Rakshitha Godahewa, Geoffrey I. Webb, Daniel F. Schmidt, Christoph Bergmeir
2023 J jnl
Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approach.
CoRR
Xueying Long, Quang Bui, Grady Oktavian, Daniel F. Schmidt, Christoph Bergmeir, Rakshitha Godahewa, Seong Per Lee, Kaifeng Zhao, Paul Condylis
2022 J jnl
An efficient algorithm for sampling from sin
Commun. Stat. Simul. Comput.
Enes Makalic, Daniel F. Schmidt
2022 J jnl
HYDRA: Competing convolutional kernels for fast and accurate time series classification.
CoRR
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
2022 J jnl
SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting.
CoRR
Rakshitha Godahewa, Geoffrey I. Webb, Daniel F. Schmidt, Christoph Bergmeir
2022 — conf
Smooth Perturbations for Time Series Adversarial Attacks.
PAKDD (1)
Gautier Pialla, Hassan Ismail Fawaz, Maxime Devanne, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller, Christoph Bergmeir, Daniel F. Schmidt, Geoffrey I. Webb, Germain Forestier
2022 — conf
Sparse Horseshoe Estimation via Expectation-Maximisation.
ECML/PKDD (5)
Shu Yu Tew, Daniel F. Schmidt, Enes Makalic
2022 J jnl
Sparse Horseshoe Estimation via Expectation-Maximisation.
CoRR
Shu Yu Tew, Daniel F. Schmidt, Enes Makalic
2021 A* conf
MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification.
KDD
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
2021 B conf
On Universal Codes for Integers: Wallace Tree, Elias Omega and Beyond.
DCC
Lloyd Allison, Arun Siddharth Konagurthu, Daniel F. Schmidt
2020 J jnl
A Bayesian-inspired, deep learning, semi-supervised domain adaptation technique for land cover mapping.
CoRR
Benjamin Lucas, Charlotte Pelletier, Daniel F. Schmidt, Geoffrey I. Webb, François Petitjean
2020 J jnl
InceptionTime: Finding AlexNet for time series classification.
Data Min. Knowl. Discov.
Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier, Charlotte Pelletier, Daniel F. Schmidt, Jonathan Weber, Geoffrey I. Webb, Lhassane Idoumghar, Pierre-Alain Muller, François Petitjean
2020 J jnl
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification.
CoRR
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
2020 C conf
Unsupervised Domain Adaptation Techniques for Classification of Satellite Image Time Series.
IGARSS
Benjamin Lucas, Charlotte Pelletier, Daniel F. Schmidt, Geoffrey I. Webb, François Petitjean
2019 — conf
Bayesian Generalized Horseshoe Estimation of Generalized Linear Models.
ECML/PKDD (2)
Daniel F. Schmidt, Enes Makalic
2019 — conf
Exploring Data Quantity Requirements for Domain Adaptation in the Classification of Satellite Image Time Series.
MultiTemp
Benjamin Lucas, Charlotte Pelletier, Jordi Inglada, Daniel F. Schmidt, Geoffrey I. Webb, François Petitjean
2019 J jnl
InceptionTime: Finding AlexNet for Time Series Classification.
CoRR
Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier, Charlotte Pelletier, Daniel F. Schmidt, Jonathan Weber, Geoffrey I. Webb, Lhassane Idoumghar, Pierre-Alain Muller, François Petitjean
2019 J jnl
On Universal Codes for Integers: Wallace Tree, Elias Omega and Variations.
CoRR
Lloyd Allison, Arun Siddharth Konagurthu, Daniel F. Schmidt
2018 J jnl
Adaptive Bayesian Shrinkage Estimation Using Log-Scale Shrinkage Priors.
CoRR
Daniel F. Schmidt, Enes Makalic
2017 — conf
Robust Lasso Regression with Student-t Residuals.
Australasian Conference on Artificial Intelligence
Daniel F. Schmidt, Enes Makalic
2016 J jnl
A Simple Sampler for the Horseshoe Estimator.
IEEE Signal Process. Lett.
Enes Makalic, Daniel F. Schmidt
2016 — conf
Approximating Message Lengths of Hierarchical Bayesian Models Using Posterior Sampling.
Australasian Conference on Artificial Intelligence
Daniel F. Schmidt, Enes Makalic, John L. Hopper
2016 — conf
Bayesian Grouped Horseshoe Regression with Application to Additive Models.
Australasian Conference on Artificial Intelligence
Zemei Xu, Daniel F. Schmidt, Enes Makalic, Guoqi Q. Qian, John L. Hopper
2016 — conf
Bayesian Robust Regression with the Horseshoe+ Estimator.
Australasian Conference on Artificial Intelligence
Enes Makalic, Daniel F. Schmidt, John L. Hopper
2015 J jnl
High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies.
Health Inf. Sci. Syst.
Benjamin Goudey, Mani Abedini, John L. Hopper, Michael Inouye, Enes Makalic, Daniel F. Schmidt, John Wagner, Zeyu Zhou, Justin Zobel, Matthias Reumann
2013 — conf
DEPTH: A Novel Algorithm for Feature Ranking with Application to Genome-Wide Association Studies.
Australasian Conference on Artificial Intelligence
Enes Makalic, Daniel F. Schmidt, John L. Hopper
2013 — conf
Minimum Message Length Ridge Regression for Generalized Linear Models.
Australasian Conference on Artificial Intelligence
Daniel F. Schmidt, Enes Makalic
2012 — conf
MML Logistic Regression with Translation and Rotation Invariant Priors.
Australasian Conference on Artificial Intelligence
Enes Makalic, Daniel F. Schmidt
2012 — conf
Minimum Message Length Inference and Mixture Modelling of Inverse Gaussian Distributions.
Australasian Conference on Artificial Intelligence
Daniel F. Schmidt, Enes Makalic
2012 — conf
Supercomputing enabling exhaustive statistical analysis of genome wide association study data: Preliminary results.
EMBC
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
2011 — conf
Minimum Message Length Analysis of the Behrens-Fisher Problem.
Algorithmic Probability and Friends
Enes Makalic, Daniel F. Schmidt
2011 — conf
Minimum Message Length Order Selection and Parameter Estimation of Moving Average Models.
Algorithmic Probability and Friends
Daniel F. Schmidt
2005 A* conf
Adaptive Control of Hydraulic Systems with MML Inferred RBF Networks.
ICRA
Daniel F. Schmidt, Andrew P. Paplinski, Gordon S. Lowe
2004 A* conf
On the Design of a Hydraulically Actuated Finger for Dexterous Manipulation.
ICRA
Daniel F. Schmidt, Gordon S. Lowe, Andrew P. Paplinski