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ORCIDDaniel F. Schmidt
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- affiliation: Centre for MEGA Epidemiology, University of Melbourne, Australia
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2020 – today
- 2024
[i16]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 abs/2401.15610 (2024)- 2023
- [j14]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): 1779-1805 (2023) - [j13]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): 2350024:1-2350024:20 (2023) - [j12]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): 1941-1973 (2023) - [j11]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): 2555-2591 (2023) - [j10]Enes Makalic, Daniel F. Schmidt:
Maximum likelihood estimation of the Weibull distribution with reduced bias. Stat. Comput. 33(3): 69 (2023) - [c32]Enes Makalic, Daniel F. Schmidt:
Minimum Message Length Inference of the Weibull Distribution with Complete and Censored Data. AI (1) 2023: 291-303 - [c31]Loong Kuan Lee, Geoffrey I. Webb, Daniel F. Schmidt, Nico Piatkowski:
Computing Marginal and Conditional Divergences between Decomposable Models with Applications. ICDM 2023: 239-248 - [c30]Shu Yu Tew, Mario Boley, Daniel F. Schmidt:
Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization. NeurIPS 2023 - [i15]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 abs/2305.11921 (2023) - [i14]Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb:
QUANT: A Minimalist Interval Method for Time Series Classification. CoRR abs/2308.00928 (2023) - [i13]Slawek Smyl, Christoph Bergmeir, Alexander Dokumentov, Erwin Wibowo, Daniel F. Schmidt:
Local and Global Trend Bayesian Exponential Smoothing Models. CoRR abs/2309.13950 (2023) - [i12]Loong Kuan Lee, Geoffrey I. Webb, Daniel F. Schmidt, Nico Piatkowski:
Computing Marginal and Conditional Divergences between Decomposable Models with Applications. CoRR abs/2310.09129 (2023) - [i11]Shu Yu Tew, Mario Boley, Daniel F. Schmidt:
Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization. CoRR abs/2310.18860 (2023) - [i10]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 abs/2311.00993 (2023) - [i9]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 abs/2311.15549 (2023) - 2022
- [j9]Enes Makalic, Daniel F. Schmidt:
An efficient algorithm for sampling from sink (x) for generating random correlation matrices. Commun. Stat. Simul. Comput. 51(5): 2731-2735 (2022) - [c29]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: 485-496 - [c28]Shu Yu Tew, Daniel F. Schmidt, Enes Makalic:
Sparse Horseshoe Estimation via Expectation-Maximisation. ECML/PKDD (5) 2022: 123-139 - [i8]Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb:
HYDRA: Competing convolutional kernels for fast and accurate time series classification. CoRR abs/2203.13652 (2022) - [i7]Shu Yu Tew, Daniel F. Schmidt, Enes Makalic:
Sparse Horseshoe Estimation via Expectation-Maximisation. CoRR abs/2211.03248 (2022) - [i6]Rakshitha Godahewa, Geoffrey I. Webb, Daniel F. Schmidt, Christoph Bergmeir:
SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting. CoRR abs/2211.08661 (2022) - 2021
- [j8]Enes Makalic, Daniel Francis Schmidt:
Minimum Message Length Inference of the Exponential Distribution with Type I Censoring. Entropy 23(11): 1439 (2021) - [c27]Lloyd Allison, Arun Siddharth Konagurthu, Daniel F. Schmidt:
On Universal Codes for Integers: Wallace Tree, Elias Omega and Beyond. DCC 2021: 313-322 - [c26]Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb:
MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification. KDD 2021: 248-257 - 2020
- [j7]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): 1936-1962 (2020) - [c25]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: 1074-1077 - [i5]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 abs/2005.11930 (2020) - [i4]Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb:
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification. CoRR abs/2012.08791 (2020)
2010 – 2019
- 2019
- [c24]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: 1-4 - [c23]Daniel F. Schmidt, Enes Makalic:
Bayesian Generalized Horseshoe Estimation of Generalized Linear Models. ECML/PKDD (2) 2019: 598-613 - [i3]Lloyd Allison, Arun Siddharth Konagurthu, Daniel F. Schmidt:
On Universal Codes for Integers: Wallace Tree, Elias Omega and Variations. CoRR abs/1906.05004 (2019) - [i2]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 abs/1909.04939 (2019) - 2018
- [i1]Daniel F. Schmidt, Enes Makalic:
Adaptive Bayesian Shrinkage Estimation Using Log-Scale Shrinkage Priors. CoRR abs/1801.02321 (2018) - 2017
