Publications

Optimising Electric Vehicle Charging Station Placement Using Advanced Discrete Choice Models
Steven Lamontagne
Margarida Carvalho
Emma Frejinger
Bernard Gendron
Miguel F. Anjos
Ribal Atallah
D'epartement d'informatique et de recherche op'erationnelle
U. Montr'eal
S. O. Mathematics
U. Edinburgh
Institut de Recherche d'Hydro-Qu'ebec
We present a new model for finding the optimal placement of electric vehicle charging stations across a multiperiod time frame so as to maxi… (see more)mise electric vehicle adoption. Via the use of stochastic discrete choice models and user classes, this work allows for a granular modelling of user attributes and their preferences in regard to charging station characteristics. We adopt a simulation approach and precompute error terms for each option available to users for a given number of scenarios. This results in a bilevel optimisation model that is, however, intractable for all but the simplest instances. Our major contribution is a reformulation into a maximum covering model, which uses the precomputed error terms to calculate the users covered by each charging station. This allows solutions to be found more efficiently than for the bilevel formulation. The maximum covering formulation remains intractable in some instances, so we propose rolling horizon, greedy, and greedy randomised adaptive search procedure heuristics to obtain good-quality solutions more efficiently. Extensive computational results are provided, and they compare the maximum covering formulation with the current state of the art for both exact solutions and the heuristic methods. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by Hydro-Québec and the Natural Sciences and Engineering Research Council of Canada [Discovery grant 2017-06054; Collaborative Research and Development Grant CRDPJ 536757–19]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0185 .
2023-01-01
INFORMS J. Comput. (published)
doi.org
arxiv.org
Optimism and Adaptivity in Policy Optimization
Veronica Chelu
Tom Zahavy
Arthur Guez
Doina Precup
Sebastian Flennerhag
2023-01-01
arXiv.org (preprint)
doi.org
Optimizing Fairness over Time with Homogeneous Workers (Short Paper).
Bart-jan Van Rossum
Ying Chen
Andrea Lodi
2023-01-01
ATMOS (published)
doi.org
PAC-Bayesian Generalization Bounds for Adversarial Generative Models
Sokhna Diarra Mbacke
Florence Clerc
Pascal Germain
We extend PAC-Bayesian theory to generative models and develop generalization bounds for models based on the Wasserstein distance and the to… (see more)tal variation distance. Our first result on the Wasserstein distance assumes the instance space is bounded, while our second result takes advantage of dimensionality reduction. Our results naturally apply to Wasserstein GANs and Energy-Based GANs, and our bounds provide new training objectives for these two. Although our work is mainly theoretical, we perform numerical experiments showing non-vacuous generalization bounds for Wasserstein GANs on synthetic datasets.
2023-01-01
ICML (published)
doi.org
openreview.net
Patient experience or patient satisfaction? A systematic review of child- and family-reported experience measures in pediatric surgery.
Julia Ferreira
Prachikumari Patel
Elena Guadagno
Nikki Ow
Jo Wray
Sherif Emil
Dan Poenaru
2023-01-01
Journal of Pediatric Surgery (published)
doi.org
Performative Prediction with Neural Networks
Mehrnaz Mofakhami
Ioannis Mitliagkas
Gauthier Gidel
2023-01-01
AISTATS (published)
doi.org
openreview.net
Performative Prediction with Neural Networks
Mehrnaz Mofakhami
Ioannis Mitliagkas
Gauthier Gidel
Performative prediction is a framework for learning models that influence the data they intend to predict. We focus on finding classifiers t… (see more)hat are performatively stable, i.e. optimal for the data distribution they induce. Standard convergence results for finding a performatively stable classifier with the method of repeated risk minimization assume that the data distribution is Lipschitz continuous to the model's parameters. Under this assumption, the loss must be strongly convex and smooth in these parameters; otherwise, the method will diverge for some problems. In this work, we instead assume that the data distribution is Lipschitz continuous with respect to the model's predictions, a more natural assumption for performative systems. As a result, we are able to significantly relax the assumptions on the loss function. In particular, we do not need to assume convexity with respect to the model's parameters. As an illustration, we introduce a resampling procedure that models realistic distribution shifts and show that it satisfies our assumptions. We support our theory by showing that one can learn performatively stable classifiers with neural networks making predictions about real data that shift according to our proposed procedure.
