Publications

Coping With Simulators That Don't Always Return
Andrew Warrington
Saeid Naderiparizi
Frank N. Wood
2019-12-31
AISTATS (publié)
proceedings.mlr.press
proceedings.mlr.press
COVI White Paper-Version 1.1
H. Alsdurf
Yoshua Bengio
T. Deleu
Prateek Gupta
Daphne Ippolito
R. Janda
Max Jarvie
Tyler Kolody
S. Krastev
Tegan Maharaj
Robert Obryk
D. Pilat
Valérie Pisano
Benjamin Prud'homme
Meng Qu
Nasim Rahaman
I. Rish
J. Rousseau
Abhinav Sharma
B. Struck … (voir 3 de plus)
Jian Tang
Martin Weiss
Yun William Yu
2019-12-31
(publié)
www.semanticscholar.org
Cross-layer communication over fading channels with adaptive decision feedback
Borna Sayedana
Aditya Mahajan
E. Yeh
In this paper, cross-layer design of transmitting data packets over AWGN fading channel with adaptive decision feedback is considered. The t… (voir plus)ransmitter decides the number of packets to transmit and the threshold of the decision feedback based on the queue length and the channel state. The transmit power is chosen such that the probability of error is below a pre-specified threshold. We model the system as a Markov decision process and use ideas from lattice theory to establish qualitative properties of optimal transmission strategies. In particular, we show that: (i) if the channel state remains the same and the number of packets in the queue increase, then the optimal policy either transmits more packets or uses a smaller decision feedback threshold or both; and (ii) if the number of packets in the queue remain the same and the channel quality deteriorates, then the optimal policy either transmits fewer packets or uses a larger threshold for the decision feedback or both. We also show under rate constraints that if the channel gains for all channel states are above a threshold, then the “or” in the above characterization can be replaced by “and”. Finally, we present a numerical example showing that adaptive decision feedback significantly improves the power-delay trade-off as compared with the case of no feedback.
2019-12-31
WiOpt (publié)
dblp.uni-trier.de
Deep interpretability for GWAS
Deepak Sharma
Audrey Durand
Marc-Andr Legault
Louis-Philippe Lemieux Perreault
Audrey Lemaon
Marie-Pierre Dub
Joelle Pineau
Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases. In the… (voir plus)se studies, association testing is done on a variant-by-variant basis, possibly missing out on non-linear interaction effects between variants. Deep networks can be used to model these interactions, but they are difficult to train and interpret on large genetic datasets. We propose a method that uses the gradient based deep interpretability technique named DeepLIFT to show that known diabetes genetic risk factors can be identified using deep models along with possibly novel associations.
2019-12-31
arXiv (prépublication)
doi.org
arxiv.org
Differentiable Causal Discovery from Interventional Data
Philippe Brouillard
Sébastien Lachapelle
Alexandre Lacoste
Simon Lacoste-Julien
Alexandre Drouin
Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the solutio… (voir plus)n is not always identifiable. A new line of work reformulates this problem as a continuous constrained optimization one, which is solved via the augmented Lagrangian method. However, most methods based on this idea do not make use of interventional data, which can significantly alleviate identifiability issues. This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data. We illustrate the flexibility of the continuous-constrained framework by taking advantage of expressive neural architectures such as normalizing flows. We show that our approach compares favorably to the state of the art in a variety of settings, including perfect and imperfect interventions for which the targeted nodes may even be unknown.
2019-12-31
NeurIPS (publié)
doi.org
arxiv.org
Discrete-Valued Neural Communication in Structured Architectures Enhances Generalization
Dianbo Liu
Alex Lamb
Kenji Kawaguchi
Anirudh Goyal
Chen Sun
Michael C. Mozer
Yoshua Bengio
Deep learning has advanced from fully connected architectures to structured models organized into components, e.g., the transformer composed… (voir plus) of positional elements, modular architectures divided into slots, and graph neural nets made up of nodes. In structured models, an interesting question is how to conduct dynamic and possibly sparse communication among the separate components. Here, we explore the hypothesis that restricting the transmitted information among components to discrete representations is a beneficial bottleneck. The motivating intuition is human language in which communication occurs through discrete symbols. Even though individuals have different understandings of what a "cat" is based on their specific experiences, the shared discrete token makes it possible for communication among individuals to be unimpeded by individual differences in internal representation. To discretize the values of concepts dynamically communicated among specialist components, we extend the quantization mechanism from the Vector-Quantized Variational Autoencoder to multi-headed discretization with shared codebooks and use it for discrete-valued neural communication (DVNC). Our experiments show that DVNC substantially improves systematic generalization in a variety of architectures -- transformers, modular architectures, and graph neural networks. We also show that the DVNC is robust to the choice of hyperparameters, making the method very useful in practice. Moreover, we establish a theoretical justification of our discretization process, proving that it has the ability to increase noise robustness and reduce the underlying dimensionality of the model.
