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In his breakthrough paper, Raz showed that any parity learning algorithm requires either quadratic memory or an exponential number of samples [FOCS'16, JACM'19].
Fast parallel matrix inversion algorithms
L. Csanky · 1976
Earlier work this paper cites.
Bounded-width polynomial-size branching programs recognize exactly those languages in nc 1
David A. Mix Barrington · 1986
Earlier work this paper cites.
Fundamental limits of online and distributed algorithms for statistical learning and estimation
Ohad Shamir · 2014
Earlier work this paper cites.
Fast learning requires good memory: A time-space lower bound for parity learning
Ran Raz · 2016
Earlier work this paper cites.
Memory, communication, and statistical queries
Jacob Steinhardt, Gregory Valiant, and Stefan Wager · 2016
Earlier work this paper cites.
Time-space hardness of learning sparse parities
Gillat Kol, Ran Raz, and Avishay Tal · 2017
Earlier work this paper cites.
Mixing implies lower bounds for space bounded learning
Dana Moshkovitz and Michal Moshkovitz · 2017
Earlier work this paper cites.
Mixing complexity and its applications to neural networks
Michal Moshkovitz and Naftali Tishby · 2017
Cited alongside, same era.
A time-space lower bound for a large class of learning problems
Ran Raz · 2017
Cited alongside, same era.
Time-space tradeoffs for learning finite functions from random evaluations, with applications to polynomials
Paul Beame, Shayan Oveis Gharan, and Xin Yang · 2018
Cited alongside, same era.
Detecting correlations with little memory and communication
Yuval Dagan and Ohad Shamir · 2018
Cited alongside, same era.
Extractor-based time-space lower bounds for learning
Sumegha Garg, Ran Raz, and Avishay Tal · 2018
Cited alongside, same era.
Entropy samplers and strong generic lower bounds for space bounded learning
Time-space lower bounds for two-pass learning
Sumegha Garg, Ran Raz, and Avishay Tal · 2019
Later among the works it cites.
Memory-sample tradeoffs for linear regression with small error
Vatsal Sharan, Aaron Sidford, and Gregory Valiant · 2019
Later among the works it cites.
Time-space tradeoffs for distinguishing distributions and applications to security of goldreich’s PRG
Sumegha Garg, Pravesh K. Kothari, and Ran Raz · 2020
Later among the works it cites.
Towards a combinatorial characterization of bounded-memory learning
Alon Gonen, Shachar Lovett, and Michal Moshkovitz · 2020
Later among the works it cites.
Memory-sample lower bounds for learning parity with noise
Sumegha Garg, Pravesh K. Kothari, Pengda Liu, and Ran Raz · 2021
Later among the works it cites.
Efficient convex optimization requires superlinear memory
Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant · 2022
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Dana Moshkovitz and Michal Moshkovitz · 2018
Cited alongside, same era.
Space lower bounds for linear prediction in the streaming model
Yuval Dagan, Gil Kur, and Ohad Shamir · 2019
Cited alongside, same era.
Later among the works it cites.
Memory-sample lower bounds for learning with classical-quantum hybrid memory
Qipeng Liu, Ran Raz, and Wei Zhan · 2023
Closest in time.