Search Results for author:
Found 10 papers, 7 papers with code
Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data
Logistic regression is a ubiquitous method for probabilistic classification.
Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approach
We are able to show the differences in characteristics of the e-commerce and brick-and-mortar retail datasets.
Computing Marginal and Conditional Divergences between Decomposable Models with Applications
Doing so tractably is non-trivial as we need to decompose the divergence between these distributions and therefore, require a decomposition over the marginal and conditional distributions of these models.
QUANT: A Minimalist Interval Method for Time Series Classification
We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a standard set of benchmark datasets using a single type of feature (quantiles), fixed intervals, and an 'off the shelf' classifier.
An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set
The measurement of progress using benchmarks evaluations is ubiquitous in computer science and machine learning.
Sparse Horseshoe Estimation via Expectation-Maximisation
A particular strength of our approach is that the M-step depends only on the form of the prior and it is independent of the form of the likelihood.
HYDRA: Competing convolutional kernels for fast and accurate time series classification
We present HYDRA, a simple, fast, and accurate dictionary method for time series classification using competing convolutional kernels, combining key aspects of both ROCKET and conventional dictionary methods.
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification
ROCKET achieves state-of-the-art accuracy with a fraction of the computational expense of most existing methods by transforming input time series using random convolutional kernels, and using the transformed features to train a linear classifier.
InceptionTime: Finding AlexNet for Time Series Classification
TSC is the area of machine learning tasked with the categorization (or labelling) of time series.
Log-Scale Shrinkage Priors and Adaptive Bayesian Global-Local Shrinkage Estimation
Simulations show that the adaptive log-$t$ procedure appears to always perform well, irrespective of the level of sparsity or signal-to-noise ratio of the underlying model.
