Regularization Paths for Generalized Linear Models via Coordinate Descent

We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include âÂÂ_1 (the lasso), âÂÂ_2 (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.

Austin L. Staley - An Appreciation

As judges, Austin Staley and I have grown up together. Eighteen years ago, at about the same time, we came to the bench of the United States Court of Appeals for the Third Circuit. We have been colleagues ever since. This has meant continuing close association in and shared responsibility for the decision of thousands of cases, affecting the interests large and small of uncounted litigants. In the long course of such association men learn each other\u27s measure, professionally and, more broadly, as human beings. And when, as in the case of Judge Staley, so much that is admirable is revealed, it becomes a happy privilege to write of the judge and the man as he has disclosed himself to his colleagues

Tracing the Evolution of Physics on the Backbone of Citation Networks

Many innovations are inspired by past ideas in a non-trivial way. Tracing these origins and identifying scientific branches is crucial for research inspirations. In this paper, we use citation relations to identify the descendant chart, i.e. the family tree of research papers. Unlike other spanning trees which focus on cost or distance minimization, we make use of the nature of citations and identify the most important parent for each publication, leading to a tree-like backbone of the citation network. Measures are introduced to validate the backbone as the descendant chart. We show that citation backbones can well characterize the hierarchical and fractal structure of scientific development, and lead to accurate classification of fields and sub-fields.Comment: 6 pages, 5 figure

Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent

We introduce a pathwise algorithm for the Cox proportional hazards model, regularized by convex combinations of l_1 and l_2 penalties (elastic net). Our algorithm fits via cyclical coordinate descent, and employs warm starts to find a solution along a regularization path. We demonstrate the efficacy of our algorithm on real and simulated data sets, and find considerable speedup between our algorithm and competing methods.

Towards compact and portable sub-kHz AlGaInP semiconductor disk lasers for cold atom experiments

Stable lasers are crucial for experiments that target narrow atomic transitions (kHz down to Hz linewidth). Such transitions are used, for example, to cool and trap atoms in magneto-optical traps down to the μK regime, in particular for optical clock systems. In this context, semiconductor disk lasers (SDLs) have demonstrated great potential due to their spectral flexibility, high brightness, and low intensity and frequency noise. Here we report our recent progress in frequency stabilisation of an AlGaInP SDL designed for ultra-narrow linewidth at 689 nm for a strontium clock, achieving sub-kHz RMS frequency noise, relative to a reference Fabry-Perot resonator

Sub-kHz linewidth VECSEL for cold atoms experiments

We report sub-kHz linewidth operation of a frequency-stabilized, AlGaInP-based vertical-external-cavity surface-emitting laser (VECSEL) at 689nm, suitable for Strontium cold atom experiments. 170mW was emitted with linewidth ≤200Hz, determined via an optical beat note measurement

Expected exponential loss for gaze-based video and volume ground truth annotation

Many recent machine learning approaches used in medical imaging are highly reliant on large amounts of image and ground truth data. In the context of object segmentation, pixel-wise annotations are extremely expensive to collect, especially in video and 3D volumes. To reduce this annotation burden, we propose a novel framework to allow annotators to simply observe the object to segment and record where they have looked at with a \$200 eye gaze tracker. Our method then estimates pixel-wise probabilities for the presence of the object throughout the sequence from which we train a classifier in semi-supervised setting using a novel Expected Exponential loss function. We show that our framework provides superior performances on a wide range of medical image settings compared to existing strategies and that our method can be combined with current crowd-sourcing paradigms as well.Comment: 9 pages, 5 figues, MICCAI 2017 - LABELS Worksho

Estimating the Expected Value of Partial Perfect Information in Health Economic Evaluations using Integrated Nested Laplace Approximation

The Expected Value of Perfect Partial Information (EVPPI) is a decision-theoretic measure of the "cost" of parametric uncertainty in decision making used principally in health economic decision making. Despite this decision-theoretic grounding, the uptake of EVPPI calculations in practice has been slow. This is in part due to the prohibitive computational time required to estimate the EVPPI via Monte Carlo simulations. However, recent developments have demonstrated that the EVPPI can be estimated by non-parametric regression methods, which have significantly decreased the computation time required to approximate the EVPPI. Under certain circumstances, high-dimensional Gaussian Process regression is suggested, but this can still be prohibitively expensive. Applying fast computation methods developed in spatial statistics using Integrated Nested Laplace Approximations (INLA) and projecting from a high-dimensional into a low-dimensional input space allows us to decrease the computation time for fitting these high-dimensional Gaussian Processes, often substantially. We demonstrate that the EVPPI calculated using our method for Gaussian Process regression is in line with the standard Gaussian Process regression method and that despite the apparent methodological complexity of this new method, R functions are available in the package BCEA to implement it simply and efficiently