Bandit optimisation of functions in the Mat\'ern kernel RKHS

We consider the problem of optimising functions in the reproducing kernel Hilbert space (RKHS) of a Mat\'ern kernel with smoothness parameter $\nu$ over the domain $[0,1]^d$ under noisy bandit feedback. Our contribution, the $\pi$-GP-UCB algorithm, is the first practical approach with guaranteed sublinear regret for all $\nu>1$ and $d \geq 1$. Empirical validation suggests better performance and drastically improved computational scalablity compared with its predecessor, Improved GP-UCB.Comment: AISTATS 2020, camera read

Exploration via linearly perturbed loss minimisation

We introduce exploration via linear loss perturbations (EVILL), a randomised exploration method for structured stochastic bandit problems that works by solving for the minimiser of a linearly perturbed regularised negative log-likelihood function. We show that, for the case of generalised linear bandits, EVILL reduces to perturbed history exploration (PHE), a method where exploration is done by training on randomly perturbed rewards. In doing so, we provide a simple and clean explanation of when and why random reward perturbations give rise to good bandit algorithms. With the data-dependent perturbations we propose, not present in previous PHE-type methods, EVILL is shown to match the performance of Thompson-sampling-style parameter-perturbation methods, both in theory and in practice. Moreover, we show an example outside of generalised linear bandits where PHE leads to inconsistent estimates, and thus linear regret, while EVILL remains performant. Like PHE, EVILL can be implemented in just a few lines of code

Sensation Seeking Impact on Skin Conductance Measures of Deception and Memory

We sought to determine whether sensation seeking would differentially predict measures of memory and deception (concealing information) as indexed by behavioral (response time, accuracy) and autonomic (skin conductance level) markers in a sample of college students. Participants were randomly assigned to a mock-crime group or an innocent-errand group. Both groups were trained to complete a task requiring the copying of documents from a secure location; the difference was the mock-crime group broke into the office whereas the errand group was given permission to enter the room and access the documents. After being trained to perform the crime or errand task, participants watched a video that showed a first-person account of the crime/errand. Participants in the mock-crime group were told to conceal their knowledge of the task during an examination on the next day but to be truthful otherwise. Participants in the errand group were truthful to all items during the examination. The examination involved a recognition task that included words that were (a) scenario-related, (b) personally familiar words gathered from participants\u27 responses to questions about their lives, and (c) irrelevant words not related to the scenario nor their personal lives. Response accuracy differed for the mock-crime and errand groups, but not as a function of sensation seeking. Skin conductance responses revealed that high and low sensation seeking impacted the mock-crime and errand groups differently to personally familiar and irrelevant words, but not to scenario-related words. Findings show that determining whether individuals are high or low sensation seekers prior to assessing deception may be useful for establishing criteria for detecting deception. These results also demonstrate the need to consider personality traits in both detecting deception and understanding the biological correlates of deception

Decay of an active GPCR: Conformational dynamics govern agonist rebinding and persistence of an active, yet empty, receptor state

G protein-coupled receptors (GPCRs) represent a major pharmaceutical drug target. However, one exception has been the visual photoreceptor rhodopsin, long considered “different” due to its covalently bound, light-sensitive retinal ligands. Here we demonstrate that, in contrast to prior assumptions, release of the agonist all-trans retinal (ATR) is not an irreversible process. Instead, during decay of the active species, ATR can rebind any rhodopsin remaining in an active-like conformation, and this active-like state can transiently persist even after agonist dissociation. These insights demonstrate rhodopsin behaves like other diffusible ligand-binding GPCRs and raise the possibility of treating rhodopsin by pharmaceutical agents

COVID-19 Forced Social Distancing and Isolation: A Multi-Perspective Experience

The article is combined of six chapters authored by these who voiced their experiences with social distancing during the COVID-19 pandemics in various contexts, but mostly centered on psychological, sociological, and ethical aspects. Authors, mostly psychologists and philosophers, were invited to describe their perspectives on the sense and practice of social distancing in times of pandemics. Their reflections seek to demonstrate various perspectives related to subjects’ novel self-experience, social situatedness, and their dealing with conventions and habits altered through the pandemics. As “the owl of Minerva takes its flight only when the shades of night are gathering” (Hegel), there is no conclusion in this article. It rather encourages other authors to reflect on the nearly global, still lasting phenomenon.The article is combined of six chapters authored by these who voiced their experiences with social distancing during the COVID-19 pandemics in various contexts, but mostly centered on psychological, sociological, and ethical aspects. Authors, mostly psychologists and philosophers, were invited to describe their perspectives on the sense and practice of social distancing in times of pandemics. Their reflections seek to demonstrate various perspectives related to subjects’ novel self-experience, social situatedness, and their dealing with conventions and habits altered through the pandemics. As “the owl of Minerva takes its flight only when the shades of night are gathering” (Hegel), there is no conclusion in this article. It rather encourages other authors to reflect on the nearly global, still lasting phenomenon

Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent

Gaussian processes are a powerful framework for quantifying uncertainty and for sequential decision-making but are limited by the requirement of solving linear systems. In general, this has a cubic cost in dataset size and is sensitive to conditioning. We explore stochastic gradient algorithms as a computationally efficient method of approximately solving these linear systems: we develop low-variance optimization objectives for sampling from the posterior and extend these to inducing points. Counterintuitively, stochastic gradient descent often produces accurate predictions, even in cases where it does not converge quickly to the optimum. We explain this through a spectral characterization of the implicit bias from non-convergence. We show that stochastic gradient descent produces predictive distributions close to the true posterior both in regions with sufficient data coverage, and in regions sufficiently far away from the data. Experimentally, stochastic gradient descent achieves state-of-the-art performance on sufficiently large-scale or ill-conditioned regression tasks. Its uncertainty estimates match the performance of significantly more expensive baselines on a large-scale Bayesian optimization task

Correcting for enzyme immunoassay changes in long term monitoring studies

Enzyme immunoassays (EIAs) are a common tool for measuring steroid hormones in wildlife due to their low cost, commercial availability, and rapid results. Testing technologies improve continuously, sometimes requiring changes in protocols or crucial assay components. Antibody replacement between EIA kits can cause differences in EIA sensitivity, which can hinder monitoring hormone concentration over time. The antibody in a common cortisol EIA kit used for long-term monitoring of stress in wildlife was replaced in 2014, causing differences in cross reactivity and standard curve concentrations. Therefore, the objective of this study was to develop a method to standardize results following changes in EIA sensitivity. We validated this method using cortisol concentrations measured in the hair of brown bears (Ursus arctos). • We used a simple linear regression to model the relationship between cortisol concentrations using kit 1 and kit 2. • We found a linear relationship between the two kits (R2 = 0.85) and used the regression equation (kit2 = (0.98 × kit1) + 1.65) to predict cortisol concentrations in re-measured samples. • Mean predicted percent error was 16% and 72% of samples had a predicted percent error <20%, suggesting that this method is well-suited for correcting changes in EIA sensitivity.publishedVersio