arXiv Machine Learning
Jun 5
Environment-Robust Representation Learning with Empirical Bayes
arXiv:2606. 05365v1 Announce Type: cross Abstract: We consider multi-environment prediction problems.
Bayesian Invariance Modeling of Multi-Environment Data
arXiv:2506. 22675v4 Announce Type: replace-cross Abstract: Invariant prediction [Peters et al.
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arXiv Machine Learning
Jul 21
Unveiling Invariant and Transferable Latent Factors Across Heterogeneous Environments via ATLAS
arXiv:2607. 18209v1 Announce Type: cross Abstract: This paper considers a multi-environment factor model in which high-dimensional covariates are collected from heterogeneous environments, with auxiliary labels available in a subset of these environments.
arXiv Machine Learning
Jun 19
Environment-Adaptive Covariate Selection: Learning When to Use Spurious Correlations for Out-of-Distribution Prediction
arXiv:2601. 02322v2 Announce Type: replace-cross Abstract: A common approach to out-of-distribution prediction restricts models to causal or invariant covariates to avoid spurious associations that may change across environments.
Hugging Face Trending Papers
Jul 20
Unveiling Invariant and Transferable Latent Factors Across Heterogeneous Environments via ATLAS
This paper considers a multi-environment factor model in which high-dimensional covariates are collected from heterogeneous environments, with auxiliary labels available in a subset of these environments. The joint distribution of the covariates may vary across environments, whereas the latent structure is decomposed into invariant factors with shared loadings and heterogeneous factors with environment-specific loadings.
arXiv Machine Learning
Jun 16
Structured Nonparametric Variational Inference for Dependent Latent Modeling
arXiv:2606. 15458v1 Announce Type: cross Abstract: Variational inference (VI) is a core engine of modern AI, enabling scalable approximate Bayesian learning and uncertainty-aware training of large probabilistic and generative models.
arXiv Machine Learning
Jun 24
Predictive variational inference: Learn the predictively optimal posterior distribution
arXiv:2410. 14843v4 Announce Type: replace-cross Abstract: Vanilla variational inference finds an optimal approximation to the Bayesian posterior distribution, but even the exact Bayesian posterior is often not meaningful under model misspecification.
arXiv Machine Learning
Jul 24
Generative Bayesian Filtering for State Estimation
arXiv:2607. 20521v1 Announce Type: new Abstract: The state of a dynamic system evolves over time, switching among several latent modes that govern its observable behavior.
arXiv Machine Learning
Jun 25
Gaussian Mean Field Variational Inference can Overestimate Predictive Variance
arXiv:2606. 25745v1 Announce Type: cross Abstract: Mean Field Variational Inference (MFVI) is widely understood to underestimate posterior variance.
arXiv Machine Learning
Jun 19
Multi-Task Bayesian In-Context Learning
arXiv:2606. 20538v1 Announce Type: new Abstract: Bayesian predictive inference provides a principled framework for uncertainty quantification, data efficiency, and robust generalization.
arXiv Machine Learning
Jun 30
Deep probabilistic model synthesis enables unified modeling of whole-brain neural activity across individual subjects
arXiv:2603. 14161v2 Announce Type: replace Abstract: Many disciplines need quantitative models that synthesize experimental data across multiple instances of the same general system.
arXiv Machine Learning
Jul 2
Generative Model Proposal based Particle Filtering for Data Assimilation
arXiv:2607. 01012v1 Announce Type: new Abstract: Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications.
arXiv Machine Learning
Jun 18
Anti-causal domain generalization: Leveraging unlabeled data
arXiv:2602. 17187v2 Announce Type: replace-cross Abstract: The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments.