Publications

Weakly Supervised Representation Learning with Sparse Perturbations
Kartik Ahuja
Jason Hartford
Yoshua Bengio
The theory of representation learning aims to build methods that provably invert the data generating process with minimal domain knowledge o… (see more)r any source of supervision. Most prior approaches require strong distributional assumptions on the latent variables and weak supervision (auxiliary information such as timestamps) to provide provable identification guarantees. In this work, we show that if one has weak supervision from observations generated by sparse perturbations of the latent variables--e.g. images in a reinforcement learning environment where actions move individual sprites--identification is achievable under unknown continuous latent distributions. We show that if the perturbations are applied only on mutually exclusive blocks of latents, we identify the latents up to those blocks. We also show that if these perturbation blocks overlap, we identify latents up to the smallest blocks shared across perturbations. Consequently, if there are blocks that intersect in one latent variable only, then such latents are identified up to permutation and scaling. We propose a natural estimation procedure based on this theory and illustrate it on low-dimensional synthetic and image-based experiments.
openreview.net
What does it mean to be an AI Ethicist: An ontology of existing roles
Shalaleh Rismani
AJung Moon
With the increasing adoption of Artificial Intelligence systems (AIS) in various application and the growing efforts to regulate such system… (see more)s, a new set of occupations has emerged in the industry. This new set of roles take different titles and hold varying responsibilities. However, the individuals in these roles are tasked with interpreting and operationalizing best practices for developing ethical and safe AI systems. We will broadly refer to this new set of occupations as AI ethicists and recognize that they often hold a specific role in the intersection of technology development, business needs, and societal implications. In this work, we examine what it means to be an AI ethicist in the industry and propose an ontology of existing roles under this broad title along with their required competencies. We create this ontology by examining the job postings for such roles over the past two years and conduct expert interviews with fourteen individuals who currently hold such a role in the industry. The proposed ontology will inform executives and leaders who are looking to build responsible AI teams and provide educators the necessary information for creating new learning objectives and curriculum.
2022-01-01
arXiv.org (preprint)
doi.org
What does it mean to be an AI Ethicist: An ontology of existing roles
Shalaleh Rismani
AJung Moon
With the increasing adoption of Artificial Intelligence systems (AIS) in various application and the growing efforts to regulate such system… (see more)s, a new set of occupations has emerged in the industry. This new set of roles take different titles and hold varying responsibilities. However, the individuals in these roles are tasked with interpreting and operationalizing best practices for developing ethical and safe AI systems. We will broadly refer to this new set of occupations as AI ethicists and recognize that they often hold a specific role in the intersection of technology development, business needs, and societal implications. In this work, we examine what it means to be an AI ethicist in the industry and propose an ontology of existing roles under this broad title along with their required competencies. We create this ontology by examining the job postings for such roles over the past two years and conduct expert interviews with fourteen individuals who currently hold such a role in the industry. The proposed ontology will inform executives and leaders who are looking to build responsible AI teams and provide educators the necessary information for creating new learning objectives and curriculum.
2022-01-01
arXiv.org (preprint)
doi.org
Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning
Samin Yeasar Arnob
Riashat Islam
Doina Precup
2021-12-31
ArXiv (preprint)
arxiv.org
arxiv.org
Machine learning application development: practitioners’ insights
Md. Saidur Rahman
Foutse Khomh
Alaleh Hamidi
Jinghui Cheng
Giuliano Antoniol
Hironori Washizaki
2021-12-31
ArXiv (preprint)
doi.org
arxiv.org
Single-Shot Pruning for Offline Reinforcement Learning
Samin Yeasar Arnob
Riyasat Ohib
Sergey Plis
Doina Precup
2021-12-31
ArXiv (preprint)
arxiv.org
arxiv.org
Heterogeneous Crowd Simulation Using Parametric Reinforcement Learning
Kaidong Hu
Michael Brandon Haworth
Glen Berseth
Vladimir Pavlovic
Petros Faloutsos
Mubbasir. T. Kapadia
Agent-based synthetic crowd simulation affords the cost-effective large-scale simulation and animation of interacting digital humans. Model-… (see more)based approaches have successfully generated a plethora of simulators with a variety of foundations. However, prior approaches have been based on statically defined models predicated on simplifying assumptions, limited video-based datasets, or homogeneous policies. Recent works have applied reinforcement learning to learn policies for navigation. However, these approaches may learn static homogeneous rules, are typically limited in their generalization to trained scenarios, and limited in their usability in synthetic crowd domains. In this article, we present a multi-agent reinforcement learning-based approach that learns a parametric predictive collision avoidance and steering policy. We show that training over a parameter space produces a flexible model across crowd configurations. That is, our goal-conditioned approach learns a parametric policy that affords heterogeneous synthetic crowds. We propose a model-free approach without centralization of internal agent information, control signals, or agent communication. The model is extensively evaluated. The results show policy generalization across unseen scenarios, agent parameters, and out-of-distribution parameterizations. The learned model has comparable computational performance to traditional methods. Qualitatively the model produces both expected (laminar flow, shuffling, bottleneck) and unexpected (side-stepping) emergent qualitative behaviours, and quantitatively the approach is performant across measures of movement quality.
2021-12-29
IEEE Transactions on Visualization and Computer Graphics (published)
doi.org
Heterogeneous Crowd Simulation Using Parametric Reinforcement Learning
Kaidong Hu
Brandon Haworth
Glen Berseth
Vladimir Pavlovic
Petros Faloutsos
Mubbasir Kapadia
Agent-based synthetic crowd simulation affords the cost-effective large-scale simulation and animation of interacting digital humans. Model-… (see more)based approaches have successfully generated a plethora of simulators with a variety of foundations. However, prior approaches have been based on statically defined models predicated on simplifying assumptions, limited video-based datasets, or homogeneous policies. Recent works have applied reinforcement learning to learn policies for navigation. However, these approaches may learn static homogeneous rules, are typically limited in their generalization to trained scenarios, and limited in their usability in synthetic crowd domains. In this article, we present a multi-agent reinforcement learning-based approach that learns a parametric predictive collision avoidance and steering policy. We show that training over a parameter space produces a flexible model across crowd configurations. That is, our goal-conditioned approach learns a parametric policy that affords heterogeneous synthetic crowds. We propose a model-free approach without centralization of internal agent information, control signals, or agent communication. The model is extensively evaluated. The results show policy generalization across unseen scenarios, agent parameters, and out-of-distribution parameterizations. The learned model has comparable computational performance to traditional methods. Qualitatively the model produces both expected (laminar flow, shuffling, bottleneck) and unexpected (side-stepping) emergent qualitative behaviours, and quantitatively the approach is performant across measures of movement quality.
2021-12-29
IEEE Transactions on Visualization and Computer Graphics (published)
doi.org
Single Allocation Hub Location with Heterogeneous Economies of Scale
Borzou Rostami
Masoud Chitsaz
Okan Arslan
Gilbert Laporte
Andrea Lodi
The economies of scale in hub location is usually modeled by a constant parameter, which captures the benefits companies obtain through cons… (see more)olidation. In their article “Single allocation hub location with heterogeneous economies of scale,” Rostami et al. relax this assumption and consider hub-hub connection costs as piecewise linear functions of the flow amounts. This spoils the triangular inequality property of the distance matrix, making the classical flow-based model invalid and further complicates the problem. The authors tackle the challenge by building a mixed-integer quadratically constrained program and by developing a methodology based on constructing Lagrangian function, linear dual functions, and specialized polynomial-time algorithms to generate enhanced cuts. The developed method offers a new strategy in Benders-type decomposition through relaxing a set of complicating constraints in subproblems when such relaxation is tight. The results confirm the efficacy of the solution methods in solving large-scale problem instances.
2021-12-28
Operational Research (published)
doi.org
Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models
Enoch Amoatey Tetteh
Joseph D Viviano
Yoshua Bengio
David Scott Krueger
Joseph Paul Cohen
Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There ha… (see more)ve been several proposals for efficient and robust visual representation learning among vision research practitioners, especially in the sensitive and critical biomedical domain. In this paper, we propose an idea for out-of-distribution generalization of chest X-ray pathologies that uses a simple balanced batch sampling technique. We observed that balanced sampling between the multiple training datasets improves the performance over baseline models trained without balancing.
2021-12-27
ArXiv (preprint)
arxiv.org
arxiv.org
COVID-19 Seroprevalence in Canada Modelling Waning and Boosting COVID-19 Immunity in Canada a Canadian Immunization Research Network Study
David W. Dick
Lauren Childs
Zhilan Feng
Jing Li
Gergely Röst
David Buckeridge
Nick H. Ogden
Jane Heffernan
2021-12-23
Vaccines (published)
doi.org
Fall 2021 Resurgence and COVID-19 Seroprevalence in Canada: Modelling waning and boosting COVID-19 immunity in Canada, A Canadian Immunization Research Network Study
David W. Dick
Lauren Childs
Zhilan Feng
Jing Li
Gergely Röst
David Buckeridge
Nick H. Ogden
Jane Heffernan
2021-12-23
Vaccines (published)
doi.org