Publications

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
Gintare Karolina Dziugaite
2023-02-12
Proceedings of The 34th International Conference on Algorithmic Learning Theory (publié)
doi.org
proceedings.mlr.press
Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL
David Earl Hostallero
Lixuan Wei
Liewei Wang
Junmei Cairns
Amin Emad
Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals … (voir plus)of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.
2023-02-10
Genomics Proteomics & Bioinformatics (publié)
doi.org
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
Siamak Ravanbakhsh
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 … (voir 58 de plus)
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 … (voir plus)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.
2023-02-09
Monthly Notices of the Royal Astronomical Society (publié)
doi.org
arxiv.org
Language Decision Transformers with Exponential Tilt for Interactive Text Environments
Nicolas Gontier
Pau Rodríguez
Issam Hadj Laradji
David Vázquez
Christopher Pal
2023-02-09
ArXiv (prépublication)
openreview.net
openreview.net
Restoring the missing person to personalized medicine and precision psychiatry
Ana Gómez-Carrillo
Vincent Paquin
Guillaume Dumas
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 … (voir plus)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.
2023-02-08
Frontiers in Neuroscience (publié)
doi.org
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
Yashar Hezaveh
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… (voir plus)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.
2023-02-07
Monthly Notices of the Royal Astronomical Society (publié)
doi.org
arxiv.org
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection
Federica Granese
Marco Romanelli
Siddharth Garg
Pablo Piantanida
2023-02-03
ArXiv (prépublication)
doi.org
arxiv.org
AmbieGen tool at the SBST 2022 Tool Competition
Dmytro Humeniuk
Giuliano Antoniol
Foutse Khomh
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… (voir plus) 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.
2023-02-02
Proceedings of the 15th Workshop on Search-Based Software Testing (publié)
doi.org
Challenges in machine learning application development
Md Saidur Rahman
Foutse Khomh
Emilio Rivera
Yann-Gaël Guéhéneuc
Bernd Lehnert
SAP is the market leader in enterprise application software offering an end-to-end suite of applications and services to enable their custom… (voir plus)ers worldwide to operate their business. Especially, retail customers of SAP deal with millions of sales transactions for their day-to-day business. Transactions are created during retail sales at the point of sale (POS) terminals and those transactions are then sent to some central servers for validations and other business operations. A considerable proportion of the retail transactions may have inconsistencies or anomalies due to many technical and human errors. SAP provides an automated process for error detection but still requires a manual process by dedicated employees using workbench software for correction. However, manual corrections of these errors are time-consuming, labor-intensive, and might be prone to further errors due to incorrect modifications. Thus, automated detection and correction of transaction errors are very important regarding their potential business values and the improvement in the business workflow. In this paper, we report on our experience from a project where we develop an AI-based system to automatically detect transaction errors and propose corrections. We identify and discuss the challenges that we faced during this collaborative research and development project, from two distinct perspectives: Software Engineering and Machine Learning. We report on our experience and insights from the project with guidelines for the identified challenges. We collect developers’ feedback for qualitative analysis of our findings. We believe that our findings and recommendations can help other researchers and practitioners embarking into similar endeavours. CCS CONCEPTS • Software and its engineering → Programming teams.
2023-02-02
Proceedings of the 1st Workshop on Software Engineering for Responsible AI (publié)
doi.org
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling
Lucas Berry
David Meger
2023-02-01
ArXiv (prépublication)
doi.org
arxiv.org
A Survey on Compositional Generalization in Applications
Baihan Lin
Djallel Bouneffouf
Irina Rish
2023-02-01
ArXiv (prépublication)
doi.org
arxiv.org
Critic Sequential Monte Carlo
Vasileios Lioutas
Jonathan Wilder Lavington
Justice Sefas
Matthew Niedoba
Yunpeng Liu
Berend Zwartsenberg
Setareh Dabiri
Frank N. Wood
Adam Ścibior
We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q fun… (voir plus)ction heuristic factors. These heuristic factors, obtained from parametric approximations of the marginal likelihood ahead, more effectively guide SMC towards the desired target distribution, which is particularly helpful for planning in environments with hard constraints placed sparsely in time. Compared with previous work, we modify the placement of such heuristic factors, which allows us to cheaply propose and evaluate large numbers of putative action particles, greatly increasing inference and planning efficiency. CriticSMC is compatible with informative priors, whose density function need not be known, and can be used as a model-free control algorithm. Our experiments on collision avoidance in a high-dimensional simulated driving task show that CriticSMC significantly reduces collision rates at a low computational cost while maintaining realism and diversity of driving behaviors across vehicles and environment scenarios.
2023-01-31
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net