Publications

Deep Active Learning: Unified and Principled Method for Query and Training
Changjian Shui
Fan Zhou
Christian Gagné
Boyu Wang
2020-06-02
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (publié)
proceedings.mlr.press
proceedings.mlr.press
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
Sharan Vaswani
Abbas Mehrabian
Audrey Durand
Branislav Kveton
2020-06-02
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (publié)
proceedings.mlr.press
proceedings.mlr.press
A principled approach for generating adversarial images under non-smooth dissimilarity metrics
Aram-Alexandre Pooladian
Chris Finlay
Tim Hoheisel
Adam Oberman
2020-06-02
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (publié)
proceedings.mlr.press
proceedings.mlr.press
A Reduction from Reinforcement Learning to No-Regret Online Learning
Ching-An Cheng
Remi Tachet des Combes
Byron Boots
Geoff Gordon
We present a reduction from reinforcement learning (RL) to no-regret online learning based on the saddle-point formulation of RL, by which "… (voir plus)any" online algorithm with sublinear regret can generate policies with provable performance guarantees. This new perspective decouples the RL problem into two parts: regret minimization and function approximation. The first part admits a standard online-learning analysis, and the second part can be quantified independently of the learning algorithm. Therefore, the proposed reduction can be used as a tool to systematically design new RL algorithms. We demonstrate this idea by devising a simple RL algorithm based on mirror descent and the generative-model oracle. For any
2020-06-02
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (publié)
proceedings.mlr.press
proceedings.mlr.press
Stochastic Neural Network with Kronecker Flow
Chin-wei Huang
Ahmed Touati
Pascal Vincent
Gintare Karolina Dziugaite
Alexandre Lacoste
Aaron Courville
Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their capacity to… (voir plus) scale to the high-dimensional setting of stochastic neural networks. This limitation motivates a need for scalable parameterizations of the noise generation process, in a manner that adequately captures the dependencies among the various parameters. In this work, we address this need and present the Kronecker Flow, a generalization of the Kronecker product to invertible mappings designed for stochastic neural networks. We apply our method to variational Bayesian neural networks on predictive tasks, PAC-Bayes generalization bound estimation, and approximate Thompson sampling in contextual bandits. In all setups, our methods prove to be competitive with existing methods and better than the baselines.
2020-06-02
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press
The Neurobiology of Social Distance
Danilo Bzdok
Robin I. M. Dunbar
2020-06-02
Trends in Cognitive Sciences (publié)
doi.org
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games
Waïss Azizian
Ioannis Mitliagkas
Simon Lacoste-Julien
Gauthier Gidel
We consider differentiable games where the goal is to find a Nash equilibrium. The machine learning community has recently started using var… (voir plus)iants of the gradient method (GD). Prime examples are extragradient (EG), the optimistic gradient method (OG) and consensus optimization (CO), which enjoy linear convergence in cases like bilinear games, where the standard GD fails. The full benefits of theses relatively new methods are not known as there is no unified analysis for both strongly monotone and bilinear games. We provide new analyses of the EG's local and global convergence properties and use is to get a tighter global convergence rate for OG and CO. Our analysis covers the whole range of settings between bilinear and strongly monotone games. It reveals that these methods converge via different mechanisms at these extremes; in between, it exploits the most favorable mechanism for the given problem. We then prove that EG achieves the optimal rate for a wide class of algorithms with any number of extrapolations. Our tight analysis of EG's convergence rate in games shows that, unlike in convex minimization, EG may be much faster than GD.
2020-06-02
International Conference on Artificial Intelligence and Statistics (inconnu)
doi.org
proceedings.mlr.press
The need for privacy with public digital contact tracing during the COVID-19 pandemic
Yoshua Bengio
Richard Janda
Yun William Yu
Daphne Ippolito
Max Jarvie
Dan Pilat
Brooke Struck
Sekoul Krastev
Abhinav Sharma
2020-06-01
The Lancet Digital Health (publié)
doi.org
Training End-to-End Analog Neural Networks with Equilibrium Propagation
Jack D. Kendall
Ross D. Pantone
Kalpana Manickavasagam
Yoshua Bengio
Benjamin Scellier
We introduce a principled method to train end-to-end analog neural networks by stochastic gradient descent. In these analog neural networks,… (voir plus) the weights to be adjusted are implemented by the conductances of programmable resistive devices such as memristors [Chua, 1971], and the nonlinear transfer functions (or `activation functions') are implemented by nonlinear components such as diodes. We show mathematically that a class of analog neural networks (called nonlinear resistive networks) are energy-based models: they possess an energy function as a consequence of Kirchhoff's laws governing electrical circuits. This property enables us to train them using the Equilibrium Propagation framework [Scellier and Bengio, 2017]. Our update rule for each conductance, which is local and relies solely on the voltage drop across the corresponding resistor, is shown to compute the gradient of the loss function. Our numerical simulations, which use the SPICE-based Spectre simulation framework to simulate the dynamics of electrical circuits, demonstrate training on the MNIST classification task, performing comparably or better than equivalent-size software-based neural networks. Our work can guide the development of a new generation of ultra-fast, compact and low-power neural networks supporting on-chip learning.
2020-06-01
ArXiv (prépublication)
arxiv.org
arxiv.org
Multi-Image Super-Resolution for Remote Sensing using Deep Recurrent Networks
Md Rifat Arefin
Vincent Michalski
Pierre-Luc St-Charles
Alfredo Kalaitzis
Sookyung Kim
Samira E. Kahou
Yoshua Bengio
High-resolution satellite imagery is critical for various earth observation applications related to environment monitoring, geoscience, fore… (voir plus)casting, and land use analysis. However, the acquisition cost of such high-quality imagery due to the scarcity of providers and needs for high-frequency revisits restricts its accessibility in many fields. In this work, we present a data-driven, multi-image super resolution approach to alleviate these problems. Our approach is based on an end-to-end deep neural network that consists of an encoder, a fusion module, and a decoder. The encoder extracts co-registered highly efficient feature representations from low-resolution images of a scene. A Gated Re-current Unit (GRU)-based module acts as the fusion module, aggregating features into a combined representation. Finally, a decoder reconstructs the super-resolved image. The proposed model is evaluated on the PROBA-V dataset released in a recent competition held by the European Space Agency. Our results show that it performs among the top contenders and offers a new practical solution for real-world applications.
2020-05-31
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publié)
doi.org
Population variability in social brain morphology for social support, household size and friendship satisfaction
Arezoo Taebi
Hannah Kiesow
Kai Vogeley
Leonhard Schilbach
Boris C. Bernhardt
Danilo Bzdok
The social brain hypothesis proposes that the complexity of human brains has coevolved with increasing complexity of social interactions in … (voir plus)primate societies. The present study explored the possible relationships between brain morphology and the richness of more intimate ‘inner’ and wider ‘outer’ social circles by integrating Bayesian hierarchical modeling with a large cohort sample from the UK Biobank resource (n = 10 000). In this way, we examined population volume effects in 36 regions of the ‘social brain’, ranging from lower sensory to higher associative cortices. We observed strong volume effects in the visual sensory network for the group of individuals with satisfying friendships. Further, the limbic network displayed several brain regions with substantial volume variations in individuals with a lack of social support. Our population neuroscience approach thus showed that distinct networks of the social brain show different patterns of volume variations linked to the examined social indices.
2020-05-31
Social Cognitive and Affective Neuroscience (publié)
doi.org
Restless bandits: indexability and computation of Whittle index
Nima Akbarzadeh
Aditya Mahajan
Restless bandits are a class of sequential resource allocation problems concerned with allocating one or more resources among several altern… (voir plus)ative processes where the evolution of the process depends on the resource allocated to them. Such models capture the fundamental trade-offs between exploration and exploitation. In 1988, Whittle developed an index heuristic for restless bandit problems which has emerged as a popular solution approach due to its simplicity and strong empirical performance. The Whittle index heuristic is applicable if the model satisfies a technical condition known as indexability. In this paper, we present two general sufficient conditions for indexability and identify simpler to verify refinements of these conditions. We then present a general algorithm to compute Whittle index for indexable restless bandits. Finally, we present a detailed numerical study which affirms the strong performance of the Whittle index heuristic.
2020-05-31
arXiv.org (prépublication)
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