Published in: LIPIcs, Volume 207, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)
Sumegha Garg, Pravesh K. Kothari, Pengda Liu, and Ran Raz. Memory-Sample Lower Bounds for Learning Parity with Noise. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 207, pp. 60:1-60:19, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)
@InProceedings{garg_et_al:LIPIcs.APPROX/RANDOM.2021.60, author = {Garg, Sumegha and Kothari, Pravesh K. and Liu, Pengda and Raz, Ran}, title = {{Memory-Sample Lower Bounds for Learning Parity with Noise}}, booktitle = {Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)}, pages = {60:1--60:19}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-207-5}, ISSN = {1868-8969}, year = {2021}, volume = {207}, editor = {Wootters, Mary and Sanit\`{a}, Laura}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2021.60}, URN = {urn:nbn:de:0030-drops-147534}, doi = {10.4230/LIPIcs.APPROX/RANDOM.2021.60}, annote = {Keywords: memory-sample tradeoffs, learning parity under noise, space lower bound, branching program} }
Feedback for Dagstuhl Publishing