Publications

DEUP: Direct Epistemic Uncertainty Prediction
Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes with more evidence. While existing work focuses on… (see more) using the variance of the Bayesian posterior due to parameter uncertainty as a measure of epistemic uncertainty, we argue that this does not capture the part of lack of knowledge induced by model misspecification. We discuss how the excess risk, which is the gap between the generalization error of a predictor and the Bayes predictor, is a sound measure of epistemic uncertainty which captures the effect of model misspecification. We thus propose a principled framework for directly estimating the excess risk by learning a secondary predictor for the generalization error and subtracting an estimate of aleatoric uncertainty, i.e., intrinsic unpredictability. We discuss the merits of this novel measure of epistemic uncertainty, and highlight how it differs from variance-based measures of epistemic uncertainty and addresses its major pitfall. Our framework, Direct Epistemic Uncertainty Prediction (DEUP) is particularly interesting in interactive learning environments, where the learner is allowed to acquire novel examples in each round. Through a wide set of experiments, we illustrate how existing methods in sequential model optimization can be improved with epistemic uncertainty estimates from DEUP, and how DEUP can be used to drive exploration in reinforcement learning. We also evaluate the quality of uncertainty estimates from DEUP for probabilistic image classification and predicting synergies of drug combinations.
Interpersonal attunement in social interactions: from collective psychophysiology to inter-personalized psychiatry and beyond
Dimitris Bolis
Leonhard Schilbach
In this article, we analyse social interactions, drawing on diverse points of views, ranging from dialectics, second-person neuroscience and… (see more) enactivism to dynamical systems, active inference and machine learning. To this end, we define interpersonal attunement as a set of multi-scale processes of building up and materializing social expectations—put simply, anticipating and interacting with others and ourselves. While cultivating and negotiating common ground, via communication and culture-building activities, are indispensable for the survival of the individual, the relevant multi-scale mechanisms have been largely considered in isolation. Here, collective psychophysiology, we argue, can lend itself to the fine-tuned analysis of social interactions, without neglecting the individual. On the other hand, an interpersonal mismatch of expectations can lead to a breakdown of communication and social isolation known to negatively affect mental health. In this regard, we review psychopathology in terms of interpersonal misattunement, conceptualizing psychiatric disorders as disorders of social interaction, to describe how individual mental health is inextricably linked to social interaction. By doing so, we foresee avenues for an inter-personalized psychiatry, which moves from a static spectrum of disorders to a dynamic relational space, focusing on how the multi-faceted processes of social interaction can help to promote mental health. This article is part of the theme issue ‘Concepts in interaction: social engagement and inner experiences’.
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization
MAHDI HAGHIFAM
Borja Rodr'iguez-G'alvez
Ragnar Thobaben
Mikael Skoglund
Daniel M. Roy
Characterization of inpaint residuals in interferometric measurements of the epoch of reionization
Michael Pagano
Jing Liu
Adrian Liu
Nicholas S. Kern
Aaron Ewall-Wice
Philip Bull
Robert Pascua
Zara Abdurashidova
Tyrone Adams
James E. Aguirre
Paul Alexander
Zaki S. Ali
Rushelle Baartman
Yanga Balfour
Adam P. Beardsley
Gianni Bernardi
Tashalee S. Billings
Judd D. Bowman
Richard F. Bradley … (see 58 more)
Jacob Burba
Steven Carey
Chris L. Carilli
Carina Cheng
David R. DeBoer
Eloy de Lera Acedo
Matt Dexter
Joshua S. Dillon
Nico Eksteen
John Ely
Nicolas Fagnoni
Randall Fritz
Steven R. Furlanetto
Kingsley Gale-Sides
Brian Glendenning
Deepthi Gorthi
Bradley Greig
Jasper Grobbelaar
Ziyaad Halday
Bryna J. Hazelton
Jacqueline N. Hewitt
Jack Hickish
Daniel C. Jacobs
Austin Julius
MacCalvin Kariseb
Joshua Kerrigan
Piyanat Kittiwisit
Saul A. Kohn
Matthew Kolopanis
Adam Lanman
Paul La Plante
Anita Loots
David Harold Edward MacMahon
Lourence Malan
Cresshim Malgas
Keith Malgas
Bradley Marero
Zachary E. Martinot
Andrei Mesinger
Mathakane Molewa
Miguel F. Morales
Tshegofalang Mosiane
Abraham R. Neben
Bojan Nikolic
Hans Nuwegeld
Aaron R. Parsons
Nipanjana Patra
Samantha Pieterse
Nima Razavi-Ghods
James Robnett
Kathryn Rosie
Peter Sims
Craig Smith
Hilton Swarts
Nithyanandan Thyagarajan
Pieter van Wyngaarden
Peter K. G. Williams
Haoxuan Zheng
Radio Frequency Interference (RFI) is one of the systematic challenges preventing 21cm interferometric instruments from detecting the Epoch … (see more)of Reionization. To mitigate the effects of RFI on data analysis pipelines, numerous inpaint techniques have been developed to restore RFI corrupted data. We examine the qualitative and quantitative errors introduced into the visibilities and power spectrum due to inpainting. We perform our analysis on simulated data as well as real data from the Hydrogen Epoch of Reionization Array (HERA) Phase 1 upper limits. We also introduce a convolutional neural network that capable of inpainting RFI corrupted data in interferometric instruments. We train our network on simulated data and show that our network is capable at inpainting real data without requiring to be retrained. We find that techniques that incorporate high wavenumbers in delay space in their modeling are best suited for inpainting over narrowband RFI. We also show that with our fiducial parameters Discrete Prolate Spheroidal Sequences (DPSS) and CLEAN provide the best performance for intermittent ``narrowband'' RFI while Gaussian Progress Regression (GPR) and Least Squares Spectral Analysis (LSSA) provide the best performance for larger RFI gaps. However we caution that these qualitative conclusions are sensitive to the chosen hyperparameters of each inpainting technique. We find these results to be consistent in both simulated and real visibilities. We show that all inpainting techniques reliably reproduce foreground dominated modes in the power spectrum. Since the inpainting techniques should not be capable of reproducing noise realizations, we find that the largest errors occur in the noise dominated delay modes. We show that in the future, as the noise level of the data comes down, CLEAN and DPSS are most capable of reproducing the fine frequency structure in the visibilities of HERA data.
