Lmst

'Entropic Gromov-Wasserstein Distances: Stability and Algorithms', by Gabriel Rioux, Ziv Goldfeld, Kengo Kato.

http://jmlr.org/papers/v25/24-0039.html

#regularization #wasserstein #variational

Bayesian Meta-Learning Is All You Need

— Why is the deterministic view of meta-learning not sufficient?

— What is the variational inference?

— How can we design neural-based Bayesian meta-learning algorithms?

https://jameskle.com/writes/bayesian-meta-learning-is-all-you-need

#machinelearning #bayesian #metalearning #variational

Computational flow of VERSA for few-shot classifications with the context-independent approximation — as presented in “Meta-Learning Probabilistic Inference for Prediction.”

'Structured Optimal Variational Inference for Dynamic Latent Space Models', by Peng Zhao, Anirban Bhattacharya, Debdeep Pati, Bani K. Mallick.

http://jmlr.org/papers/v25/22-0514.html

#variational #models #priors

'A Framework for Improving the Reliability of Black-box Variational Inference', by Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins.

http://jmlr.org/papers/v25/22-0327.html

#variational #adaptively #optimization

`Using the framework of utility-calibrated #variational inference, we unify Gaussian process approximation & data acquisition into a joint #optimization problem, thereby ensuring optimal decisions under a limited computational budget. Our approach can be used with any decision-theoretic acquisition function and is compatible with trust region methods like TuRBO... Our approach outperforms standard SVGPs on high-dimensional benchmark tasks in control and molecular design`

https://arxiv.org/abs/2406.04308

'A Variational Approach to Bayesian Phylogenetic Inference', by Cheng Zhang, Frederick A. Matsen IV.

http://jmlr.org/papers/v25/22-0348.html

#phylogenetic #bayesian #variational

'Low-rank Variational Bayes correction to the Laplace method', by Janet van Niekerk, Haavard Rue.

http://jmlr.org/papers/v25/21-1405.html

#variational #hyperparameters #approximations

'Additive smoothing error in backward variational inference for general state-space models', by Mathis Chagneux, Elisabeth Gassiat, Pierre Gloaguen, Sylvain Le Corff.

http://jmlr.org/papers/v25/22-1392.html

#variational #smoothing #estimation

'Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box', by Ryan Giordano, Martin Ingram, Tamara Broderick.

http://jmlr.org/papers/v25/23-1015.html

#variational #optimizer #optimizing

Os VAE são fascinantes...😮

https://youtu.be/pea3sH6orMc?si=YXY5U3I5WBn6HcPu

#autoencoder #variational #diffusion #VAE

'Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables', by Hamid Mousavi, Jakob Drefs, Florian Hirschberger, Jörg Lücke.

http://jmlr.org/papers/v24/22-0359.html

#probabilistic #sparse #variational

'Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics', by Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet.

http://jmlr.org/papers/v24/22-1160.html

#variational #divergence #estimators