16 papers · ranked by Valyu relevance
Daniel E. Ehrmann, Shalmali Joshi, Sebastian D. Goodfellow, Mjaye L. Mazwi + 1 more
'Mjaye L. Mazwi' 'Danny Eytan'] Machine learning (ML) has the potential to transform patient care and outcomes. However, there are important differences between measuring the performance of ML models in silico and usefulness at the point of care. One lens to use to evaluate models during early development is…
Ibai Laña, Javier J. Sanchez-Medina, Eleni I. Vlahogianni, Javier Del Ser + 1 more
'Javier Del Ser' 'Rashid Mehmood'] Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and…
Ibai Laña, Javier Medina, Eleni I. Vlahogianni, Javier Del Ser
aTECNALIA, Basque Research & Technology Alliance (BRTA), P. Tecnologico Bizkaia, Ed. 700, 48160 Derio, Spain bCICEI, Department of Computer Science, University of Las Palmas de Gran Canaria, 35001 Las Palmas, Spain cDepartment of Transportation Planning and Engineering, National Technical University of Athens, 15780…
Samuel Marc Denton, Ansaf Salleb-Aouissi
This paper finds a solution to the question of actionability on both linear and non-linear SVM models. Additionally, we introduce a way to account for weighted actions that allow for more change in certain features than others. We propose a gradient descent solution on the linear, RBF, and polynomial kernels, and we…
Lydia T. Liu, Solon Barocas, Jon Kleinberg, Karen Levy
Predicting future outcomes is a prevalent application of machine learning in social impact domains. Examples range from predicting student success in education to predicting disease risk in healthcare. Practitioners recognize that the ultimate goal is not just to predict but to act effectively. Increasing evidence…
Ronal Singh, Tim B. Miller, Liz Sonenberg, Eduardo Velloso + 3 more
'Frank Vetere' 'Piers D. L. Howe' 'Paul Dourish'] Abstract—In this paper, we introduce and evaluate a tool for researchers and practitioners to assess the actionability of information provided to users to support algorithmic recourse. While there are clear benefits of recourse from the user's perspective, the notion of…
Muffy Calder, Claire Craig, Dave Culley, Richard de Cani + 15 more
'Christl A. Donnelly' 'Rowan Douglas' 'Bruce Edmonds' 'Jonathon Gascoigne' 'Nigel Gilbert' 'Caroline Hargrove' 'Derwen Hinds' 'David C. Lane' 'Dervilla Mitchell' 'Giles Pavey' 'David Robertson' 'Bridget Rosewell' 'Spencer Sherwin' 'Mark Walport' 'Alan Wilson'] In order to deal with an increasingly complex world, we…
Keith D. Harris, Guy Hadari, Gili Greenbaum
Modeling the dynamics of biological processes is ubiquitous across the ecological and evolutionary disciplines. However, the increasing complexity of these models poses a significant challenge to the dissemination of model-derived results. With the existing requirements of scientific publishing, most often only a small…
Jonathan Karr, Rahuman S. Malik-Sheriff, James Osborne, Gilberto Gonzalez-Parra + 14 more
'Gilberto Gonzalez-Parra' 'Eric Forgoston' 'Ruth Bowness' 'Yaling Liu' 'Robin Thompson' 'Winston Garira' 'Jacob Barhak' 'John Rice' 'Marcella Torres' 'Hana M. Dobrovolny' 'Tingting Tang' 'William Waites' 'James A. Glazier' 'James R. Faeder' 'Alexander Kulesza'] During the COVID-19 pandemic, mathematical modeling of…
Jana L. Gevertz, Irina Kareva
Mathematical models are increasingly being developed and calibrated in tandem with data collection, empowering scientists to intervene in real time based on quantitative model predictions. Well-designed experiments can help augment the predictive power of a mathematical model but the question of when to collect data to…
Suyog Chandramouli, Danqing Shi, Aini Putkonen, Sebastiaan De Peuter + 4 more
Computational rationality explains human behavior as arising due to the maximization of expected utility under the constraints imposed by the environment and limited cognitive resources. This simple assumption, when instantiated via partially observable Markov decision processes (POMDPs), gives rise to a powerful…
John Mark Agosta, Robert F. Horton
With the explosion of applications of Data Science, the field is has come loose from its foundations. This article argues for a new program of applied research in areas familiar to researchers in Bayesian methods in AI that are needed to ground the practice of Data Science by borrowing from AI techniques for model…
Authors not listed
Machine learning holds significant promise for accelerating biomarker discovery in clinical proteomics, yet its real-world impact remains limited by widespread methodological pitfalls and unrealistic expectations. In this perspective, we critically examine the integration of machine learning into clinical proteomics…
Kate E. Dray, Joseph J. Muldoon, Niall M. Mangan, Neda Bagheri + 1 more
Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier…
Prashanth Athri, Vidhya Murali, Pradyumna Y Muralidhar, Cassandra Königs + 4 more
- 1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India - 2. PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India - 3. Bioinformatics and Medical Informatics, Bielefeld University…
Tianfan Jin, Brett M Savoie
Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive…