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Random sampling is a fundamental primitive in modern algorithms, statistics, and machine learning, used as a generic method to obtain a small yet "representative" subset of the data.
A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations
Herman Chernoff · 1952
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On the uniform convergence of relative frequencies of events to their probabilities
V. Vapnik and A. Chervonenkis · 1971
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On the density of families of sets
Norbert Sauer · 1972
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A combinatorial problem; stability and order for models and theories in infinitary languages
Saharon Shelah · 1972
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On tail probabilities for martingales
David A. Freedman · 1975
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A theory of the learnable
L. G. Valiant · 1984
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Random sampling with a reservoir
Jeffrey Scott Vitter · 1985
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Clocked adversaries for hashing
Richard J. Lipton and Jeffrey F. Naughton · 1993
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Sharper bounds for gaussian and empirical processes
Michel Talagrand · 1994
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Approximating center points with iterative radon points
Kenneth L. Clarkson, David Eppstein, Gary L. Miller, Carl Sturtivant, and Shang-Hua Teng · 1996
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The Art of Computer Programming, Volume 2 (3rd Ed.): Seminumerical Algorithms
Donald E. Knuth · 1997
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Concentration
Colin McDiarmid · 1998
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Random sampling techniques for space efficient online computation of order statistics of large datasets
Gurmeet Singh Manku, Sridhar Rajagopalan, and Bruce G. Lindsay · 1999
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The discrepancy method - randomness and complexity
Bernard Chazelle · 2001
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Space-efficient online computation of quantile summaries
Michael Greenwald and Sanjeev Khanna · 2001
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Improved bounds on the sample complexity of learning
Yi Li, Philip M. Long, and Aravind Srinivasan · 2001
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The gigascope stream database
Charles D. Cranor, Theodore Johnson, Oliver Spatscheck, and Vladislav Shkapenyuk · 2003
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Online maintenance of very large random samples
Chris Jermaine, Abhijit Pol, and Subramanian Arumugam · 2004
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Range counting over multidimensional data streams
Subhash Suri, Csaba D. Tóth, and Yunhong Zhou · 2004
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Sketching streams through the net: Distributed approximate query tracking
Graham Cormode and Minos N. Garofalakis · 2005
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Estimating flow distributions from sampled flow statistics
Nick G. Duffield, Carsten Lund, and Mikkel Thorup · 2005
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Sampling algorithms in a stream operator
Theodore Johnson, S. Muthukrishnan, and Irina Rozenbaum · 2005
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Concentration inequalities and martingale inequalities: a survey
Fan Chung and Linyuan Lu · 2006
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Weighted random sampling with a reservoir
Pavlos S. Efraimidis and Paul G. Spirakis · 2006
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Deterministic sampling and range counting in geometric data streams
Amitabha Bagchi, Amitabh Chaudhary, David Eppstein, and Michael T. Goodrich · 2007
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Logarithmic regret algorithms for online convex optimization
Elad Hazan, Amit Agarwal, and Satyen Kale · 2007
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Smoothness, low noise and fast rates
Nathan Srebro, Karthik Sridharan, and Ambuj Tewari · 2010
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Efficient stream sampling for variance-optimal estimation of subset sums
Edith Cohen, Nick G. Duffield, Haim Kaplan, Carsten Lund, and Mikkel Thorup · 2011
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A simple message-optimal algorithm for random sampling from a distributed stream
Yung-Yu Chung, Srikanta Tirthapura, and David P. Woodruff · 2016
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Quantiles and equi-depth histograms over streams
Michael B. Greenwald and Sanjeev Khanna · 2016
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State-of-the-art on clustering data streams
Mohammed Ghesmoune, Mustapha Lebbah, and Hanene Azzag · 2016
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Introduction to online convex optimization
Elad Hazan · 2016
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Optimal quantile approximation in streams
Zohar Karnin, Kevin Lang, and Edo Liberty · 2016
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Algorithms for distributed functional monitoring
Graham Cormode, S. Muthukrishnan, and Ke Yi · 2011
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Sketching in adversarial environments
Ilya Mironov, Moni Naor, and Gil Segev · 2011
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Principles of Distributed Database Systems
M. Tamer Özsu and Patrick Valduriez · 2011
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Continuous sampling from distributed streams
Graham Cormode, S. Muthukrishnan, Ke Yi, and Qin Zhang · 2012
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Recovering simple signals
Anna C. Gilbert, Brett Hemenway, Atri Rudra, Martin J. Strauss, and Mary Wootters · 2012
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Reusable low-error compressive sampling schemes through privacy
Anna C. Gilbert, Brett Hemenway, Martin J. Strauss, David P. Woodruff, and Mary Wootters · 2012
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Epsilon-approximations and epsilon-nets
Nabil H. Mustafa and Kasturi R. Varadarajan · 2017
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
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Online learning with local permutations and delayed feedback
Ohad Shamir and Liran Szlak · 2017
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Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
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PAC-learning in the presence of evasion adversaries
Daniel Cullina, Arjun Nitin Bhagoji, and Prateek Mittal · 2018
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Graph sparsification, spectral sketches, and faster resistance computation, via short cycle decompositions
Timothy Chu, Yu Gao, Richard Peng, Sushant Sachdeva, Saurabh Sawlani, and Junxing Wang · 2018
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Making machine learning robust against adversarial inputs
Ian J. Goodfellow, Patrick D. McDaniel, and Nicolas Papernot · 2018
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Stochastic bandits robust to adversarial corruptions
Thodoris Lykouris, Vahab Mirrokni, and Renato Paes Leme · 2018
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Background class defense against adversarial examples
Michael McCoyd and David A. Wagner · 2018
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DARTS: deceiving autonomous cars with toxic signs
Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, and Prateek Mittal · 2018
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Don’t decay the learning rate, increase the batch size, 2017
Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying, and Quoc V. Le · 2018
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The everlasting database: Statistical validity at a fair price
Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, and Nati Srebro · 2018
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Adaptive online learning in dynamic environments
Lijun Zhang, Shiyin Lu, and Zhi-Hua Zhou · 2018
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Weighted reservoir sampling from distributed streams
Rajesh Jayaram, Gokarna Sharma, Srikanta Tirthapura, and David P. Woodruff · 2019
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Vc classes are adversarially robustly learnable, but only improperly
Omar Montasser, Steve Hanneke, and Nathan Srebro · 2019
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Can adversarially robust learning leverage computational hardness?
Saeed Mahloujifar and Mohammad Mahmoody · 2019
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