2019-12-31
International Conference on Machine Learning (publié)
doi.org
openreview.net
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
Philip Amortila
Doina Precup
Prakash Panangaden
Bellemare Marc-Emmanuel
We present a distributional approach to theoretical analyses of reinforcement learning algorithms for constant step-sizes. We demonstrate it… (voir plus)s effectiveness by presenting simple and unified proofs of convergence for a variety of commonly-used methods. We show that value-based methods such as TD(
2019-12-31
AISTATS (publié)
doi.org
proceedings.mlr.press
Divergent protein-coding genes and brain size in primates
Malesys Bourgeron Dumas
Guillaume Dumas
Simon Malesys
Thomas Bourgeron
17 The human brain differs from that of other primates, but the genetic basis of these differences 18 remains unclear. We investigated the e… (voir plus)volutionary pressures acting on almost all human 19 protein-coding genes ( N =11,667; 1:1 orthologs in primates) on the basis of their divergence 20 from those of early hominins, such as Neanderthals, and non-human primates. We confirm 21 that genes encoding brain-related proteins are among the most strongly conserved protein- 22 coding genes in the human genome. Combining our evolutionary pressure metrics for the 23 protein-coding genome with recent datasets, we found that this conservation applied to genes 24 functionally associated with the synapse and expressed in brain structures such as the 25 prefrontal cortex and the cerebellum. Conversely, several of the protein-coding genes that 26 diverge most in hominins relative to other primates are associated with brain-associated 27 diseases, such as micro/macrocephaly, dyslexia, and autism. We also showed that cerebellum 28 granule neurons express a set of divergent protein-coding genes that may have contributed to 29 the emergence of fine motor skills and social cognition in humans. This resource is available 30 from http://neanderthal.pasteur.fr and can be used to estimate evolutionary constraints acting 31 on a set of genes and to explore their relative contributions to human traits. 32
2019-12-31
(publié)
www.semanticscholar.org
On Efficiency in Hierarchical Reinforcement Learning
Zheng Wen
Doina Precup
Morteza Ibrahimi
Andre Barreto
Benjamin Van Roy
Satinder Singh
Hierarchical Reinforcement Learning (HRL) approaches promise to provide more efficient solutions to sequential decision making problems, bo… (voir plus)th in terms of statistical as well as computational efficiency. While this has been demonstrated empirically over time in a variety of tasks, theoretical results quantifying the ben-efits of such methods are still few and far between. In this paper, we discuss the kind of structure in a Markov decision process which gives rise to efficient HRL methods. Specifically, we formalize the intuition that HRL can exploit well repeating "subMDPs", with similar reward and transition structure. We show that, under reasonable assumptions, a model-based Thompson sampling-style HRL algorithm that exploits this structure is statistically efficient, as established through a finite-time regret bound. We also establish conditions under which planning with structure-induced options is near-optimal and computationally efficient.
2019-12-31
Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (publié)
dblp.uni-trier.de
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning
Tianyu Li
Bogdan Mazoure
Doina Precup
Guillaume Rabusseau
2019-12-31
International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press
Electric Vehicles Equilibrium Model that Considers Queue Delay and Mixed Traffic
Nurit Oliker
Miguel F. Anjos
Margarida Carvalho
Emma Frejinger
Bernard Gendron
This study develops an equilibrium model for electric vehicles (EVs) that considers both queue delays in charging stations and flow dependen… (voir plus)t travel times. This is a user equilibrium model that accounts for travel, charging and queuing time in the path choice modelling of EVs and the complementary traffic. Waiting and service times in charging stations are represented by an m/m/k queuing system. The model considers multiple vehicle and driver classes, expressing different battery capacity, initial charge state and range anxiety level. Feasible paths are found for multiple classes given their limited travel range. A numerical application exemplifies the limitations of EVs assignment and their impact on flow distribution.
2019-12-31
(publié)
www.semanticscholar.org
AN ENSEMBLE APPROACH FOR DETECTING MACHINE FAILURE FROM SOUND Technical
Faruk Ahmed
Phong Cao Nguyen
Aaron Courville
We develop an ensemble-based approach for our submission to the anomaly detection challenge at DCASE 2020. The main members of our ensemble … (voir plus)are auto-encoders (with reconstruction error as the signal), classifiers (with negative predictive confidence as the signal), mismatch of the time-shifted signal with its Fourier-phase-shifted version, and a Gaussian mixture model on a set of common short-term features extracted from the waveform. The scores are passed through an exponential non-linearity and weighted to provide the final score, where the weighting and scaling hyper-parameters are learned on the development set. Our ensemble improves over the baseline on the development set.
2019-12-31
(publié)
www.semanticscholar.org