Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimization

Marshal Arijona Sinaga, Julien Martinelli, Samuel Kaski; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:1-26

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Wavelet Conditional Neural Processes

Junyu Xuan, Mengjing Wu; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:27-49

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Universality of Singular Complexity for Hyvärinen Generalized Bayes: Exact Transfer in Gaussian Factor Analysis

Manoj Saravanan, Rohit Kumar Salla; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:50-85

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An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms

Nils Grünefeld, Jes Frellsen, Christian Hardmeier; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:86-118

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Causal Temporal Graphs for Counterfactual Validation of Temporal Link Prediction

Aniq Ur Rahman, Justin Coon; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:119-134

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Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling

Malinda Lu, Yue-Jane Liu, Matthew K. Nock, Yaniv Yacoby; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:135-188

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Conditionally Identifiable Latent Representation for Multivariate Time Series with Structural Dynamics

Minkey Chang, Jae-Young Kim; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:189-213

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When Individually Calibrated Models Become Collectively Miscalibrated

Zhaohui Geoffrey Wang; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:214-255

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Characterizing the Representational Capacity of Neural Processes

Robin Young; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:256-289

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Identifiability, Fisher Information, and Amortized Inference for Heterogeneous Diffusion from Discrete-Time Noisy Observations

Zhen Yuan Yeo; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:290-313

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Heterogeneous Coupled Diffusion for Graph Generation with $α$-Stable Node Feature Noise

Chengyu Tang, Ercan Engin Kuruoglu; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:314-345

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Uncertainty Propagation Through Green’s Kernels and Gaussian Process Inference Dynamics

Chi-Jen Roger Lo, Joan Lasenby; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:346-366

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RAMP: Recognition parametrisation by Amortised Message Passing

Lior Fox, Kai Biegun, James Heald, Samo Hromadka, Arielle Rosinski, Maneesh Sahani; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:367-397

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Intention Inference Under Execution Noise: Separating Aleatoric and Epistemic Uncertainty in Social Dilemmas

Kival Mahadew, Jonathan P. Shock; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:398-424

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Latent Semantic Regularization: Enhancing Semantic Integrity in Tabular Data Synthesis

Saba Amiri, Carlijn Nijhuis, Eric Nalisnick, Adam Belloum, Sander Klous, Leon Gommans; Proceedings of The 1st Symposium on Probabilistic Machine Learning, PMLR 327:425-443

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