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Sample + Seek

Bolin Ding, Silu Huang, Surajit Chaudhuri, Kaushik Chakrabarti, Chi Wang
2016 Proceedings of the 2016 International Conference on Management of Data - SIGMOD '16  
We then study how to provide fast approximate answers to aggregation queries with distribution precision guaranteed within a userspecified error bound.  ...  To address those limitations, we first introduce a new precision metric, called distribution precision, to express such error guarantees.  ...  CONCLUSIONS We propose an AQP system based on a novel sample+seek framework. Distribution precision is guaranteed in answers to aggregation queries.  ... 
doi:10.1145/2882903.2915249 dblp:conf/sigmod/DingHCC016 fatcat:ozu2b2nw2fg7pemm26nbv72oqu

Unified frameworks for sampled-data extremum seeking control: Global optimisation and multi-unit systems

Sei Zhen Khong, Dragan Nešić, Ying Tan, Chris Manzie
2013 Automatica  
Multi-unit extremum seeking is also investigated with the objective of accelerating the speed of convergence.  ...  Two frameworks are proposed for extremum seeking of general nonlinear plants based on a sampled-data control law, within which a broad class of nonlinear programming methods is accommodated.  ...  Fig. 1 . 1 Extremum seeking algorithm with noisy output measurement. Fig. 2 . 2 Sampled-data extremum seeking control.  ... 
doi:10.1016/j.automatica.2013.06.020 fatcat:ktlwcxjqwjax3hrrlntco35v2m

Stochastic generalized Nash equilibrium seeking under partial-decision information [article]

Barbara Franci, Sergio Grammatico
2021 arXiv   pre-print
We propose several distributed algorithms for network games and aggregative games that we show being special instances of a preconditioned forward-backward splitting method.  ...  We prove that the algorithms converge to a generalized Nash equilibrium when the forward operator is restricted cocoercive by using the stochastic approximation scheme with variance reduction to estimate  ...  Since (12) implies that the second moment of the error diminishes with the number of samples M k , algorithms using the approximation in (9) are also known as variancereduced methods [19] .  ... 
arXiv:2011.05357v2 fatcat:u5l6wdn4djhu7pcyouot5xuxuu

Reinforcement Learning for Online Information Seeking [article]

Xiangyu Zhao and Long Xia and Jiliang Tang and Dawei Yin
2019 arXiv   pre-print
With recent great advances in deep reinforcement learning (DRL), there have been increasing interests in developing DRL based information seeking techniques.  ...  cumulative long-term reward from users where reward has different definitions according to information seeking applications such as click-through rate, revenue, user satisfaction and engagement.  ...  These are two-time-scale algorithms where the critic uses Temporal-Difference (TD) learning with a linear approximation architecture and the actor is updated in an approximate gradient direction based  ... 
arXiv:1812.07127v4 fatcat:pyc75g5hufcs5b3f75gonbkp24

Segregation That No One Seeks*

Ryan Muldoon, Tony Smith, Michael Weisberg
2012 Philosophy of Science  
This set of 100 samples was then compared to a set of 100 randomly generated distributions of agents on the grid using precisely the same procedure as above.  ...  We began with 498 agents of three types distributed randomly over our toroidal grid.  ... 
doi:10.1086/663236 fatcat:arqqedde4zfurpdczgxmslxa2q

Desperately Seeking (Environmental) Kuznets

Marzio Galeotti, Alessandro Lanza
1999 Social Science Research Network  
The number of studies seeking to empirically characterize the reduced-form relationship between a country economic growth and the quantity of various pollutants produced has recently increased significantly  ...  While all the studies have focused upon the empirical emergence of the environmental Kuznets curve and have typically discussed its implications with special reference to the value of the income turning  ...  The philosophy behind the above criteria is that the best fitting model is the closest approximation to the data generating process.  ... 
doi:10.2139/ssrn.158340 fatcat:22jyc2uvnnhctcqsen5kkclnvi

Desperately seeking environmental Kuznets

Marzio Galeotti, Alessandro Lanza
2005 Environmental Modelling & Software  
The number of studies seeking to empirically characterize the reduced-form relationship between a country economic growth and the quantity of various pollutants produced has recently increased significantly  ...  While all the studies have focused upon the empirical emergence of the environmental Kuznets curve and have typically discussed its implications with special reference to the value of the income turning  ...  The philosophy behind the above criteria is that the best fitting model is the closest approximation to the data generating process.  ... 
doi:10.1016/j.envsoft.2004.09.018 fatcat:ciu72tpnzjgs7eqnys2hbt7imi

Distributed Algorithms for Stochastic Source Seeking with Mobile Robot Networks: Technical Report [article]

Nikolay A. Atanasov and Jerome Le Ny and George J. Pappas
2014 arXiv   pre-print
Our approach is distributed, robust to deformations in the group geometry, does not necessitate global localization, and is guaranteed to lead the sensors to a neighborhood of a local maximum of the field  ...  We develop algorithms specific to two scenarios: one in which the sensors have a precise model of the signal formation process and one in which a signal model is not available.  ...  In Sec. 2 we describe the considered source-seeking scenarios precisely.  ... 
arXiv:1402.0051v2 fatcat:jjtkgscaqfafllaomhruwwx5vm

