Publication
Gradient estimates of return
Publications associées (32)
ActiveAx(ADD): Toward non-parametric and orientationally invariant axon diameter distribution mapping using PGSE
Jean-Philippe Thiran, David Paul Roger Romascano, Alessandro Daducci, Muhamed Barakovic, Tim Bjørn Dyrby
Purpose Non-invasive axon diameter distribution (ADD) mapping using diffusion MRI is an ill-posed problem. Current ADD mapping methods require knowledge of axon orientation before performing the acquisition. Instead, ActiveAx uses a 3D sampling scheme to e ...
2019Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo
Nisheeth Vishnoi, Oren Rami Mangoubi
Hamiltonian Monte Carlo (HMC) is a widely deployed method to sample from high-dimensional distributions in Statistics and Machine learning. HMC is known to run very efficiently in practice and its popular second-order "leapfrog" implementation has long bee ...
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018Efficient Variance-Reduced Learning Over Multi-Agent Networks
Ali H. Sayed, Bicheng Ying, Kun Yuan
This work develops a fully decentralized variance-reduced learning algorithm for multi-agent networks where nodes store and process the data locally and are only allowed to communicate with their immediate neighbors. In the proposed algorithm, there is no ...
IEEE COMPUTER SOC2018Distributed Coupled Learning Over Adaptive Networks
Ali H. Sayed, Sulaiman A S A E Alghunaim
This work develops an effective distributed algorithm for the solution of stochastic optimization problems that involve partial coupling among both local constraints and local cost functions. While the collection of networked agents is interested in discov ...
IEEE2018Decentralized exact coupled optimization
Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim
This work develops an exact converging algorithm for the solution of a distributed optimization problem with partially-coupled parameters across agents in a multi-agent scenario. In this formulation, while the network performance is dependent on a collecti ...
2017Exact Diffusion for Distributed Optimization and Learning---Part I: Algorithm Development
Ali H. Sayed, Bicheng Ying, Kun Yuan
This work develops a distributed optimization strategy with guaranteed exact convergence for a broad class of left-stochastic combination policies. The resulting exact diffusion strategy is shown in Part II to have a wider stability range and superior conv ...
2017Comparative assessment of selected sugarcane biorefinery-centered systems in Brazil: A multi-criteria method based on sustainability indicators
Edgard Gnansounou, Elia Mercedes Ruiz Pachon, Pavel Vaskan, Catarina Marciano Alves
This work proposes a new sustainability assessment framework aiming to compare selected options of biorefineries subject to provide the same services to a community. At this end, a concept of biorefinery-centered system helps to develop a joint resources a ...
Elsevier2017How close are the eigenvectors and eigenvalues of the sample and actual covariance matrices?
How many samples are sufficient to guarantee that the eigenvectors and eigenvalues of the sample covariance matrix are close to those of the actual covariance matrix? For a wide family of distributions, including distributions with finite second moment and ...
2017Diffusion gradient boosting for networked learning
Using duality arguments from optimization theory, this work develops an effective distributed gradient boosting strategy for inference and classification by networked clusters of learners. By sharing local dual variables with their immediate neighbors thro ...
2017Distributor of neurons in a neocortical column
Henry Markram, Felix Schürmann, Georges Khazen, Martin Telefont
Computer-implemented methods, software, and systems for determining a distribution of neuronal cells across a portion of a brain are described. One computer-implemented method for determining a target distribution of one or more neuronal cells across a por ...
2014