2023-01-01
AISTATS (published)
doi.org
openreview.net
Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation
Houssem Ben Braiek
Thomas Reid
Foutse Khomh
In the context of aircraft system performance assessment, deep learning technologies allow us to quickly infer models from experimental meas… (see more)urements, with less detailed system knowledge than usually required by physics-based modeling. However, this inexpensive model development also comes with new challenges regarding model trustworthiness. This article presents a novel approach, physics-guided adversarial machine learning (ML), which improves the confidence over the physics consistency of the model. The approach performs, first, a physics-guided adversarial testing phase to search for test inputs revealing behavioral system inconsistencies, while still falling within the range of foreseeable operational conditions. Then, it proceeds with a physics-informed adversarial training to teach the model the system-related physics domain foreknowledge through iteratively reducing the unwanted output deviations on the previously uncovered counterexamples. Empirical evaluation on two aircraft system performance models shows the effectiveness of our adversarial ML approach in exposing physical inconsistencies of both the models and in improving their propensity to be consistent with physics domain knowledge.
2023-01-01
IEEE Transactions on Reliability (published)
doi.org
arxiv.org
Physics-Inspired Protein Encoder Pre-Training via Siamese Sequence-Structure Diffusion Trajectory Prediction
Zuobai Zhang
Minghao Xu
Aurelie Lozano
Vijil Chenthamarakshan
Payel Das
Jian Tang
Pre-training methods on proteins are recently gaining interest, leveraging either protein sequences or structures, while modeling their join… (see more)t energy landscape is largely unexplored. In this work, inspired by the success of denoising diffusion models, we propose the DiffPreT approach to pre-train a protein encoder by sequence-structure multimodal diffusion modeling. DiffPreT guides the encoder to recover the native protein sequences and structures from the perturbed ones along the multimodal diffusion trajectory, which acquires the joint distribution of sequences and structures. Considering the essential protein conformational variations, we enhance DiffPreT by a physics-inspired method called Siamese Diffusion Trajectory Prediction ( SiamDiff ) to capture the correlation between different conformers of a protein. SiamDiff attains this goal by maximizing the mutual information between representations of diffusion trajectories of structurally-correlated conformers. We study the effectiveness of DiffPreT and SiamDiff on both atom-and residue-level structure-based protein understanding tasks. Experimental results show that the performance of DiffPreT is consistently competitive on all tasks, and SiamDiff achieves new state-of-the-art performance, considering the mean ranks on all tasks. The source code will be released upon acceptance.
2023-01-01
arXiv.org (preprint)
doi.org
Preference-Based Offline Evaluation
C. Clarke
Fernando Diaz
Negar Arabzadeh
A core step in production model research and development involves the offline evaluation of a system before production deployment. Tradition… (see more)al offline evaluation of search, recommender, and other systems involves gathering item relevance labels from human editors. These labels can then be used to assess system performance using offline evaluation metrics. Unfortunately, this approach does not work when evaluating highly effective ranking systems, such as those emerging from the advances in machine learning. Recent work demonstrates that moving away from pointwise item and metric evaluation can be a more effective approach to the offline evaluation of systems. This tutorial, intended for both researchers and practitioners, reviews early work in preference-based evaluation and covers recent developments in detail.
2023-01-01
WSDM (published)
doi.org
Price Forecasting in the Ontario Electricity Market via TriConvGRU Hybrid Model: Univariate vs. Multivariate Frameworks
Behdad Ehsani
Pierre-Olivier Pineau
Laurent Charlin
2023-01-01
SSRN Electronic Journal (published)
doi.org
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design
Chuan Guo
Kamalika Chaudhuri
Pierre Stock
Michael Rabbat
In private federated learning (FL), a server aggregates differentially private updates from a large number of clients in order to train a ma… (see more)chine learning model. The main challenge in this setting is balancing privacy with both classification accuracy of the learnt model as well as the number of bits communicated between the clients and server. Prior work has achieved a good trade-off by designing a privacy-aware compression mechanism, called the minimum variance unbiased (MVU) mechanism, that numerically solves an optimization problem to determine the parameters of the mechanism. This paper builds upon it by introducing a new interpolation procedure in the numerical design process that allows for a far more efficient privacy analysis. The result is the new Interpolated MVU mechanism that is more scalable, has a better privacy-utility trade-off, and provides SOTA results on communication-efficient private FL on a variety of datasets.
2023-01-01
ICML (published)
openreview.net
openreview.net