arXiv AI
Jul 22
RAMP: Recognition parametrisation by Amortised Message Passing
arXiv:2607. 18883v1 Announce Type: cross Abstract: A central aim of unsupervised learning is to uncover latent factors that explain dependencies among observations.
Hierarchical Latent Structure Learning through Online Inference
arXiv:2603. 19139v2 Announce Type: replace Abstract: Learning systems must balance generalization across experiences with discrimination of task-relevant details.
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arXiv Machine Learning
Jun 5
Discrete Causal Representations from Heterogeneous Domains: A Bayesian Approach with Social Survey Applications
arXiv:2606. 06288v1 Announce Type: cross Abstract: Causal representation learning aims to infer the high-level latent causal concepts that give rise to observed low-level measurements.
Hugging Face Trending Papers
Jul 29
Temporally Centered SIGReg Improves Multi-Task LeWorldModel Learning: From Analysis to Method
Recent work on LeWorldModel (LeWM) has shown that the Sketched Isotropic Gaussian Regularizer (SIGReg) enables stable end-to-end world-model learning from pixels by regularizing the latent marginal distribution toward an isotropic Gaussian, thereby preventing representation collapse. While effective and elegant in single-task settings, this recipe does not extend reliably to multi-task training, leading to substantially worse downstream behavior-cloning performance.
arXiv Statistics ML
3d ago
Amortized Bayesian Inference on Multilevel Models of Arbitrary Structure
arXiv:2609.40024v1 Announce Type: new Abstract: We develop a general method for amortized Bayesian inference on multilevel models of arbitrary structure. Given a generative model specified as a direc...
arXiv Machine Learning
4d ago
High-Dimensional Simulation-Based Inference in Latent Spaces
arXiv:2609.37381v1 Announce Type: new Abstract: Neural simulation-based inference (SBI) has been widely successful in inferring a relatively small number of interpretable parameters from potentially...
arXiv Machine Learning
Jul 30
Temporally Centered SIGReg Improves Multi-Task LeWorldModel Learning: From Analysis to Method
arXiv:2607. 26924v1 Announce Type: new Abstract: Recent work on LeWorldModel (LeWM) has shown that the Sketched Isotropic Gaussian Regularizer (SIGReg) enables stable end-to-end world-model learning from pixels by regularizing the latent marginal distribution toward an isotropic Gaussian, thereby preventing representation collapse.
arXiv Machine Learning
1d ago
Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports
The paper introduces Multi-Task Anti-Causal learning (MTAC), a framework that estimates latent causes from observed effects by exploiting both task-invariant and task-specific structural dependencies. MTAC constructs a structural equation model that separates a shared backbone mechanism from task-specific deviations, then uses maximum a posteriori inference to reconstruct causes. Applied to urban event reconstruction—parking violations, abandoned properties, and unsanitary conditions—MTAC outperforms strong baselines on real data from Manhattan and Newark, achieving up to a 33.04% reduction in mean absolute error.
Hugging Face Trending Papers
3d ago
Amortized Bayesian Inference on Multilevel Models of Arbitrary Structure
We develop a general method for amortized Bayesian inference on multilevel models of arbitrary structure. Given a generative model specified as a directed acyclic graph, our method automatically deriv...
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.
Hugging Face Trending Papers
Sep 15
Large Language Models Develop Belief State Geometry In-Context
Large language models (LLMs) trained on next-token prediction exhibit remarkable in-context learning (ICL) abilities, yet the representations that support ICL remain poorly understood. We consider suc...
arXiv AI
4d ago
Abductive World Modeling via Causal Representation Learning
arXiv:2609.36985v1 Announce Type: cross Abstract: The central challenge of world modeling is to learn representations that capture how the world evolves. However, existing world models predominantly...
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.