Publications

A deep learning benchmark for first break detection from hardrock seismic reflection data
Pierre-Luc St-Charles
Bruno Rousseau
Joumana Ghosn
Gilles Bellefleur
Ernst Schetselaar
2023-12-13
GEOPHYSICS (publié)
doi.org
Privacy-preserving analysis of time-to-event data under nested case-control sampling
Lamin Juwara
Archer Yang
Ana M Velly
Paramita Saha-Chaudhuri
2023-12-13
Statistical Methods in Medical Research (publié)
doi.org
Q-learners Can Provably Collude in the Iterated Prisoner's Dilemma
Quentin Bertrand
Juan Duque
Emilio Calvano
Gauthier Gidel
The deployment of machine learning systems in the market economy has triggered academic and institutional fears over potential tacit collusi… (voir plus)on between fully automated agents. Multiple recent economics studies have empirically shown the emergence of collusive strategies from agents guided by machine learning algorithms. In this work, we prove that multi-agent Q-learners playing the iterated prisoner's dilemma can learn to collude. The complexity of the cooperative multi-agent setting yields multiple fixed-point policies for
2023-12-13
ArXiv (prépublication)
doi.org
arxiv.org
Relative Almost Sure Regret Bounds for Certainty Equivalence Control of Markov Jump Systems
Borna Sayedana
Mohammad Afshari
Peter E. Caines
Aditya Mahajan
In this paper, we consider learning and control problem in an unknown Markov jump linear system (MJLS) with perfect state observations. We f… (voir plus)irst establish a generic upper bound on regret for any learning based algorithm. We then propose a certainty equivalence-based learning alagrithm and show that this algorithm achieves a regret of
2023-12-13
2023 62nd IEEE Conference on Decision and Control (CDC) (publié)
doi.org
Weighted-Norm Bounds on Model Approximation in MDPs with Unbounded Per-Step Cost
Berk Bozkurt
Aditya Mahajan
Ashutosh Nayyar
Yi Ouyang
We consider the problem of designing a control policy for an infinite-horizon discounted cost Markov Decision Process (MDP) …
2023-12-13
IEEE Conference on Decision and Control (publié)
doi.org
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Alexandre AGM Duval
Simon V. Mathis
Chaitanya K. Joshi
Victor Schmidt
Santiago Miret
Fragkiskos D. Malliaros
Taco Cohen
Pietro Lio’
Yoshua Bengio
Michael M. Bronstein
2023-12-12
ArXiv (prépublication)
doi.org
arxiv.org
Efficient Graphics Representation with Differentiable Indirection
Sayantan Datta
Carl Marshall
Zhao Dong
Zhengqin Li
Derek Nowrouzezahrai
We introduce differentiable indirection – a novel learned primitive that employs differentiable multi-scale lookup tables as an effective … (voir plus)substitute for traditional compute and data operations across the graphics pipeline. We demonstrate its flexibility on a number of graphics tasks, i.e., geometric and image representation, texture mapping, shading, and radiance field representation. In all cases, differentiable indirection seamlessly integrates into existing architectures, trains rapidly, and yields both versatile and efficient results.
2023-12-11
SIGGRAPH Asia 2023 Conference Papers (publié)
doi.org
arxiv.org
Explorable Mesh Deformation Subspaces from Unstructured 3D Generative Models
Arman Maesumi
Paul Guerrero
Noam Aigerman
Vladimir Kim
Matthew Fisher
Siddhartha Chaudhuri
Daniel Ritchie
2023-12-11
SIGGRAPH Asia 2023 Conference Papers (publié)
doi.org
arxiv.org
Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks
MohammadReza Davari
Eugene Belilovsky
2023-12-11
ArXiv (prépublication)
doi.org
arxiv.org
Lagrangian Properties and Control of Soft Robots Modeled with Discrete Cosserat Rods
Lekan Molu
Shaoru Chen
Audrey Sedal
The characteristic ``in-plane"bending associated with soft robots' deformation make them preferred over rigid robots in sophisticated manipu… (voir plus)lation and movement tasks. Executing such motion strategies to precision in soft deformable robots and structures is however fraught with modeling and control challenges given their infinite degrees-of-freedom. Imposing \textit{piecewise constant strains} (PCS) across (discretized) Cosserat microsolids on the continuum material however, their dynamics become amenable to tractable mathematical analysis. While this PCS model handles the characteristic difficult-to-model ``in-plane"bending well, its Lagrangian properties are not exploited for control in literature neither is there a rigorous study on the dynamic performance of multisection deformable materials for ``in-plane"bending that guarantees steady-state convergence. In this sentiment, we first establish the PCS model's structural Lagrangian properties. Second, we exploit these for control on various strain goal states. Third, we benchmark our hypotheses against an Octopus-inspired robot arm under different constant tip loads. These induce non-constant ``in-plane"deformation and we regulate strain states throughout the continuum in these configurations. Our numerical results establish convergence to desired equilibrium throughout the continuum in all of our tests. Within the bounds here set, we conjecture that our methods can find wide adoption in the control of cable- and fluid-driven multisection soft robotic arms; and may be extensible to the (learning-based) control of deformable agents employed in simulated, mixed, or augmented reality.
2023-12-10
ArXiv (prépublication)
doi.org
arxiv.org
Maximum flow-based formulation for the optimal location of electric vehicle charging stations
Pierre-Luc Parent
Margarida Carvalho
Miguel F. Anjos
Ribal Atallah
With the increasing effects of climate change, the urgency to step away from fossil fuels is greater than ever before. Electric vehicles (EV… (voir plus)s) are one way to diminish these effects, but their widespread adoption is often limited by the insufficient availability of charging stations. In this work, our goal is to expand the infrastructure of EV charging stations, in order to provide a better quality of service in terms of user satisfaction (and availability of charging stations). Specifically, our focus is directed towards urban areas. We first propose a model for the assignment of EV charging demand to stations, framing it as a maximum flow problem. This model is the basis for the evaluation of user satisfaction with a given charging infrastructure. Secondly, we incorporate the maximum flow model into a mixed‐integer linear program, where decisions on the opening of new stations and on the expansion of their capacity through additional outlets is accounted for. We showcase our methodology for the city of Montreal, demonstrating the scalability of our approach to handle real‐world scenarios. We conclude that considering both spacial and temporal variations in charging demand is meaningful when solving realistic instances.
2023-12-10
ArXiv (prépublication)
doi.org
arxiv.org
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings
Vincent Taboga
Hanane Dagdougui
The surge in electricity use, coupled with the dependency on intermittent renewable energy sources, poses significant hurdles to effectively… (voir plus) managing power grids, particularly during times of peak demand. Demand Response programs and energy conservation measures are essential to operate energy grids while ensuring a responsible use of our resources This research combines distributed optimization using ADMM with Deep Learning models to plan indoor temperature setpoints effectively. A two-layer hierarchical structure is used, with a central building coordinator at the upper layer and local controllers at the thermal zone layer. The coordinator must limit the building's maximum power by translating the building's total power to local power targets for each zone. Local controllers can modify the temperature setpoints to meet the local power targets. The resulting control algorithm, called Distributed Planning Networks, is designed to be both adaptable and scalable to many types of buildings, tackling two of the main challenges in the development of such systems. The proposed approach is tested on an 18-zone building modeled in EnergyPlus. The algorithm successfully manages Demand Response peak events.
2023-12-08
ArXiv (prépublication)
doi.org
arxiv.org