HS2: Active learning over hypergraphs with pointwise and pairwise queries

被引:0
|
作者
Chien, I. [1 ]
Zhou, Huozhi [1 ]
Li, Pan [1 ]
机构
[1] UIUC, Dept ECE, Urbana, IL 61820 USA
基金
美国国家科学基金会;
关键词
PROBABILITY-INEQUALITIES;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We propose a hypergraph-based active learning scheme which we term HS2; HS2 generalizes the previously reported algorithm S-2 originally proposed for graph-based active learning with pointwise queries [1]. Our HS2 method can accommodate hypergraph structures and allows one to ask both pointwise queries and pairwise queries. Based on a novel parametric system particularly designed for hypergraphs, we derive theoretical results on the query complexity of HS2 for the above described generalized settings. Both the theoretical and empirical results show that HS2 requires a significantly fewer number of queries than S-2 when one uses S-2 over a graph obtained from the corresponding hypergraph via clique expansion.
引用
收藏
页数:10
相关论文
共 7 条
  • [1] Reflection and Double Loop Learning: The Case of HS2
    Synnott, Michael
    TEACHING PUBLIC ADMINISTRATION, 2013, 31 (01) : 124 - 134
  • [2] Functional nucleosomal phases over HS2 of the human beta-LCR1.
    Kiyama, R
    Onishi, Y
    Wada-Kiyama, Y
    BLOOD, 2001, 98 (11) : 499A - 499A
  • [3] MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning
    Wang, Menghan
    Guo, Yuchen
    Zhao, Zhenqi
    Hu, Guangzheng
    Shen, Yuming
    Gong, Mingming
    Torr, Philip
    PROCEEDINGS OF THE 45TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '22), 2022, : 2105 - 2109
  • [4] A comprehensive nucleosome study of human K562 cells:: Active dinucleosomes at HS2 of β-LCR
    Kiyama, R
    Kato, M
    Onishi, Y
    Wada-Kiyama, Y
    BLOOD, 2003, 102 (11) : 518A - 519A
  • [5] Synthesis of noncoding enhancer RNAs initiated by HS2 enhancer regulates enhancer-promoter interaction over distance.
    Ling, JH
    Ainol, L
    Pi, WH
    Yu, XP
    Ling, Z
    Tuan, D
    BLOOD, 2002, 100 (11) : 241A - 241A
  • [6] Predicting the rates of photocatalytic hydrogen evolution over cocatalyst-deposited TiO2 using machine learning with active photon flux as a unifying feature
    Haghshenas, Yousof
    Wong, Wei Ping
    Gunawan, Denny
    Khataee, Alireza
    Keyikoglu, Ramazan
    Razmjou, Amir
    Kumar, Priyank Vijaya
    Toe, Cui Ying
    Masood, Hassan
    Amal, Rose
    Sethu, Vidhyasaharan
    Teoh, Wey Yang
    EES CATALYSIS, 2024, 2 (02): : 612 - 623
  • [7] Revealing the structure of the active sites for the electrocatalytic CO2 reduction to CO over Co single atom catalysts using operando XANES and machine learning
    Martini, Andrea
    Timoshenko, Janis
    Ruescher, Martina
    Hursan, Dorottya
    Monteiro, Mariana C. O.
    Liberra, Eric
    Cuenya, Beatriz Roldan
    JOURNAL OF SYNCHROTRON RADIATION, 2024, 31 : 741 - 750