Covariate Shift Adaptation for Structured Regression With Frank–Wolfe Algorithms by Santosh V. Chapaneri, Deepak J. Jayaswal

Subjects: “…Covariate shift…”
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Certifying Black-Box Policies With Stability for Nonlinear Control by Tongxin Li, Ruixiao Yang, Guannan Qu, Yiheng Lin, Adam Wierman, Steven H. Low

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Improving imbalance classification via ensemble learning based on two-stage learning by Na Liu, Jiaqi Wang, Yongtong Zhu, Lihong Wan, Qingdu Li

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Impact of the Covid-19 pandemic on the performance of machine learning algorithms for predicting perioperative mortality by D. I. Andonov, B. Ulm, M. Graessner, A. Podtschaske, M. Blobner, B. Jungwirth, S. M. Kagerbauer

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Unsupervised Detection of Covariate Shift Due to Changes in EEG Headset Position: Towards an Effective Out-of-Lab Use of Passive Brain–Computer Interface by Daniele Germano, Nicolina Sciaraffa, Vincenzo Ronca, Andrea Giorgi, Giacomo Trulli, Gianluca Borghini, Gianluca Di Flumeri, Fabio Babiloni, Pietro Aricò

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Analysis of Continual Learning Models for Intrusion Detection System by Sai Prasath, Kamalakanta Sethi, Dinesh Mohanty, Padmalochan Bera, Subhransu Ranjan Samantaray

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Leveraging Unseen Features along with their PLM-based Representation to Handle Negative Covariate Shift Problem in Text Classification by Wasi Nesar Ahmad, Abulaish Muhammad

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Direct domain adaptation through reciprocal linear transformations by Tariq Alkhalifah, Oleg Ovcharenko

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On the Decomposition of Covariate Shift Assumption for the Set-to-Set Matching by Masanari Kimura

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Susceptibility of AutoML mortality prediction algorithms to model drift caused by the COVID pandemic by Simone Maria Kagerbauer, Bernhard Ulm, Armin Horst Podtschaske, Dimislav Ivanov Andonov, Manfred Blobner, Bettina Jungwirth, Martin Graessner

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The Effects of Individual Differences, Non-Stationarity, and the Importance of Data Partitioning Decisions for Training and Testing of EEG Cross-Participant Models by Alexander Kamrud, Brett Borghetti, Christine Schubert Kabban

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A Generalized Linear Joint Trained Framework for Semi-Supervised Learning of Sparse Features by Juan Carlos Laria, Line H. Clemmensen, Bjarne K. Ersbøll, David Delgado-Gómez

Subjects: “…covariate shift…”
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The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey by Patrick Glauner, Jorge Augusto Meira, Petko Valtchev, Radu State, Franck Bettinger

Subjects: “…Covariate shift…”
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Geostatistical Learning: Challenges and Opportunities by Júlio Hoffimann, Maciel Zortea, Breno de Carvalho, Bianca Zadrozny

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AdamB: Decoupled Bayes by Backprop With Gaussian Scale Mixture Prior by Keigo Nishida, Makoto Taiji

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Undersmoothing Causal Estimators With Generative Trees by Damian Machlanski, Spyridon Samothrakis, Paul Clarke

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Unsupervised Domain Adaptation in Activity Recognition: A GAN-Based Approach by Andrea Rosales Sanabria, Franco Zambonelli, Juan Ye

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Factorizable Joint Shift in Multinomial Classification by Dirk Tasche

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Efficient deep convolutional neural networks using CReLU for ATR with limited SAR images by Zelong Wang, Xianghui Xu

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