A blast from the past (specifically #DS2023): "Prioritizing the essential: A robust evaluation framework for novelty detection" by Jean-Gabriel Gaudreault & Paula Branco (link.springer.com/article/10.100…)

And a blast from the future (ECML PKDD 2025): "Navigating explanatory multiverse through counterfactual path geometry" by Kacper Sokol, Edward Small & Yueqing Xuan. Open access to boot: link.springer.com/article/10.100… #OA

Another #MLJ open access paper for your perusal: "One transformer for all time series: representing and training with time-dependent heterogeneous tabular data" by Simone Luetto, Fabrizio Garuti, Enver Sangineto, Lorenzo Forni & Rita Cucchiara (link.springer.com/article/10.100…) #OA

It might not be clear from the title but this is a meta-learning #MLJ paper: "Imbalanced regression pipeline recommendation" by Juscimara G. Avelino, George D. C. Cavalcanti & Rafael M. O. Cruz (link.springer.com/article/10.100…)

We've an open access #MLJ article for you: "Learning answer set programs with aggregates via sampling and genetic programming". Single author: Damiano Azzolini! (link.springer.com/article/10.100…) #OA

A breath of fresh air - a rather applied #MLJ paper: "FLight: optimization algorithm for traffic lights based on short-term traffic state forecast" by Daimin Liu & Jian Huang (link.springer.com/article/10.100…)

"Rope-net: deep convolutional neural network via robust principal component analysis" by Baichen Liu, Zhi Han, Xi’ai Chen, Yanmei Wang & Yandong Tang (link.springer.com/article/10.100…)

Open Access is nice! #MLJ's got one for you: "A comparison of latent space modeling techniques in a plain-vanilla autoencoder setting" by Fabian Kächele, Maximilian Coblenz & Oliver Grothe (link.springer.com/article/10.100…)

Open access #MLJ article: "Graph spring neural ODEs for link sign prediction" by Andrin Rehmann & Alexandre Bovet (link.springer.com/article/10.100…) #OA

"Constraint guided autoencoders for joint optimization of condition indicator estimation and anomaly detection in machine condition monitoring" by Maarten Meire, Quinten Van Baelen, Ted Ooijevaar & Peter Karsmakers (link.springer.com/article/10.100…)

Another open access paper: "Learning using statistical invariants: when they work and when they don’t" by Yang Liu, Ernest Fokoue & Daniel E. Krutz (link.springer.com/article/10.100…) #OA

"A new adaptive gradient method with gradient decomposition" by Zhou Shao, Hang Zhou & Tong Lin (link.springer.com/article/10.100…)

The open access #MLJ articles keep on coming: "Tight mixed-integer optimization formulations for prescriptive trees" by Max Biggs & Georgia Perakis (link.springer.com/article/10.100…) #OA

Open access #MLJ article: "Simultaneous outlier detection and elimination in hyperspectral unmixing via weighted non-negative matrix tri-factorization" by Y. Hashemi-Nazari, F. Saberi-Movahed, A. Tajaddini, C. Moreira, X. Ning & P. Tiwari (link.springer.com/article/10.100…) #OA

"Towards interpretable and robust UAV-based foundation model for endangered species monitoring in complex ecosystems" by Jihan Zhang, Mingqiao Han, K. H. Laurie, Benyun Zhao, Lei Lei, Xi Chen, Hon Chi Judy Wan, Siu Gin Cheung, Wenxing Hong & Ben M. Chen (link.springer.com/article/10.100…)

"Cherry on the cake: fairness is NOT an optimization problem" by Marco Favier & Toon Calders (link.springer.com/article/10.100…)

"The KANDY benchmark: Incremental neuro-symbolic learning and reasoning with Kandinsky patterns" by Luca Salvatore Lorello, Marco Lippi & Stefano Melacci (link.springer.com/article/10.100…)

Another ECML PKDD 2025 paper, and it's open access! "Jensen–Tsallis divergence for supervised classification under data imbalance" by Antonio Squicciarini, Tom Trigano & David Luengo (link.springer.com/article/10.100…) #OA