On the Design of a Hydraulically Actuated Finger for Dexterous Manipulation. - Daniel F. Schmidt; Gordon S. Lowe; Andrew P. Paplinski (ICRA, 2004)

Adaptive Control of Hydraulic Systems with MML Inferred RBF Networks. - Daniel F. Schmidt; Andrew P. Paplinski; Gordon S. Lowe (ICRA, 2005)

A Probabilistic Model for Understanding Composite Spoken Descriptions. - Enes Makalic; Ingrid Zukerman; Michael Niemann; Daniel Francis Schmidt (PRICAI, 2008)

MML Invariant Linear Regression. - Daniel Francis Schmidt; Enes Makalic (Australasian Conference on Artificial Intelligence, 2009)

Spatial Processes for Recommender Systems. - Fabian Bohnert; Daniel Francis Schmidt; Ingrid Zukerman (IJCAI, 2009)

Using Gaussian Spatial Processes to Model and Predict Interests in Museum Exhibits. - Fabian Bohnert; Ingrid Zukerman; Daniel Francis Schmidt (ITWP, 2009)

Assessing the Impact of Measurement Uncertainty on User Models in Spatial Domains. - Daniel Francis Schmidt; Ingrid Zukerman; David W. Albrecht (UMAP, 2009)

Universal models for the exponential distribution. - Daniel Francis Schmidt; Enes Makalic (IEEE Trans. Inf. Theory, 2009)

Review of Modern Logistic Regression Methods with Application to Small and Medium Sample Size Problems. - Enes Makalic; Daniel Francis Schmidt (Australasian Conference on Artificial Intelligence, 2010)

The Behaviour of the Akaike Information Criterion When Applied to Non-nested Sequences of Models. - Daniel Francis Schmidt; Enes Makalic (Australasian Conference on Artificial Intelligence, 2010)

Fast Computation of the Kullback-Leibler Divergence and Exact Fisher Information for the First-Order Moving Average Model. - Enes Makalic; Daniel Francis Schmidt (IEEE Signal Process. Lett., 2010)

Logistic Regression with the Nonnegative Garrote. - Enes Makalic; Daniel Francis Schmidt (Australasian Conference on Artificial Intelligence, 2011)

A Simple Bayesian Algorithm for Feature Ranking in High Dimensional Regression Problems. - Enes Makalic; Daniel Francis Schmidt (Australasian Conference on Artificial Intelligence, 2011)

Minimum Message Length Analysis of the Behrens-Fisher Problem. - Enes Makalic; Daniel F. Schmidt (Algorithmic Probability and Friends, 2011)

Minimum Message Length Order Selection and Parameter Estimation of Moving Average Models. - Daniel F. Schmidt (Algorithmic Probability and Friends, 2011)

Estimating the Order of an Autoregressive Model Using Normalized Maximum Likelihood. - Daniel Francis Schmidt; Enes Makalic (IEEE Trans. Signal Process., 2011)

MML Logistic Regression with Translation and Rotation Invariant Priors. - Enes Makalic; Daniel F. Schmidt (Australasian Conference on Artificial Intelligence, 2012)

Minimum Message Length Inference and Mixture Modelling of Inverse Gaussian Distributions. - Daniel F. Schmidt; Enes Makalic (Australasian Conference on Artificial Intelligence, 2012)

Supercomputing enabling exhaustive statistical analysis of genome wide association study data: Preliminary results. - 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; et al. (EMBC, 2012)

The Consistency of MDL for Linear Regression Models With Increasing Signal-to-Noise Ratio. - Daniel Francis Schmidt; Enes Makalic (IEEE Trans. Signal Process., 2012)

DEPTH: A Novel Algorithm for Feature Ranking with Application to Genome-Wide Association Studies. - Enes Makalic; Daniel F. Schmidt; John L. Hopper (Australasian Conference on Artificial Intelligence, 2013)

Minimum Message Length Ridge Regression for Generalized Linear Models. - Daniel F. Schmidt; Enes Makalic (Australasian Conference on Artificial Intelligence, 2013)

High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies. - Benjamin Goudey; Mani Abedini; John L. Hopper; Michael Inouye; Enes Makalic; Daniel F. Schmidt; John Wagner; Zeyu Zhou; Justin Zobel; Matthias Reumann (Health Inf. Sci. Syst., 2015)

Bayesian Robust Regression with the Horseshoe+ Estimator. - Enes Makalic; Daniel F. Schmidt; John L. Hopper (Australasian Conference on Artificial Intelligence, 2016)

