• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 24 matches

• 2023
• 
  David Holzmüller:
  Regression from linear models to neural networks: double descent, active learning, and sampling. University of Stuttgart, Germany, 2023
• 
  Retracted: Active Learning Query Strategies for Linear Regression Based on Efficient Global Optimization. J. Electr. Comput. Eng. 2023: 9787854:1-9787854:1 (2023)
• 
  XiaoYan Chen:
  Linear regression active learning based on IRD algorithm. ICISE 2023: 457-461
• 2022
• 
  Kyriacos Yiasemides, Katerina Zachariadou, Nikolaos Moshonas, Maria Rangoussi, Angelos Charitopoulos:
  Development and Assessment of a Web-based Platform for an Active Learning Physics Lab Session on the linear regression technique. EDUCON 2022: 946-955
• 
  Cameron Musco, Christopher Musco, David P. Woodruff, Taisuke Yasuda:
  Active Linear Regression for ℓp Norms and Beyond. FOCS 2022: 744-753
• 
  Nived Rajaraman, Devvrit, Pranjal Awasthi:
  Semi-supervised Active Linear Regression. NeurIPS 2022
• 
  (Withdrawn) Active Learning Query Strategies for Linear Regression Based on Efficient Global Optimization. J. Electr. Comput. Eng. 2022: 2891463:1-2891463:16 (2022)
• 2021
• 
  Xavier Fontaine, Pierre Perrault, Michal Valko, Vianney Perchet:
  Online A-Optimal Design and Active Linear Regression. ICML 2021: 3374-3383
• 
  Cameron Musco, Christopher Musco, David P. Woodruff, Taisuke Yasuda:
  Active Sampling for Linear Regression Beyond the $\ell_2$ Norm. CoRR abs/2111.04888 (2021)
• 2020
• 
  Sergio Matiz, Kenneth E. Barner:
  Conformal prediction based active learning by linear regression optimization. Neurocomputing 388: 157-169 (2020)
• 
  Ziang Liu, Dongrui Wu:
  Unsupervised Pool-Based Active Learning for Linear Regression. CoRR abs/2001.05028 (2020)
• 2019
• 
  Xue Chen, Eric Price:
  Active Regression via Linear-Sample Sparsification. COLT 2019: 663-695
• 
  Xavier Fontaine, Pierre Perrault, Vianney Perchet:
  Active Linear Regression. CoRR abs/1906.08509 (2019)
• 2017
• 
  Carlos Riquelme, Ramesh Johari, Baosen Zhang:
  Online Active Linear Regression via Thresholding. AAAI 2017: 2506-2512
• 2016
• 
  Xiaohua Li, Jian Zheng:
  Joint machine learning and human learning design with sequential active learning and outlier detection for linear regression problems. CISS 2016: 407-411
• 
  Elvis Dohmatob, Michael Eickenberg, Bertrand Thirion, Gaël Varoquaux:
  Local Q-linear convergence and finite-time active set identification of ADMM on a class of penalized regression problems. ICASSP 2016: 4752-4756
• 
  Carlos Riquelme, Ramesh Johari, Baosen Zhang:
  Online Active Linear Regression via Thresholding. CoRR abs/1602.02845 (2016)
• 2014
• 
  Zohar Shay Karnin, Elad Hazan:
  Hard-Margin Active Linear Regression. ICML 2014: 883-891
• 2012
• 
  Nozomi Kurihara, Masashi Sugiyama:
  Improving importance estimation in pool-based batch active learning for approximate linear regression. Neural Networks 36: 73-82 (2012)
• 
  Yasuhiro Sogawa, Tsuyoshi Ueno, Yoshinobu Kawahara, Takashi Washio:
  Robust Active Learning for Linear Regression via Density Power Divergence. ICONIP (3) 2012: 594-602
• 2010
• 
  Jingu Kim, Haesun Park:
  Fast Active-set-type Algorithms for L1-regularized Linear Regression. AISTATS 2010: 397-404
• 2009
• 
  Masashi Sugiyama, Shinichi Nakajima:
  Pool-based active learning in approximate linear regression. Mach. Learn. 75(3): 249-274 (2009)
• 2008
• 
  Masashi Sugiyama, Neil Rubens:
  Active Learning with Model Selection in Linear Regression. SDM 2008: 518-529
• 2006
• 
  Masashi Sugiyama:
  Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error. J. Mach. Learn. Res. 7: 141-166 (2006)