Private data analysis suffers a costly curse of dimensionality. However, the data often has an underlying low-dimensional structure. For example, when optimizing via gradient descent, the gradients often lie in
Review of Low-Voltage Load Forecasting: Methods, Applications, and Recommendations. (arXiv:2106.00006v1 [stat.OT])
The increased digitalisation and monitoring of the energy system opens up numerous opportunities % and solutions which can help to decarbonise the energy system. Applications on low voltage (LV), localised
PUDLE: Implicit Acceleration of Dictionary Learning by Backpropagation. (arXiv:2106.00058v1 [cs.LG])
The dictionary learning problem, representing data as a combination of few atoms, has long stood as a popular method for learning representations in statistics and signal processing. The most popular
Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data. (arXiv:2106.00072v1 [stat.ML])
Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to
Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference. (arXiv:2106.00075v1 [stat.ML])
Bayesian phylogenetic inference is often conducted via local or sequential search over topologies and branch lengths using algorithms such as random-walk Markov chain Monte Carlo (MCMC) or Combinatorial Sequential Monte
Generalized AdaGrad (G-AdaGrad) and Adam: A State-Space Perspective. (arXiv:2106.00092v1 [cs.LG])
Accelerated gradient-based methods are being extensively used for solving non-convex machine learning problems, especially when the data points are abundant or the available data is distributed across several agents. Two
On some properties of the bimodal normal distribution and its bivariate version. (arXiv:2106.00097v1 [math.ST])
In this work, we derive some novel properties of the bimodal normal distribution. Some of its mathematical properties are examined. We provide a formal proof for the bimodality and assess
Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models. (arXiv:2106.00120v1 [cs.LG])
Probabilistic deep learning is deep learning that accounts for uncertainty, both model uncertainty and data uncertainty. It is based on the use of probabilistic models and deep neural networks. We
Explanations for Monotonic Classifiers. (arXiv:2106.00154v1 [cs.LG])
In many classification tasks there is a requirement of monotonicity. Concretely, if all else remains constant, increasing (resp. decreasing) the value of one or more features must not decrease (resp.
Median bias of M-estimators. (arXiv:2106.00164v1 [math.ST])
In this note, we derive bounds on the median bias of univariate M-estimators under mild regularity conditions. These requirements are not sufficient to imply convergence in distribution of the M-estimators.
