Knowledge Tree
online learning
Facts (9)
Sources
Track: Poster Session 3 - aistats 2026 virtual.aistats.org 4 facts
claimTaS-FG (Track and Stop for Feedback Graphs) is an asymptotically optimal algorithm for pure exploration in online learning with feedback graphs.
claimThe study of pure exploration in online learning with a feedback graph examines scenarios ranging from full-information to pure bandit feedback and settings with no feedback on chosen actions.
claimThe researchers derived an instance-specific lower bound on the sample complexity of learning the best action with fixed confidence in online learning with feedback graphs, even when the graph is unknown and stochastic.
claimAleksandar Armacki, Shuhua Yu, Pranay Sharma, Gauri Joshi, Dragana Bajovic, Dusan Jakovetic, and Soummya Kar study high-probability convergence in online learning in the presence of heavy-tailed noise.
Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Apr 3, 2023 3 facts
claimStudents face significant challenges in acquiring knowledge efficiently online due to the presence of confusing or low-quality content on social media.
claimZablith (2022) proposed constructing a knowledge graph that integrates social media content with formal educational content to facilitate online learning.
claimKnowledge graphs can filter social media content to support formal learning and assist students with efficient online learning.
Exploring the Impact of Parenting Styles on the Social Development ... acr-journal.com 1 fact
referenceC. Y. Tan, Q. Pan, Y. Zhang, M. Lan, and N. Law published a 2022 study in Frontiers in Psychology titled 'Parental home monitoring and support and students’ online learning and socioemotional well-being during COVID-19 school suspension in Hong Kong', which investigates the impact of parental involvement on students during the pandemic.
A Survey on the Theory and Mechanism of Large Language Models arxiv.org Mar 12, 2026 1 fact
referenceThe paper 'Adaptive subgradient methods for online learning and stochastic optimization' was published in the Journal of Machine Learning Research 12 (7).