JMLR<p>'Supervised Learning with Evolving Tasks and Performance Guarantees', by Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano.</p><p><a href="http://jmlr.org/papers/v26/24-0343.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v26/24-0343.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a> <a href="https://sigmoid.social/tags/tasks" class="mention hashtag" rel="tag">#<span>tasks</span></a> <a href="https://sigmoid.social/tags/classification" class="mention hashtag" rel="tag">#<span>classification</span></a></p>
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#supervised
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JMLR<p>'Recursive Estimation of Conditional Kernel Mean Embeddings', by Ambrus Tamás, Balázs Csanád Csáji.</p><p><a href="http://jmlr.org/papers/v25/23-0168.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v25/23-0168.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/embeddings" class="mention hashtag" rel="tag">#<span>embeddings</span></a> <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a> <a href="https://sigmoid.social/tags/estimation" class="mention hashtag" rel="tag">#<span>estimation</span></a></p>
JMLR<p>'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.</p><p><a href="http://jmlr.org/papers/v25/22-1024.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v25/22-1024.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/causal" class="mention hashtag" rel="tag">#<span>causal</span></a> <a href="https://sigmoid.social/tags/causality" class="mention hashtag" rel="tag">#<span>causality</span></a> <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a></p>
JMLR<p>'Learning from many trajectories', by Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi.</p><p><a href="http://jmlr.org/papers/v25/23-1145.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v25/23-1145.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/trajectories" class="mention hashtag" rel="tag">#<span>trajectories</span></a> <a href="https://sigmoid.social/tags/trajectory" class="mention hashtag" rel="tag">#<span>trajectory</span></a> <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a></p>
JMLR<p>'An Entropy-Based Model for Hierarchical Learning', by Amir R. Asadi.</p><p><a href="http://jmlr.org/papers/v25/23-0096.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v25/23-0096.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a> <a href="https://sigmoid.social/tags/hierarchical" class="mention hashtag" rel="tag">#<span>hierarchical</span></a> <a href="https://sigmoid.social/tags/multiscale" class="mention hashtag" rel="tag">#<span>multiscale</span></a></p>
JMLR<p>'Semi-supervised Inference for Block-wise Missing Data without Imputation', by Shanshan Song, Yuanyuan Lin, Yong Zhou.</p><p><a href="http://jmlr.org/papers/v25/21-1504.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v25/21-1504.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/imputation" class="mention hashtag" rel="tag">#<span>imputation</span></a> <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a> <a href="https://sigmoid.social/tags/neuroimaging" class="mention hashtag" rel="tag">#<span>neuroimaging</span></a></p>
JMLR<p>'Distributed Estimation on Semi-Supervised Generalized Linear Model', by Jiyuan Tu, Weidong Liu, Xiaojun Mao.</p><p><a href="http://jmlr.org/papers/v25/22-0670.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v25/22-0670.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a> <a href="https://sigmoid.social/tags/distributed" class="mention hashtag" rel="tag">#<span>distributed</span></a> <a href="https://sigmoid.social/tags/estimation" class="mention hashtag" rel="tag">#<span>estimation</span></a></p>
JMLR<p>'Sample-efficient Adversarial Imitation Learning', by Dahuin Jung, Hyungyu Lee, Sungroh Yoon.</p><p><a href="http://jmlr.org/papers/v25/23-0314.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v25/23-0314.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/imitation" class="mention hashtag" rel="tag">#<span>imitation</span></a> <a href="https://sigmoid.social/tags/adversarial" class="mention hashtag" rel="tag">#<span>adversarial</span></a> <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a></p>
JMLR<p>'On Truthing Issues in Supervised Classification', by Jonathan K. Su.</p><p><a href="http://jmlr.org/papers/v25/19-301.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v25/19-301.htm</span><span class="invisible">l</span></a> <br /> <br /><a href="https://sigmoid.social/tags/classification" class="mention hashtag" rel="tag">#<span>classification</span></a> <a href="https://sigmoid.social/tags/classifier" class="mention hashtag" rel="tag">#<span>classifier</span></a> <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a></p>
JMLR<p>'Dimensionality Reduction and Wasserstein Stability for Kernel Regression', by Stephan Eckstein, Armin Iske, Mathias Trabs.</p><p><a href="http://jmlr.org/papers/v24/22-0303.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v24/22-0303.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/regression" class="mention hashtag" rel="tag">#<span>regression</span></a> <a href="https://sigmoid.social/tags/pca" class="mention hashtag" rel="tag">#<span>pca</span></a> <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a></p>
JMLR<p>'Weisfeiler and Leman go Machine Learning: The Story so far', by Christopher Morris et al.</p><p><a href="http://jmlr.org/papers/v24/22-0240.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v24/22-0240.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/graphs" class="mention hashtag" rel="tag">#<span>graphs</span></a> <a href="https://sigmoid.social/tags/graph" class="mention hashtag" rel="tag">#<span>graph</span></a> <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a></p>
