Fetching the paper…
Reading the bibliography…
Designing bounded-memory algorithms is becoming increasingly important nowadays.
The perceptron, a perceiving and recognizing automaton Project Para
Frank Rosenblatt · 1957
Earlier work this paper cites.
Relating data compression and learnability
Nick Littlestone and Manfred Warmuth · 1986
Earlier work this paper cites.
Occam’s razor
Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K Warmuth · 1987
Earlier work this paper cites.
Learning decision lists
Ronald L Rivest · 1987
Earlier work this paper cites.
Space-bounded learning and the vapnik-chervonenkis dimension
Sally Floyd · 1989
Earlier work this paper cites.
The strength of weak learnability
Robert E Schapire · 1990
Earlier work this paper cites.
Weakly learning dnf and characterizing statistical query learning using fourier analysis
Avrim Blum, Merrick Furst, Jeffrey Jackson, Michael Kearns, Yishay Mansour, and Steven Rudich · 1994
Earlier work this paper cites.
PAC learning with irrelevant attributes
Aditi Dhagat and Lisa Hellerstein · 1994
Earlier work this paper cites.
Efficient noise-tolerant learning from statistical queries
Michael Kearns · 1998
Earlier work this paper cites.
Madaboost: A modification of adaboost
Carlos Domingo and Osamu Watanabe · 2000
Cited alongside, same era.
Decision lists and related boolean functions
Thomas Eiter, Toshihide Ibaraki, and Kazuhisa Makino · 2002
Cited alongside, same era.
On online learning of decision lists
Ziv Nevo and Ran El-Yaniv · 2002
Cited alongside, same era.
Toward attribute efficient learning of decision lists and parities
Adam R Klivans and Rocco A Servedio · 2006
Cited alongside, same era.
Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions
Philip M Long and Rocco Servedio · 2007
Cited alongside, same era.
Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
Cited alongside, same era.
Time-space tradeoffs for learning from small test spaces: Learning low degree polynomial functions
Paul Beame, Shayan Oveis Gharan, and Xin Yang · 2017
Closest in time.
A general characterization of the statistical query complexity
Vitaly Feldman · 2017
Closest in time.
Extractor-based time-space lower bounds for learning
Sumegha Garg, Ran Raz, and Avishay Tal · 2017
Closest in time.
Time-space hardness of learning sparse parities
Gillat Kol, Ran Raz, and Avishay Tal · 2017
Closest in time.
Mixing implies lower bounds for space bounded learning
Dana Moshkovitz and Michal Moshkovitz · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fundamental limits of online and distributed algorithms for statistical learning and estimation
Ohad Shamir · 2014
Cited alongside, same era.
Fast learning requires good memory: A time-space lower bound for parity learning
Ran Raz · 2016
Cited alongside, same era.
Memory, communication, and statistical queries
Jacob Steinhardt, Gregory Valiant, and Stefan Wager · 2016
Cited alongside, same era.
Michal Moshkovitz and Naftali Tishby · 2017
Closest in time.
A time-space lower bound for a large class of learning problems
Ran Raz · 2017
Closest in time.
Entropy samplers and strong generic lower bounds for space bounded learning
Dana Moshkovitz and Michal Moshkovitz · 2018
Closest in time.
Internet live statistics
Worldometers · 2019
Closest in time.