'A Comparative Evaluation of Quantification Methods', by Tobias Schumacher, Markus Strohmaier, Florian Lemmerich.

http://jmlr.org/papers/v26/21-0241.html

#classifiers #supervised #quantification

'Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response', by Jue Hou, Rajarshi Mukherjee, Tianxi Cai.

http://jmlr.org/papers/v26/23-1587.html

#supervised #annotated #annotate

'Optimizing Data Collection for Machine Learning', by Rafid Mahmood, James Lucas, Jose M. Alvarez, Sanja Fidler, Marc T. Law.

http://jmlr.org/papers/v26/23-0292.html

#supervised #deep #collecting

'Supervised Learning with Evolving Tasks and Performance Guarantees', by Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano.

http://jmlr.org/papers/v26/24-0343.html

#supervised #tasks #classification

'Recursive Estimation of Conditional Kernel Mean Embeddings', by Ambrus Tamás, Balázs Csanád Csáji.

http://jmlr.org/papers/v25/23-0168.html

#embeddings #supervised #estimation

'On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models', by Xuetong Wu, Mingming Gong, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu.

http://jmlr.org/papers/v25/22-1024.html

#causal #causality #supervised

'Learning from many trajectories', by Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi.

http://jmlr.org/papers/v25/23-1145.html

#trajectories #trajectory #supervised

'An Entropy-Based Model for Hierarchical Learning', by Amir R. Asadi.

http://jmlr.org/papers/v25/23-0096.html

#supervised #hierarchical #multiscale

'Semi-supervised Inference for Block-wise Missing Data without Imputation', by Shanshan Song, Yuanyuan Lin, Yong Zhou.

http://jmlr.org/papers/v25/21-1504.html

#imputation #supervised #neuroimaging

'Distributed Estimation on Semi-Supervised Generalized Linear Model', by Jiyuan Tu, Weidong Liu, Xiaojun Mao.

http://jmlr.org/papers/v25/22-0670.html

#supervised #distributed #estimation

'Sample-efficient Adversarial Imitation Learning', by Dahuin Jung, Hyungyu Lee, Sungroh Yoon.

http://jmlr.org/papers/v25/23-0314.html

#imitation #adversarial #supervised

'On Truthing Issues in Supervised Classification', by Jonathan K. Su.

http://jmlr.org/papers/v25/19-301.html

#classification #classifier #supervised

'Dimensionality Reduction and Wasserstein Stability for Kernel Regression', by Stephan Eckstein, Armin Iske, Mathias Trabs.

http://jmlr.org/papers/v24/22-0303.html

#regression #pca #supervised

'Weisfeiler and Leman go Machine Learning: The Story so far', by Christopher Morris et al.

http://jmlr.org/papers/v24/22-0240.html

#graphs #graph #supervised

'Fair Data Representation for Machine Learning at the Pareto Frontier', by Shizhou Xu, Thomas Strohmer.

http://jmlr.org/papers/v24/22-0005.html

#wasserstein #supervised #fairness

'The Power of Contrast for Feature Learning: A Theoretical Analysis', by Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang.

http://jmlr.org/papers/v24/21-1501.html

#autoencoders #supervised #generative

'Semi-Supervised Off-Policy Reinforcement Learning and Value Estimation for Dynamic Treatment Regimes', by Aaron Sonabend-W, Nilanjana Laha, Ashwin N. Ananthakrishnan, Tianxi Cai, Rajarshi Mukherjee.

http://jmlr.org/papers/v24/21-0187.html

#supervised #labeled #annotated

'Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research', by Jorg Bornschein et al.

http://jmlr.org/papers/v24/22-1345.html

#recognition #supervised #classification

'Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction', by Jue Hou, Zijian Guo, Tianxi Cai.

http://jmlr.org/papers/v24/21-1075.html

#imputation #predictors #supervised

New OfferFit #whitepaper on going from #supervised #machinelearning to #reinforcementlearning - there can be a lot of issues if you try to pick next best actions with supervised models alone! (The white paper cites my own blog post on the subject

https://offerfit.ai/content/white-paper/time-to-let-your-ai-out-of-the-box