Publications

A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs

Andrea Lodi

Sriram Sankaranarayanan

We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (… (voir plus)2SIP) problems with constraints in the first and second stages. The goal of the algorithm is to predict a "representative scenario" (RS) for the problem such that, deterministically solving the 2SIP with the random realization equal to the RS, gives a near-optimal solution to the original 2SIP. Predicting an RS, instead of directly predicting a solution ensures first-stage feasibility of the solution. If the problem is known to have complete recourse, second-stage feasibility is also guaranteed. For computational testing, we learn to find an RS for a two-stage stochastic facility location problem with integer variables and linear constraints in both stages and consistently provide near-optimal solutions. Our computing times are very competitive with those of general-purpose integer programming solvers to achieve a similar solution quality.

2020-09-18

Integration of Constraint Programming, Artificial Intelligence, and Operations Research (publié)

doi.org

arxiv.org

A normative modelling approach reveals age-atypical cortical thickness in a subgroup of males with autism spectrum disorder

Richard A.I. Bethlehem

Jakob Seidlitz

Rafael Romero-Garcia

Stavros Trakoshis

Guillaume Dumas

Michael V. Lombardo

2020-09-03

Communications Biology (publié)

doi.org

Keynote Lecture - Building Knowledge For AI AgentsWith Reinforcement Learning

Doina Precup

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings.… (voir plus) Reinforcement learning allows autonomous agents to learn how to act in a stochastic, unknown environment, with which they can interact. Deep reinforcement learning, in particular, has achieved great success in well-defined application domains, such as Go or chess, in which an agent has to learn how to act and there is a clear success criterion. In this talk, I will focus on the potential role of reinforcement learning as a tool for building knowledge representations in AI agents whose goal is to perform continual learning. I will examine a key concept in reinforcement learning, the value function, and discuss its generalization to support various forms of predictive knowledge. I will also discuss the role of temporally extended actions, and their associated predictive models, in learning procedural knowledge. In order to tame the possible complexity of learning knowledge representations, reinforcement learning agents can use the concepts of intents (ie intended consequences of courses of actions) and affordances (which capture knowlege about where actions can be applied). Finally, I will discuss the challenge of how to evaluate reinforcement learning agents whose goal is not just to control their environment, but also to build knowledge about their world.

2020-09-02

International Conference on Computational Photography (publié)

doi.org

Shared Decision Making in Surgery: A Meta-Analysis of Existing Literature

Kacper Niburski

Elena Guadagno

S. A. Rahimi

Dan Poenaru

2020-09-02

The Patient - Patient-Centered Outcomes Research (publié)

doi.org

Correction to: Why public health matters today and tomorrow: the role of applied public health research

Lindsay McLaren

Paula Braitstein

David L Buckeridge

Damien Contandriopoulos

Maria I. Creatore

Guy Faulkner

David Hammond

Steven J. Hoffman

Yan Kestens

Scott Leatherdale

Jonathan McGavock

Wendy V. Norman

Candace Nykiforuk

Valéry Ridde

Janet Smylie

The article “Why public health matters today and tomorrow: the role of applied public health research,” written by Lindsay McLaren et al… (voir plus)., was originally published Online First without Open Access.

2020-09-01

Canadian Journal of Public Health = Revue Canadienne de Santé Publique (publié)

doi.org

Traceability Network Analysis: A Case Study of Links in Issue Tracking Systems

Alexander Nicholson

Deeksha M. Arya

Jin L.C. Guo

Traceability links between software artifacts serve as an invaluable resource for reasoning about software products and their development pr… (voir plus)ocess. Most conventional methods for capturing traceability are based on pair-wise artifact relations such as trace matrices or navigable links between two directly related artifacts. However, this limited view of trace links ignores the propagating effect of artifact connections as well as the trace link properties at a project level. In this work, we propose the use of network structures to provide another perspective from which reasoning on a collective of trace events is possible. We explore various network analysis techniques in the issue tracking system of sixty-six open source projects. Our observation reveals two salient properties of the traceability network, i.e. scale free and triadic closure. These properties provide a strong indication of the applicability of network analysis tools and can be used to identify and examine important "hub" issues. As a stepping stone, these properties can further support project status analysis and link type prediction. As a proof-of-concept, we demonstrate the effectiveness of applying the triadic closure property to link type prediction.

