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Symbolic Density Estimators for Unnormalized Distributions
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arXiv Machine Learning
Oct 9
Symbolic Density Estimators for Unnormalized Distributions
The paper introduces a framework that combines deep generative models with symbolic regression to estimate symbolic expressions for unnormalized probability density functions directly from observed samples. By incorporating domain-specific priors—such as interaction ranges and a set of primitive functions—the method can factorize large distributions and remain tractable. Experiments demonstrate its effectiveness on multivariate toy distributions and on lattice models from computational physics, including the XY model and φ⁴ theory, where it produces compact symbolic approximations of the Hamiltonian across scales.
arXiv Machine Learning
Jun 2
Flow-Transformed Implicit Processes for Function-Space Variational Inference
arXiv:2606. 01954v1 Announce Type: new Abstract: Implicit-process priors define distributions over functions through flexible generative mechanisms, making them attractive for Bayesian function-space modelling.
arXiv Machine Learning
Oct 19
Acquisition Function-Guided Generation for Bayesian Optimization over Discrete Spaces
arXiv:2610.22763v1 Announce Type: new Abstract: Bayesian optimization (BO) over discrete spaces is challenging due to missing gradients and the sparsity of valid solutions. Latent-space BO sidesteps...
arXiv Machine Learning
Aug 4
Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows
arXiv:2512. 04954v3 Announce Type: replace Abstract: We present a novel technique for amortized posterior estimation using Normalizing Flows trained with likelihood-weighted importance sampling.
arXiv Machine Learning
Aug 11
Leveraging generative models to assist Monte Carlo sampling
arXiv:2608. 07648v1 Announce Type: cross Abstract: Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to statistical physics and molecular simulation.
arXiv Machine Learning
5d ago
Active Learning of Conditional Generative Models via the Transport Neural Tangent Kernel
The paper introduces the transport Neural Tangent Kernel (tNTK) to enable active learning for conditional generative models that produce distributional outputs. By decomposing the Wasserstein test risk into mean and shape components, the authors show that mean‑based active learning misses distributional shape, and propose tNTK as a tractable kernel that captures sensitivity of the predicted distribution to model parameters. Using tNTK as a Gaussian process covariance, they greedily select inputs that minimize posterior variance, and demonstrate that this distribution‑aware strategy outperforms mean‑based NTK baselines across several generative model types and datasets.