Convergence Aspects of Hybrid Kernel SVGD
Anson MacDonald, Scott A Sisson, Sahani Pathiraja
Action editor: Yingzhen Li
https://openreview.net/forum?id=JZkbMSQDmD
#svgd #kernelised #variational
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#variational
Latest posts tagged with #variational on Bluesky
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Posts tagged #variational
Variational Online Mirror Descent for Robust Learning in Schrödinger Bridge
Dong-Sig Han, Jaein Kim, HEE BIN YOO, Byoung-Tak Zhang
Action editor: Chris Maddison
https://openreview.net/forum?id=g3SsM9FLpm
#schrödinger #variational #generative
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Generalized Smooth Stochastic Variational Inequalities: Almost Sure Convergence and Convergence ...
Daniil Vankov, Angelia Nedich, Lalitha Sankar
Action editor: Yunwen Lei
https://openreview.net/forum?id=EjqSpbUBWU
#extragradient #variational #stochastic
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G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving
Naoki Murata, Chieh-Hsin Lai, Yuhta Takida et al.
Action editor: Ying Nian Wu
https://openreview.net/forum?id=fj23qnVifX
#diffusion #generative #variational
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New #J2C Certification:
Generalized Smooth Stochastic Variational Inequalities: Almost Sure Convergence and Convergence ...
Daniil Vankov, Angelia Nedich, Lalitha Sankar
https://openreview.net/forum?id=EjqSpbUBWU
#extragradient #variational #stochastic
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New #J2C Certification:
G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving
Naoki Murata, Chieh-Hsin Lai, Yuhta Takida et al.
https://openreview.net/forum?id=fj23qnVifX
#diffusion #generative #variational
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New #J2C Certification:
Learning Energy-Based Generative Models via Potential Flow: A Variational Principle Approach to P...
Junn Yong Loo, Leong Fang Yu, Michelle Adeline et al.
https://openreview.net/forum?id=vc7poEYOFK
#generative #variational #flow
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New #J2C Certification:
Tighter sparse variational Gaussian processes
Thang D Bui, Matthew Ashman, Richard E. Turner
https://openreview.net/forum?id=L33DSu3zvq
#sparse #variational #gaussian
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New #J2C Certification:
Optimization Guarantees for Square-Root Natural-Gradient Variational Inference
Navish Kumar, Thomas Möllenhoff, Mohammad Emtiyaz Khan, Aurelien Lucchi
https://openreview.net/forum?id=OMOFmb6ve7
#variational #gradient #parameterization
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New #J2C Certification:
Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics
Hanming Yang, Antonio Khalil Moretti, Sebastian Macaluso et al.
https://openreview.net/forum?id=pCapRF2vFf
#generative #jet #variational
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Optimization Guarantees for Square-Root Natural-Gradient Variational Inference
Navish Kumar, Thomas Möllenhoff, Mohammad Emtiyaz Khan, Aurelien Lucchi
Action editor: Jan-Willem van de Meent
https://openreview.net/forum?id=OMOFmb6ve7
#variational #gradient #parameterization
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Variational Framework Enhances Adaptive Neural PDE Solvers
Researchers introduced a variational framework applying convex transforms to PDE residuals with exponential or linear weights. Tests on fluid dynamics benchmarks showed reduced error. Read more: getnews.me/variational-framework-en... #variational #neuralpde
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NeuVAS: New Variational Method for Neural Implicit Shape Modeling
NeuVAS, a framework for implicit shape modeling, launched June 2025 and updated September 2025. It adds smoothness and G0 sharp‑feature handling for surfaces from sparse 3D sketches. getnews.me/neuvas-new-variational-m... #neuralimplicit #variational
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Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
Xiaoyu Jiang, Sokratia Georgaka, Magnus Rattray, Mauricio A Álvarez
Action editor: Vincent Fortuin
https://openreview.net/forum?id=kK0WrBZAli
#mogp #variational #batches
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Tighter sparse variational Gaussian processes
Thang D Bui, Matthew Ashman, Richard E. Turner
Action editor: Geoff Pleiss
https://openreview.net/forum?id=L33DSu3zvq
#sparse #variational #gaussian
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Latent mixed-effect models for high-dimensional longitudinal data
Priscilla Ong, Manuel Haussmann, Otto Lönnroth, Harri Lähdesmäki
Action editor: Stefan Feuerriegel
https://openreview.net/forum?id=7A96yteeF9
#longitudinal #variational #autoencoders
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Learning Energy-Based Generative Models via Potential Flow: A Variational Principle Approach to P...
Junn Yong Loo, Leong Fang Yu, Michelle Adeline et al.
Action editor: Hankook Lee
https://openreview.net/forum?id=vc7poEYOFK
#generative #variational #flow
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Variational Stochastic Gradient Descent for Deep Neural Networks
Anna Kuzina, Haotian Chen, Babak Esmaeili, Jakub M. Tomczak
Action editor: Markus Heinonen
https://openreview.net/forum?id=xu4ATNjcdy
#gradients #gradient #variational
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New #Expert Certification:
Latent mixed-effect models for high-dimensional longitudinal data
Priscilla Ong, Manuel Haussmann, Otto Lönnroth, Harri Lähdesmäki
https://openreview.net/forum?id=7A96yteeF9
#longitudinal #variational #autoencoders
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Stabilizing the Kumaraswamy Distribution
Max Wasserman, Gonzalo Mateos
Action editor: Diana Cai
https://openreview.net/forum?id=baZLwdphqw
#bandits #variational #tensorflow
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Variational Neural Stochastic Differential Equations with Change Points
Yousef El-Laham, Zhongchang Sun, Haibei Zhu, Tucker Balch, Svitlana Vyetrenko
Action editor: Michael Gutmann
https://openreview.net/forum?id=GEilvtsFNV
#stochastic #autoencoder #variational
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Doubly Robust Conditional VAE via Decoder Calibration: An Implicit KL Annealing Approach
Chuanhui Liu, Xiao Wang
Action editor: Yingzhen Li
https://openreview.net/forum?id=VIkycTWDWo
#autoencoders #variational #vae
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Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics
Hanming Yang, Antonio Khalil Moretti, Sebastian Macaluso et al.
Action editor: Alexander Alemi
https://openreview.net/forum?id=pCapRF2vFf
#generative #jet #variational
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Exact Fractional Inference via Re-Parametrization \& Interpolation between Tree-Re-Weighted- and ...
Hamidreza Behjoo, Michael Chertkov
Action editor: Florent Krzakala
https://openreview.net/forum?id=AWRpSgaNfc
#fractional #approximations #variational
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Sparsifying Bayesian neural networks with latent binary variables and normalizing flows
Lars Skaaret-Lund, Geir Storvik, Aliaksandr Hubin
Action editor: Pierre Alquier
https://openreview.net/forum?id=d6kqUKzG3V
#variational #lbbnn #models
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Variational Inference on the Final-Layer Output of Neural Networks
Yadi Wei, Roni Khardon
Action editor: Jasper Snoek
https://openreview.net/forum?id=mTOzXLmLKr
#variational #bayesian #rademacher
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The network training can be described as constructing a #variational approximation, where we approximate the intractable posterior with a simplified and tractable distribution — to learn the variational posterior we minimize the Kullback-Leibler (KL) divergence
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