2020-08-31

International Workshop on Artificial Intelligence for Requirements Engineering (publié)

doi.org

Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images

Travis Manderson

Stefan Wapnick

David Meger

Gregory Dudek

We present a method for learning to drive on smooth terrain while simultaneously avoiding collisions in challenging off-road and unstructure… (voir plus)d outdoor environments using only visual inputs. Our approach applies a hybrid model-based and model-free reinforcement learning method that is entirely self-supervised in labeling terrain roughness and collisions using on-board sensors. Notably, we provide both first-person and overhead aerial image inputs to our model. We find that the fusion of these complementary inputs improves planning foresight and makes the model robust to visual obstructions. Our results show the ability to generalize to environments with plentiful vegetation, various types of rock, and sandy trails. During evaluation, our policy attained 90% smooth terrain traversal and reduced the proportion of rough terrain driven over by 6.1 times compared to a model using only first-person imagery.

2020-08-30

2020 IEEE International Conference on Robotics and Automation (ICRA) (publié)

doi.org

arxiv.org

A Neural Network Based Approach to Domain Modelling Relationships and Patterns Recognition

Rijul Saini

Gunter Mussbacher

Jin L.C. Guo

Jörg Kienzle

Model-Driven Software Engineering advocates the use of models and their transformations across different stages of software engineering to b… (voir plus)etter understand and analyze systems under development. Domain modelling is used during requirements analysis or the early stages of design to transform informal requirements written in natural language to domain models which are analyzable and more concise. Since domain modelling is time-consuming and requires modelling skills and experience, many approaches have been proposed to extract domain concepts and relationships automatically using extraction rules. However, relationships and patterns are often hidden in the sentences of a problem description. Automatic recognition of relationships or patterns in those cases requires context information and external knowledge of participating domain concepts, which goes beyond what is possible with extraction rules. In this paper, we draw on recent work on domain model extraction and envision a novel technique where sentence boundaries are customized and clusters of sentences are created for domain concepts. The technique further exploits a BiLSTM neural network model to identify relationships and patterns among domain concepts. We also present a classification strategy for relationships and patterns and use it to instantiate our technique. Preliminary results indicate that this novel idea is promising and warrants further research.

2020-08-30

2020 IEEE Tenth International Model-Driven Requirements Engineering (MoDRE) (publié)

doi.org

Information correspondence between types of documentation for APIs

Deeksha M. Arya

Jin L.C. Guo

Martin P. Robillard

2020-08-25

Empirical Software Engineering (publié)

doi.org

Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets

Marc-Andre Schulz

B. T. Thomas Yeo

Joshua T. Vogelstein

Janaina Mourao-Miranada

Jakob N. Kather

Konrad Kording

Blake Richards

Danilo Bzdok

Recently, deep learning has unlocked unprecedented success in various domains, especially using images, text, and speech. However, deep lear… (voir plus)ning is only beneficial if the data have nonlinear relationships and if they are exploitable at available sample sizes. We systematically profiled the performance of deep, kernel, and linear models as a function of sample size on UKBiobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improves when escalating from linear models to shallow-nonlinear models, and further improves with deep-nonlinear models. In contrast, using structural or functional brain scans, simple linear models perform on par with more complex, highly parameterized models in age/sex prediction across increasing sample sizes. In sum, linear models keep improving as the sample size approaches ~10,000 subjects. Yet, nonlinearities for predicting common phenotypes from typical brain scans remain largely inaccessible to the examined kernel and deep learning methods.

2020-08-24

Nature Communications (publié)

doi.org

BIAS: Transparent reporting of biomedical image analysis challenges

Lena Maier-Hein

Annika Reinke

Michal Kozubek

Anne L. Martel

Tal Arbel

Matthias Eisenmann

Allan Hanbury

Pierre Jannin