Interactive Machine Comprehension with Information Seeking Agents [article]

Xingdi Yuan, Jie Fu, Marc-Alexandre Cote, Yi Tay, Christopher Pal, Adam Trischler
2020 arXiv   pre-print
We repurpose SQuAD and NewsQA as an initial case study, and then show how the interactive corpora can be used to train a model that seeks relevant information through sequential decision making.  ...  In A2C, exploration is performed implicitly by sampling from a probability distribution over the action space; although entropy regularization is applied, good exploration is still not guaranteed (if there  ...  More precisely, at a step t during the information gathering phase, the encoder reads observation string o t and question string q to generate the attention aggregated hidden representations M t .  ... 
arXiv:1908.10449v3 fatcat:futb3lxn7fgg7bdg7gfdjzml7e

Let's Play Hide-and-Seek

Steve Bongiovanni, Milan Borkovec, Robert D. Sinclair
2006 The Journal of Trading  
ITG Solutions Network, Inc. does not guarantee its accuracy or completeness and ITG Solutions Network, Inc. does not make any warranties regarding results from usage.  ...  With the advent of "smart" algorithmic trading systems driven in part by more transparent data offerings from market venues, information leakage from order placement is the nightmare of any market participant  ...  To get a representative sample of tickers across the universe, we rank all available tickers (approximately 7,000) according to their 21-day median trade volume at the beginning of our sample period and  ... 
doi:10.3905/jot.2006.644087 fatcat:jq2thopkbbaubiu4s2hplfdc7a

Progressive Mode-Seeking on Graphs for Sparse Feature Matching [chapter]

Chao Wang, Lei Wang, Lingqiao Liu
2014 Lecture Notes in Computer Science  
We further design a density-aware sampling technique to considerably accelerate mode-seeking.  ...  We further design a density-aware sampling technique to considerably accelerate modeseeking.  ...  [12] approximate the whole feature space by using a greatly reduced number of points randomly sampled from the distribution defined by KDE. The speed-up is proportional to the sub-sampling factor.  ... 
doi:10.1007/978-3-319-10605-2_51 fatcat:f2s2ssmfyrdejal4msekmyxhvq

Joins on Samples: A Theoretical Guide for Practitioners [article]

Dawei Huang, Dong Young Yoon, Seth Pettie, Barzan Mozafari
2020 arXiv   pre-print
We study limitations of offline samples in approximating join queries: given an offline sampling budget, how well can one approximate the join of two tables?  ...  We then define a hybrid sampling scheme that captures all combinations of stratified, universe, and Bernoulli sampling, and show that this scheme with our optimal parameters achieves the theoretical lower  ...  Any summary of T1 that can estimate an AVG query with precision δ with probability at least 2/3 must have a size of at least Ω(n/(tδ 2 )).  ... 
arXiv:1912.03443v4 fatcat:fzvc7j4l5nfujjojzacsq3d2ty

Secure Random Sampling in Differential Privacy [article]

Naoise Holohan, Stefano Braghin
2021 arXiv   pre-print
This paper presents a practical solution to the finite-precision floating point vulnerability, where the inverse transform sampling of the Laplace distribution can itself be inverted, thus enabling an  ...  attack where the original value can be retrieved with non-negligible advantage.  ...  Of particular interest is the ability to generate samples from the Laplace distribution in a single statement of code (Section 5.2), with strong attack guarantees.  ... 
arXiv:2107.10138v2 fatcat:igtgvr3psjb6fg2ixkaafsf2ha

Differential Privacy By Sampling [article]

Josh Joy, Mario Gerla
2017 arXiv   pre-print
In this paper we present the Sampling Privacy mechanism for privately releasing personal data. Sampling Privacy is a sampling based privacy mechanism that satisfies differential privacy.  ...  We also require a distributed set of aggregators or trusted aggregator whereby at least one aggregator does not collude with the others.  ...  The intuition is that when sampling approximates the original aggregate information, an attacker is unable to distinguish when sampling is performed and which data owners are sampled.  ... 
arXiv:1708.01884v1 fatcat:rfliovxkerasloujlbyuyw2skm

BOSH: Bayesian Optimization by Sampling Hierarchically [article]

Henry B. Moss, David S. Leslie, Paul Rayson
2020 arXiv   pre-print
To solve this problem, we propose Bayesian Optimization by Sampling Hierarchically (BOSH), a novel BO routine pairing a hierarchical Gaussian process with an information-theoretic framework to generate  ...  However, disregarding the true objective function in this manner finds a high-precision optimum of the wrong function.  ...  For GP initialization, we randomly sample one more evaluation than kernel parameters (to guarantee identifiability).  ... 
arXiv:2007.00939v1 fatcat:2zxffyjqtjhetkhr7p2vuhpag4
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