Approximating Message Lengths of Hierarchical Bayesian Models Using Posterior Sampling. - Daniel F. Schmidt; Enes Makalic; John L. Hopper (Australasian Conference on Artificial Intelligence, 2016)

Bayesian Grouped Horseshoe Regression with Application to Additive Models. - Zemei Xu; Daniel F. Schmidt; Enes Makalic; Guoqi Q. Qian; John L. Hopper (Australasian Conference on Artificial Intelligence, 2016)

A Simple Sampler for the Horseshoe Estimator. - Enes Makalic; Daniel F. Schmidt (IEEE Signal Process. Lett., 2016)

Robust Lasso Regression with Student-t Residuals. - Daniel F. Schmidt; Enes Makalic (Australasian Conference on Artificial Intelligence, 2017)

Adaptive Bayesian Shrinkage Estimation Using Log-Scale Shrinkage Priors. - Daniel F. Schmidt; Enes Makalic (CoRR, 2018)

Exploring Data Quantity Requirements for Domain Adaptation in the Classification of Satellite Image Time Series. - Benjamin Lucas; Charlotte Pelletier; Jordi Inglada; Daniel F. Schmidt; Geoffrey I. Webb; François Petitjean (MultiTemp, 2019)

Bayesian Generalized Horseshoe Estimation of Generalized Linear Models. - Daniel F. Schmidt; Enes Makalic (ECML/PKDD (2), 2019)

On Universal Codes for Integers: Wallace Tree, Elias Omega and Variations. - Lloyd Allison; Arun Siddharth Konagurthu; Daniel F. Schmidt (CoRR, 2019)

Unsupervised Domain Adaptation Techniques for Classification of Satellite Image Time Series. - Benjamin Lucas; Charlotte Pelletier; Daniel F. Schmidt; Geoffrey I. Webb; François Petitjean (IGARSS, 2020)

A Bayesian-inspired, deep learning, semi-supervised domain adaptation technique for land cover mapping. - Benjamin Lucas; Charlotte Pelletier; Daniel F. Schmidt; Geoffrey I. Webb; François Petitjean (CoRR, 2020)

InceptionTime: Finding AlexNet for time series classification. - Hassan Ismail Fawaz; Benjamin Lucas; Germain Forestier; Charlotte Pelletier; Daniel F. Schmidt; Jonathan Weber; Geoffrey I. Webb; Lhassane Idoumghar; Pierre-Alain Muller; François Petitjean (Data Min. Knowl. Discov., 2020)

On Universal Codes for Integers: Wallace Tree, Elias Omega and Beyond. - Lloyd Allison; Arun Siddharth Konagurthu; Daniel F. Schmidt (DCC, 2021)

MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification. - Angus Dempster; Daniel F. Schmidt; Geoffrey I. Webb (KDD, 2021)

Minimum Message Length Inference of the Exponential Distribution with Type I Censoring. - Enes Makalic; Daniel Francis Schmidt (Entropy, 2021)

Smooth Perturbations for Time Series Adversarial Attacks. - Gautier Pialla; Hassan Ismail Fawaz; Maxime Devanne; Jonathan Weber; Lhassane Idoumghar; Pierre-Alain Muller; Christoph Bergmeir; Daniel F. Schmidt; Geoffrey I. Webb; Germain Forestier (PAKDD (1), 2022)

Sparse Horseshoe Estimation via Expectation-Maximisation. - Shu Yu Tew; Daniel F. Schmidt; Enes Makalic (ECML/PKDD (5), 2022)

An efficient algorithm for sampling from sink (x) for generating random correlation matrices. - Enes Makalic; Daniel F. Schmidt (Commun. Stat. Simul. Comput., 2022)

Minimum Message Length Inference of the Weibull Distribution with Complete and Censored Data. - Enes Makalic; Daniel F. Schmidt (AI (1), 2023)

Computing Marginal and Conditional Divergences between Decomposable Models with Applications. - Loong Kuan Lee; Geoffrey I. Webb; Daniel F. Schmidt; Nico Piatkowski (ICDM, 2023)

Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization. - Shu Yu Tew; Mario Boley; Daniel F. Schmidt (NeurIPS, 2023)

An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set. - 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 (CoRR, 2023)

Local and Global Trend Bayesian Exponential Smoothing Models. - Slawek Smyl; Christoph Bergmeir; Alexander Dokumentov; Erwin Wibowo; Daniel F. Schmidt (CoRR, 2023)

Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approach. - Xueying Long; Quang Bui; Grady Oktavian; Daniel F. Schmidt; Christoph Bergmeir; Rakshitha Godahewa; Seong Per Lee; Kaifeng Zhao 005; Paul Condylis (CoRR, 2023)

From Prediction to Action: The Critical Role of Proper Performance Estimation for Machine-Learning-Driven Materials Discovery. - Mario Boley; Felix Luong; Simon Teshuva; Daniel F. Schmidt; Lucas Foppa; Matthias Scheffler (CoRR, 2023)

Improved Neurophysiological Process Imaging Through Optimization of Kalman Filter Initial Conditions. - Yun Zhao; Felix Luong; Simon Teshuva; Andria Pelentritou; William Woods; David T. J. Liley; Daniel F. Schmidt; Mario Boley; Levin Kuhlmann (Int. J. Neural Syst., 2023)

A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping. - Benjamin Lucas; Charlotte Pelletier; Daniel F. Schmidt; Geoffrey I. Webb; François Petitjean (Mach. Learn., 2023)

Maximum likelihood estimation of the Weibull distribution with reduced bias. - Enes Makalic; Daniel F. Schmidt (Stat. Comput., 2023)

SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting. - Rakshitha Godahewa; Geoffrey I. Webb; Daniel F. Schmidt; Christoph Bergmeir (Mach. Learn., 2023)

Hydra: competing convolutional kernels for fast and accurate time series classification. - Angus Dempster; Daniel F. Schmidt; Geoffrey I. Webb (Data Min. Knowl. Discov., 2023)

Highly Scalable Time Series Classification for Very Large Datasets. - Angus Dempster; Chang Wei Tan; Lynn Miller; Navid Mohammadi Foumani; Daniel F. Schmidt; Geoffrey I. Webb (AALTD@ECML/PKDD, 2024)

Bayesian Parametric Proportional Hazards Regression with the Fused Lasso. - Enes Makalic; Daniel F. Schmidt (AI (2), 2024)

PADMr: Patch-Based Denoising Diffusion Probabilistic Model for Magnetic Resonance Imaging Reconstruction. - Mengting Huang; Thanh Nguyen-Duc; Martin Soellradl; Daniel F. Schmidt; Roland Bammer (ISBI, 2024)

Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data. - Angus Dempster; Geoffrey I. Webb; Daniel F. Schmidt (CoRR, 2024)

Fast Gibbs sampling for the local and global trend Bayesian exponential smoothing model. - Xueying Long; Daniel F. Schmidt; Christoph Bergmeir; Slawek Smyl (CoRR, 2024)

quant: a minimalist interval method for time series classification. - Angus Dempster; Daniel F. Schmidt; Geoffrey I. Webb (Data Min. Knowl. Discov., 2024)

Computing marginal and conditional divergences between decomposable models with applications in quantum computing and earth observation. - Loong Kuan Lee; Geoffrey I. Webb; Daniel F. Schmidt; Nico Piatkowski (Knowl. Inf. Syst., 2024)

Time series adversarial attacks: an investigation of smooth perturbations and defense approaches. - Gautier Pialla; Hassan Ismail Fawaz; Maxime Devanne; Jonathan Weber; Lhassane Idoumghar; Pierre-Alain Muller; Christoph Bergmeir; Daniel F. Schmidt; Geoffrey I. Webb; Germain Forestier (Int. J. Data Sci. Anal., 2025)

MONSTER: Monash Scalable Time Series Evaluation Repository. - Angus Dempster; Navid Mohammadi Foumani; Chang Wei Tan; Lynn Miller; Amish Mishra; Mahsa Salehi; Charlotte Pelletier; Daniel F. Schmidt; Geoffrey I. Webb (CoRR, 2025)

Improving Random Forests by Smoothing. - Ziyi Liu; Phuc Luong; Mario Boley; Daniel F. Schmidt (CoRR, 2025)

Efficient Parameter Estimation for Bayesian Network Classifiers using Hierarchical Linear Smoothing. - Connor Cooper; Geoffrey I. Webb; Daniel F. Schmidt (CoRR, 2025)

GRASP: Grouped Regression with Adaptive Shrinkage Priors. - Shu Yu Tew; Daniel F. Schmidt; Mario Boley (CoRR, 2025)

Fast Gibbs sampling for the local-seasonal-global trend Bayesian exponential smoothing model. - Xueying Long; Daniel F. Schmidt; Christoph Bergmeir; Slawek Smyl (Stat. Comput., 2025)