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Distributed Non-Stochastic Experts
[article]
2012
arXiv
pre-print
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We consider the online distributed non-stochastic experts problem, where the distributed system consists of one coordinator node that is connected to k sites, and the sites are required to communicate ...
The two extreme solutions to this problem are: (i) Full communication: This essentially simulates the non-distributed setting to obtain the optimal O(√((n)T)) regret bound at the cost of T communication ...
distributed online non-stochastic experts setting. ...
arXiv:1211.3212v1
fatcat:ozgcr4q4rfc6rl4jaef2wz4gwa
Stochastic-Expert Variational Autoencoder for Collaborative Filtering
2022
Proceedings of the ACM Web Conference 2022
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Our proposed stochastic expert framework is generic and adaptable to any VAE architecture. ...
In our method, individual experts are sampled stochastically at each user-item interaction which can effectively utilize the variability among multiple experts. ...
Mixture of Expert vs Stochastic Expert. ...
doi:10.1145/3485447.3512120
fatcat:km4bnt35inemfjvrpt2vrougx4
The True Expert Knows Which Question Should be Asked
2004
Social Science Research Network
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We suggest a test for discovering whether a potential expert is informed of the distribution of a stochastic process. ...
In a non-Bayesian non-parametric setting, the expert is asked to make a prediction which is tested against a single realization of the stochastic process. ...
A decision maker named Alice is trying to decide whether Bob, who is potentially an expert, is informed about the distribution governing a stochastic process. ...
doi:10.2139/ssrn.560082
fatcat:ce2m2xtdind2vfrvs4dacv5sqe
Augmented Artificial Intelligence: a Conceptual Framework
[article]
2018
arXiv
pre-print
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The mathematical foundations of AI non-destructive correction are presented and a series of new stochastic separation theorems is proven. ...
These errors are unexpected, and differ often from the typical human mistakes ("non-human" errors). ...
Gromov, who attracted our attention to the seminal question about product distributions in a multidimensional cube, and to G. ...
arXiv:1802.02172v3
fatcat:rmdzg3e3vfhezp75sba7nnlpsy
Frontmatter
2015
Random Operators and Stochastic Equations
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The majority of the articles is written by authors from the former USSR, but contributions from leading experts from all over the world will be published as well. ...
RANDOM OPERATORS AND STOCHASTIC EQUATIONS Unauthenticated Download Date | 7/26/18 4:22 AM RANDOM OPERATORS AND STOCHASTIC EQUATIONS is a quarterly journal publishing peerreviewed, English-language, original ...
Random Operators and Stochastic Equations Sadibou Aidara, Ahmadou Bamba Sow Anticipated BDSDEs driven by Lévy process with non-Lipschitz coe cients | Volume
| Issue
Contents
Mohamed-Ahmed Boudref, ...
doi:10.1515/rose-2015-frontmatter3
fatcat:22fmebhpejf5dejjkabincjcvy
Predicting distributions with Linearizing Belief Networks
[article]
2016
arXiv
pre-print
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Conditional belief networks introduce stochastic binary variables in neural networks. ...
A LBN decomposes into a deep linear network where each linear unit can be turned on or off by non-deterministic binary latent units. ...
This yields a mixture of non-linear neural networks gated by a stochastic non-linear neural network. ...
arXiv:1511.05622v4
fatcat:ta22g5y34zdf5f56ov622ymkoa
Frontmatter
2017
Random Operators and Stochastic Equations
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The majority of the articles is written by authors from the former USSR, but contributions from leading experts from all over the world will be published as well. ...
RANDOM OPERATORS AND STOCHASTIC EQUATIONS Unauthenticated Download Date | 7/19/18 4:04 AM RANDOM OPERATORS AND STOCHASTIC EQUATIONS is a quarterly journal publishing peerreviewed, English-language, original ...
Rodríguez-Dagnino The distribution of random motion at non-constant velocity in semi-Markov media 71 Sergio Albeverio, Leszek Gawarecki, Vidyadhar Mandrekar, Barbara Rüdiger, Barun Sarkar Itô formula for ...
doi:10.1515/rose-2017-frontmatter2
fatcat:g5dxuggirfgk7agszvvq56vzbm
A prequential test for exchangeable theories
2014
Journal of Dynamics & Games
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2013 pre-print arXiv:1305.4421v1 fatcat:ccno764i6vggxe5rkb7ka66vmq
A theory is a probability measure over Ω, representing the distribution of some stochastic process. ...