JMLR<p>'Fair Data Representation for Machine Learning at the Pareto Frontier', by Shizhou Xu, Thomas Strohmer.</p><p><a href="http://jmlr.org/papers/v24/22-0005.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v24/22-0005.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/wasserstein" class="mention hashtag" rel="tag">#<span>wasserstein</span></a> <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a> <a href="https://sigmoid.social/tags/fairness" class="mention hashtag" rel="tag">#<span>fairness</span></a></p>
JMLR<p>'The Power of Contrast for Feature Learning: A Theoretical Analysis', by Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang.</p><p><a href="http://jmlr.org/papers/v24/21-1501.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v24/21-1501.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/autoencoders" class="mention hashtag" rel="tag">#<span>autoencoders</span></a> <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a> <a href="https://sigmoid.social/tags/generative" class="mention hashtag" rel="tag">#<span>generative</span></a></p>
JMLR<p>'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.</p><p><a href="http://jmlr.org/papers/v24/21-0187.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v24/21-0187.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a> <a href="https://sigmoid.social/tags/labeled" class="mention hashtag" rel="tag">#<span>labeled</span></a> <a href="https://sigmoid.social/tags/annotated" class="mention hashtag" rel="tag">#<span>annotated</span></a></p>
JMLR<p>'Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research', by Jorg Bornschein et al.</p><p><a href="http://jmlr.org/papers/v24/22-1345.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v24/22-1345.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/recognition" class="mention hashtag" rel="tag">#<span>recognition</span></a> <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a> <a href="https://sigmoid.social/tags/classification" class="mention hashtag" rel="tag">#<span>classification</span></a></p>
JMLR<p>'Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction', by Jue Hou, Zijian Guo, Tianxi Cai.</p><p><a href="http://jmlr.org/papers/v24/21-1075.html" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v24/21-1075.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/imputation" class="mention hashtag" rel="tag">#<span>imputation</span></a> <a href="https://sigmoid.social/tags/predictors" class="mention hashtag" rel="tag">#<span>predictors</span></a> <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a></p>
Carl Gold, PhD<p>New OfferFit <a href="https://sigmoid.social/tags/whitepaper" class="mention hashtag" rel="tag">#<span>whitepaper</span></a> on going from <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a> <a href="https://sigmoid.social/tags/machinelearning" class="mention hashtag" rel="tag">#<span>machinelearning</span></a> to <a href="https://sigmoid.social/tags/reinforcementlearning" class="mention hashtag" rel="tag">#<span>reinforcementlearning</span></a> - 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 😎</p><p><a href="https://offerfit.ai/content/white-paper/time-to-let-your-ai-out-of-the-box" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">https://</span><span class="ellipsis">offerfit.ai/content/white-pape</span><span class="invisible">r/time-to-let-your-ai-out-of-the-box</span></a></p>
Published papers at TMLR<p>Mitigating Confirmation Bias in Semi-supervised Learning via Efficient Bayesian Model Averaging</p><p>Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj et al.</p><p>Action editor: Frederic Sala.</p><p><a href="https://openreview.net/forum?id=PRrKOaDQtQ" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">https://</span><span class="ellipsis">openreview.net/forum?id=PRrKOa</span><span class="invisible">DQtQ</span></a></p><p><a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a> <a href="https://sigmoid.social/tags/labeling" class="mention hashtag" rel="tag">#<span>labeling</span></a> <a href="https://sigmoid.social/tags/classifier" class="mention hashtag" rel="tag">#<span>classifier</span></a></p>
New Submissions to TMLR<p>Semi-Supervised Single Domain Generalization with Label-Free Adversarial Data Augmentation</p><p><a href="https://openreview.net/forum?id=sUlbRfLijj" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">https://</span><span class="ellipsis">openreview.net/forum?id=sUlbRf</span><span class="invisible">Lijj</span></a></p><p><a href="https://sigmoid.social/tags/adversarial" class="mention hashtag" rel="tag">#<span>adversarial</span></a> <a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a> <a href="https://sigmoid.social/tags/generalization" class="mention hashtag" rel="tag">#<span>generalization</span></a></p>
Published papers at TMLR<p>Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment a...</p><p>Viraj Uday Prabhu, David Acuna, Rafid Mahmood et al.</p><p>Action editor: Hongsheng Li.</p><p><a href="https://openreview.net/forum?id=lAQQx7hlku" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">https://</span><span class="ellipsis">openreview.net/forum?id=lAQQx7</span><span class="invisible">hlku</span></a></p><p><a href="https://sigmoid.social/tags/supervised" class="mention hashtag" rel="tag">#<span>supervised</span></a> <a href="https://sigmoid.social/tags/adapting" class="mention hashtag" rel="tag">#<span>adapting</span></a> <a href="https://sigmoid.social/tags/adaptation" class="mention hashtag" rel="tag">#<span>adaptation</span></a></p>
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