Language Decision Transformers with Exponential Tilt for Interactive Text Environments
Nicolas Gontier
Pau Rodríguez
Issam Hadj Laradji
Christopher Pal
Restoring the missing person to personalized medicine and precision psychiatry
Ana Gómez-Carrillo
Vincent Paquin
Laurence J. Kirmayer
Precision psychiatry has emerged as part of the shift to personalized medicine and builds on frameworks such as the U.S. National Institute … (see more)of Mental Health Research Domain Criteria (RDoC), multilevel biological “omics” data and, most recently, computational psychiatry. The shift is prompted by the realization that a one-size-fits all approach is inadequate to guide clinical care because people differ in ways that are not captured by broad diagnostic categories. One of the first steps in developing this personalized approach to treatment was the use of genetic markers to guide pharmacotherapeutics based on predictions of pharmacological response or non-response, and the potential risk of adverse drug reactions. Advances in technology have made a greater degree of specificity or precision potentially more attainable. To date, however, the search for precision has largely focused on biological parameters. Psychiatric disorders involve multi-level dynamics that require measures of phenomenological, psychological, behavioral, social structural, and cultural dimensions. This points to the need to develop more fine-grained analyses of experience, self-construal, illness narratives, interpersonal interactional dynamics, and social contexts and determinants of health. In this paper, we review the limitations of precision psychiatry arguing that it cannot reach its goal if it does not include core elements of the processes that give rise to psychopathological states, which include the agency and experience of the person. Drawing from contemporary systems biology, social epidemiology, developmental psychology, and cognitive science, we propose a cultural-ecosocial approach to integrating precision psychiatry with person-centered care.
Spectroscopy of CASSOWARY gravitationally-lensed galaxies in SDSS: characterisation of an extremely bright reionization-era analog at z = 1.42
Ramesh Mainali
Daniel P Stark
Tucker Jones
Richard S Ellis
Jane R Rigby
We present new observations of sixteen bright (r = 19 − 21) gravitationally lensed galaxies at z ≃ 1 − 3 selected from the CASSOWARY s… (see more)urvey. Included in our sample is the z = 1.42 galaxy CSWA-141, one of the brightest known reionization-era analogs at high redshift (g=20.5), with a large sSFR (31.2 Gyr−1) and an [OIII]+Hβ equivalent width (EW[OIII] + Hβ=730 Å) that is nearly identical to the average value expected at z ≃ 7 − 8. In this paper, we investigate the rest-frame UV nebular line emission in our sample with the goal of understanding the factors that regulate strong CIII] emission. Whereas most of the sources in our sample show weak UV line emission, we find elevated CIII] in the spectrum of CSWA-141 (EWCIII]=4.6±1.9 Å) together with detections of other prominent emission lines (OIII], Si III], Fe II⋆, Mg II). We compare the rest-optical line properties of high redshift galaxies with strong and weak CIII] emission, and find that systems with the strongest UV line emission tend to have young stellar populations and nebular gas that is moderately metal-poor and highly ionized, consistent with trends seen at low and high redshift. The brightness of CSWA-141 enables detailed investigation of the extreme emission line galaxies which become common at z > 6. We find that gas traced by the CIII] doublet likely probes higher densities than that traced by [OII] and [SII]. Characterisation of the spectrally resolved Mg II emission line and several low ionization absorption lines suggests neutral gas around the young stars is likely optically thin, potentially facilitating the escape of ionizing radiation.
DeltaShield: Information Theory for Human- Trafficking Detection
Catalina Vajiac
Meng-Chieh Lee
Sacha Lévy
Namyong Park
Andreas Olligschlaeger
Cara Jones
Christos Faloutsos
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection
Federica Granese
Marco Romanelli
Siddharth Garg
AmbieGen tool at the SBST 2022 Tool Competition
Dmytro Humeniuk
Giuliano Antoniol
AmbieGen is a tool for generating test cases for cyber-physical systems (CPS). In the context of SBST 2022 CPS tool competition, it has been… (see more) adapted to generating virtual roads to test a car lane keeping assist system. AmbieGen leverages a two objective NSGA-II algorithm to produce the test cases. It has achieved the highest final score, accounting for the test case efficiency, effectiveness and diversity in both testing configurations.
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling
Lucas Berry
A Survey on Compositional Generalization in Applications
Djallel Bouneffouf