- [c22]Daniel F. Schmidt, Enes Makalic:
Robust Lasso Regression with Student-t Residuals. Australasian Conference on Artificial Intelligence 2017: 365-374 - 2016
- [j6]Enes Makalic, Daniel F. Schmidt:
A Simple Sampler for the Horseshoe Estimator. IEEE Signal Process. Lett. 23(1): 179-182 (2016) - [c21]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: 229-240 - [c20]Enes Makalic, Daniel F. Schmidt, John L. Hopper:
Bayesian Robust Regression with the Horseshoe+ Estimator. Australasian Conference on Artificial Intelligence 2016: 429-440 - [c19]Daniel F. Schmidt, Enes Makalic, John L. Hopper:
Approximating Message Lengths of Hierarchical Bayesian Models Using Posterior Sampling. Australasian Conference on Artificial Intelligence 2016: 482-494 - 2015
- [j5]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) - 2013
- [c18]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: 80-85 - [c17]Daniel F. Schmidt, Enes Makalic:
Minimum Message Length Ridge Regression for Generalized Linear Models. Australasian Conference on Artificial Intelligence 2013: 408-420 - 2012
- [j4]Daniel Francis Schmidt, Enes Makalic:
The Consistency of MDL for Linear Regression Models With Increasing Signal-to-Noise Ratio. IEEE Trans. Signal Process. 60(3): 1508-1510 (2012) - [c16]Daniel F. Schmidt, Enes Makalic:
Minimum Message Length Inference and Mixture Modelling of Inverse Gaussian Distributions. Australasian Conference on Artificial Intelligence 2012: 672-682 - [c15]Enes Makalic, Daniel F. Schmidt:
MML Logistic Regression with Translation and Rotation Invariant Priors. Australasian Conference on Artificial Intelligence 2012: 878-889 - [c14]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: 1258-1261 - 2011
- [j3]Daniel Francis Schmidt, Enes Makalic:
Estimating the Order of an Autoregressive Model Using Normalized Maximum Likelihood. IEEE Trans. Signal Process. 59(2): 479-487 (2011) - [c13]Enes Makalic, Daniel Francis Schmidt:
Logistic Regression with the Nonnegative Garrote. Australasian Conference on Artificial Intelligence 2011: 82-91 - [c12]Enes Makalic, Daniel Francis Schmidt:
A Simple Bayesian Algorithm for Feature Ranking in High Dimensional Regression Problems. Australasian Conference on Artificial Intelligence 2011: 223-230 - [c11]Enes Makalic, Daniel F. Schmidt:
Minimum Message Length Analysis of the Behrens-Fisher Problem. Algorithmic Probability and Friends 2011: 250-260 - [c10]Daniel F. Schmidt:
Minimum Message Length Order Selection and Parameter Estimation of Moving Average Models. Algorithmic Probability and Friends 2011: 327-338 - 2010
- [j2]Enes Makalic, Daniel Francis Schmidt:
Fast Computation of the Kullback-Leibler Divergence and Exact Fisher Information for the First-Order Moving Average Model. IEEE Signal Process. Lett. 17(4): 391-393 (2010) - [c9]Enes Makalic, Daniel Francis Schmidt:
Review of Modern Logistic Regression Methods with Application to Small and Medium Sample Size Problems. Australasian Conference on Artificial Intelligence 2010: 213-222 - [c8]Daniel Francis Schmidt, Enes Makalic:
The Behaviour of the Akaike Information Criterion When Applied to Non-nested Sequences of Models. Australasian Conference on Artificial Intelligence 2010: 223-232
2000 – 2009
- 2009
- [j1]Daniel Francis Schmidt, Enes Makalic:
Universal models for the exponential distribution. IEEE Trans. Inf. Theory 55(7): 3087-3090 (2009) - [c7]Daniel Francis Schmidt, Enes Makalic:
MML Invariant Linear Regression. Australasian Conference on Artificial Intelligence 2009: 312-321 - [c6]Fabian Bohnert, Daniel Francis Schmidt, Ingrid Zukerman:
Spatial Processes for Recommender Systems. IJCAI 2009: 2022-2027 - [c5]Fabian Bohnert, Ingrid Zukerman, Daniel Francis Schmidt:
Using Gaussian Spatial Processes to Model and Predict Interests in Museum Exhibits. ITWP 2009 - [c4]Daniel Francis Schmidt, Ingrid Zukerman, David W. Albrecht:
Assessing the Impact of Measurement Uncertainty on User Models in Spatial Domains. UMAP 2009: 210-222 - 2008
- [c3]Enes Makalic, Ingrid Zukerman, Michael Niemann, Daniel Francis Schmidt:
A Probabilistic Model for Understanding Composite Spoken Descriptions. PRICAI 2008: 750-759 - 2005
- [c2]Daniel F. Schmidt, Andrew P. Paplinski, Gordon S. Lowe:
Adaptive Control of Hydraulic Systems with MML Inferred RBF Networks. ICRA 2005: 2368-2374 - 2004
- [c1]Daniel F. Schmidt, Gordon S. Lowe, Andrew P. Paplinski:
On the Design of a Hydraulically Actuated Finger for Dexterous Manipulation. ICRA 2004: 1239-1244
Coauthor Index
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last updated on 2024-03-03 00:11 CET by the dblp team
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