Strong non-manipulability. The definition of strong non-manipulability appeals to a topological notion of 'large' set of distribution as a co-meager set. ...
doi:10.3934/jdg.2014.1.497
fatcat:rzq2tm7ipjartjoislk25e7wli
Stochastic population forecasts based on conditional expert opinions
2011
Journal of the Royal Statistical Society: Series A (Statistics in Society)
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The full probability distribution of population forecasts is specified by starting from expert opinions on the future development of demographic components. ...
The paper develops and applies an expert-based stochastic population forecasting method, which can also be used to obtain a probabilistic version of scenario-based official forecasts. ...
Traditional population projections are, in fact, non-stochastic expert-based 'what if ?' scenarios. ...
doi:10.1111/j.1467-985x.2011.01015.x
pmid:22879704
pmcid:PMC3412228
fatcat:ermeffeljvcbbluvmpb7s63ku4
RETURN, RELIABILITY AND RISK AS A PROACTIVE SET OF CONCEPTS IN DEVELOPING AN EFFICIENT INTEGRATION STRATEGY OF COMPANIES
2016
Journal of Business Economics and Management
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To formulate and solve the management problems of the complex system, a number of methods were used, namely, the stochastic recording of the aims, the existing restrictions and the stochastic optimisation ...
Also, it looks at the practical application of the system through the examination of a specific situation by employing analytical possibilities of a stochastic network. ...
So, having ensured the reliability of expert evaluations and the adequacy of the stochastic utility function, we can generate significant information about the distribution of strategic development resources ...
doi:10.3846/16111699.2016.1150876
fatcat:kkjgvsglrbdnpd5xtcubytewhy
Lifelong Learning with Branching Experts
2021
Asian Conference on Machine Learning
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While it was known that the adversarial branching experts problem is strictly harder than the non-branching one, the stochastic branching experts problem is in fact no harder. ...
Furthermore, we prove a regret lower bound which shows that in the lifelong learning scenario, the case with branching experts now becomes strictly harder than the non-branching case in the stochastic ...
In the paper (Luo and Schapire, 2015) , for the non-branching case, the AdaNormal-Hedge.TV algorithm has small adaptive regrets for both adversarial and stochastic setting. ...
dblp:conf/acml/0001HL21
fatcat:xixjuhza65fijencfnov3qd56m
Learning stochastic finite automata from experts
[chapter]
1998
Lecture Notes in Computer Science
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We present in this paper a new learning problem called learning distributions from experts. In the case we study the experts are stochastic deterministic finite automata (sdfa). ...
This is intended to model the situation where the data is not generated automatically, but in an order dependent of its probability, as would be the case with the data presented by a human expert. ...
Perhaps even a second expert can help : one can use the amount of times experts agree on the ordering of specific strings as a compatibility test. ...
doi:10.1007/bfb0054066
fatcat:mtep6bidkbghbkvnkchqtym6q4
Information Processing in Neuron with Exponential Distributed Delay
2018
International Journal of Machine Learning and Networked Collaborative Engineering
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Intelligent machines and systems are also known as expert systems. ...
(15) is the stationary state membrane potential distribution of the LIF model with stochastic input stimulus. ...
doi:10.30991/ijmlnce.2018v02i02.003
fatcat:wewzt7bsinf6hl76l5yafobw6u
A review on degradation models in reliability analysis
[chapter]
2010
Engineering Asset Lifecycle Management
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Degradation phenomenon is a kind of stochastic process; therefore, it could be modelled in several approaches. ...
suitable models for incomplete data sets Computationally efficient when they are developed Key limitations These models need large amount of data for training These models assume a single monotonic, non-temporal ...
The Gamma process is a stochastic process with independent non-negative increments having a Gamma distribution with identical scale parameter [64] . ...
doi:10.1007/978-0-85729-320-6_42
fatcat:btxl5v2sgfgthiajwakue2o7ra
Stochastic variational hierarchical mixture of sparse Gaussian processes for regression
2018
Machine Learning
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Stochastic optimization can be employed to allow the application of the model to large-scale problems. ...
A two-step variational inference algorithm is developed to learn the global GP, the GP experts and the gating network simultaneously. ...
(42) ] in the first step, the term q( f k (x n )) is only needed when q(z n = k) is non-zero, i.e. r nk is non-zero. ...
doi:10.1007/s10994-018-5721-5
fatcat:nl3y7szs5zh2la6dwhj55wahfu
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