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1.
Izdebski, A.* et al.: Synergistic benefits of joint molecule generation and property prediction. Trans. Machine Learn. Res. 2026-January, 1-32 (2026)
2.
Kazeminia, S. ; Marr, C. & Rieck, B.: Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarios. Trans. Machine Learn. Res. 2026-February, accepted (2026)
3.
Angelis, E. ; Quinzan, F.* ; Soleymani, A.* ; Jail, P.J.* & Bauer, S.: Double machine learning based structure identification from temporal data. Trans. Machine Learn. Res. 2025, accepted (2025)
4.
Manduchi, L.* et al.: On the challenges and opportunities in generative AI. Trans. Machine Learn. Res. 2025, accepted (2025)
5.
Mueller, T.T.* et al.: Are Population Graphs Really as Powerful as Believed? Trans. Machine Learn. Res. 2024, accepted (2024)
6.
Nasirigerdeh, R.* ; Torkzadehmahani, R.* ; Rueckert, D.* & Kaissis, G.: Kernel normalized convolutional networks. Trans. Machine Learn. Res. 2024, 107-118 (2024)
7.
Quinzan, F.* ; Casolo, C. ; Muandet, K.* ; Luo, Y.* & Kilbertus, N.: Learning counterfactually invariant predictors. Trans. Machine Learn. Res. 2024, accepted (2024)
8.
Sharma, M.* ; Rainforth, T.* ; Teh, Y.W.* & Fortuin, V.: Incorporating unlabelled data into bayesian neural networks. Trans. Machine Learn. Res. 2024, accepted (2024)
9.
von Rohrscheidt, J.C. ; Rieck, B. & Schmon, S.M.*: Bayesian computation meets topology. Trans. Machine Learn. Res. 2024, accepted (2024)
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