Fetching the paper…
Reading the bibliography…
We examine Contextualized Machine Learning (ML), a paradigm for learning heterogeneous and context-dependent effects.
Identifiability of a linear relation between variables which are subject to error
Olav Reiersøl · 1950
Earlier work this paper cites.
Varying-coefficient models
Trevor Hastie and Robert Tibshirani · 1993
Earlier work this paper cites.
On global identifiability for arbitrary model parametrizations
Lennart Ljung and Torkel Glad · 1994
Earlier work this paper cites.
Multitask learning
Rich Caruana · 1997
Earlier work this paper cites.
Predicting multivariate responses in multiple linear regression
Leo Breiman and Jerome H Friedman · 1997
Earlier work this paper cites.
Identifiablity of models for clusterwise linear regression
Christian Hennig · 2000
Earlier work this paper cites.
Efficient estimation and inferences for varying-coefficient models
Zongwu Cai, Jianqing Fan, and Runze Li · 2000
Earlier work this paper cites.
Testing homogeneity in gamma mixture models
Xin Liu, Cristian Pasarica, and Yongzhao Shao · 2003
Earlier work this paper cites.
Testing homogeneity in a mixture distribution via the l 2 distance between competing models
Richard Charnigo and Jiayang Sun · 2004
Earlier work this paper cites.
Generalized varying coefficient models with unknown link function
CN Kuruwita, KB Kulasekera, and CM Gallagher · 2011
Earlier work this paper cites.
Estimation in generalised varying-coefficient models with unspecified link functions
Wenyang Zhang, Degui Li, and Yingcun Xia · 2015
Earlier work this paper cites.
Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
Personalized characterization of diseases using sample-specific networks
Xiaoping Liu, Yuetong Wang, Hongbin Ji, Kazuyuki Aihara, and Luonan Chen · 2016
Cited alongside, same era.
Generating visual explanations
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell · 2016
Cited alongside, same era.
Not just a black box: Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje · 2016
Cited alongside, same era.
Model-agnostic interpretability of machine learning
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Contextual explanation networks
Maruan Al-Shedivat, Avinava Dubey, and Eric P Xing · 2017
Cited alongside, same era.
Identifiability of nonparametric mixture models and bayes optimal clustering
Bryon Aragam, Chen Dan, Eric P Xing, and Pradeep Ravikumar · 2018
Later among the works it cites.
Unremarkable ai: Fitting intelligent decision support into critical, clinical decision-making processes
Qian Yang, Aaron Steinfeld, and John Zimmerman · 2019
Later among the works it cites.
Learning sample-specific models with low-rank personalized regression
Benjamin J. Lengerich, Bryon Aragam, and Eric P Xing · 2019
Later among the works it cites.
Estimating sample-specific regulatory networks
Marieke Lydia Kuijjer, Matthew George Tung, GuoCheng Yuan, John Quackenbush, and Kimberly Glass · 2019
Later among the works it cites.
Identification and estimation in quantile varying-coefficient models with unknown link function
Lili Yue, Gaorong Li, and Heng Lian · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Interpretable & explorable approximations of black box models
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec · 2017
Cited alongside, same era.
Personalized survival prediction with contextual explanation networks
Maruan Al-Shedivat, Avinava Dubey, and Eric P Xing · 2018
Cited alongside, same era.
Contextual parameter generation for universal neural machine translation
Emmanouil Antonios Platanios, Mrinmaya Sachan, Graham Neubig, and Tom Mitchell · 2018
Cited alongside, same era.
Personalized regression enables sample-specific pan-cancer analysis
Benjamin J Lengerich, Bryon Aragam, and Eric P Xing · 2018
Cited alongside, same era.
Instance-specific bayesian network structure learning
Fattaneh Jabbari, Shyam Visweswaran, and Gregory F. Cooper · 2018
Cited alongside, same era.
Learning subject-specific directed acyclic graphs with mixed effects structural equation models from observational data
Xiang Li, Shanghong Xie, Peter McColgan, Sarah J Tabrizi, Rachael I Scahill, Donglin Zeng, and Yuanjia Wang · 2018
Cited alongside, same era.
Testing for homogeneity in mixture models
Jiaying Gu, Roger Koenker, and Stanislav Volgushev · 2018
Cited alongside, same era.
Estimation and identification of a varying-coefficient additive model for locally stationary processes
Lixia Hu, Tao Huang, and Jinhong You · 2019
Later among the works it cites.
Sample-Specific Models for Precision Medicine
Benjamin Lengerich · 2020
Later among the works it cites.
Purifying interaction effects with the functional anova: An efficient algorithm for recovering identifiable additive models
Benjamin Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, and Rich Caruana · 2020
Later among the works it cites.
Neural additive models: Interpretable machine learning with neural nets
Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Rich Caruana, and Geoffrey E Hinton · 2021
Later among the works it cites.
Notmad: Estimating bayesian networks with sample-specific structures and parameters
Ben Lengerich, Caleb Ellington, Bryon Aragam, Eric P Xing, and Manolis Kellis · 2021
Later among the works it cites.
Dropout as a regularizer of interaction effects
Benjamin J Lengerich, Eric Xing, and Rich Caruana · 2022
Later among the works it cites.