Awesome Statistics Theory

Lipschitzian SLLNs for random functions (2026)

Lai Tian et al.

5.49

Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction (2026)

Yurui Zheng et al.

5.01

Statistical Inference for Rank Allocation in Low-Rank Adaptation (2026)

Yihang Gao et al.

4.39

Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling (2016)

Christophe Dupuy (SIERRA) et al.

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Asymptotically exact inference in differentiable generative models (2016)

Matthew M. Graham and Amos J. Storkey

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Understanding Probabilistic Sparse Gaussian Process Approximations (2016)

Matthias Bauer et al.

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Fast robustness quantification with variational Bayes (2016)

Ryan Giordano et al.

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Variational Inference with Hamiltonian Monte Carlo (2016)

Christopher Wolf et al.

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Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation (2016)

Thomas Brouwer et al.

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Variational Inference via $\chi$-Upper Bound Minimization (2016)

Adji B. Dieng et al.

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Improving variational methods via pairwise linear response identities (2016)

Jack Raymond and Federico Ricci-Tersenghi

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Variational Boosting: Iteratively Refining Posterior Approximations (2016)

Andrew C. Miller et al.

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Gaussian-Dirichlet Posterior Dominance in Sequential Learning (2017)

Ian Osband and Benjamin Van Roy

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The variational Laplace approach to approximate Bayesian inference (2017)

Jean Daunizeau

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On parameters transformations for emulating sparse priors using variational-Laplace inference (2017)

Jean Daunizeau

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Variational Sequential Monte Carlo (2017)

Christian A. Naesseth and Scott W. Linderman and Rajesh Ranganath and David M. Blei

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A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI (2017)

Justin Domke

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Concentration of tempered posteriors and of their variational approximations (2017)

Pierre Alquier and James Ridgway

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Boosting Variational Inference: an Optimization Perspective (2017)

Francesco Locatello et al.

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Perturbative Black Box Variational Inference (2017)

Robert Bamler et al.

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$\alpha$-Variational Inference with Statistical Guarantees (2017)

Yun Yang and Debdeep Pati and Anirban Bhattacharya

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Convergence Rates of Variational Posterior Distributions (2017)

Fengshuo Zhang and Chao Gao

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Variational Inference over Non-differentiable Cardiac Simulators using Bayesian Optimization (2017)

Adam McCarthy et al.

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On Statistical Optimality of Variational Bayes (2017)

Debdeep Pati et al.

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Gauged Mini-Bucket Elimination for Approximate Inference (2018)

Sungsoo Ahn et al.

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Alpha-Beta Divergence For Variational Inference (2018)

Jean-Baptiste Regli et al.

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Variance reduction properties of the reparameterization trick (2018)

Ming Xu et al.

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Using Large Ensembles of Control Variates for Variational Inference (2018)

Tomas Geffner et al.

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Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems (2018)

Arash Mehrjou et al.

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Non-Factorised Variational Inference in Dynamical Systems (2018)

Alessandro Davide Ialongo et al.

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Mixed Variational Inference (2019)

Nikolaos Gianniotis

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Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems (2019)

Yibo Yang et al.

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On the Limitations of Representing Functions on Sets (2019)

Edward Wagstaff et al.

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Multilevel Monte Carlo Variational Inference (2019)

Masahiro Fujisawa and Issei Sato

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Variational Bayesian Decision-making for Continuous Utilities (2019)

Tomasz Ku\'smierczyk et al.

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Copula-like Variational Inference (2019)

Marcel Hirt et al.

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A Latent Variational Framework for Stochastic Optimization (2019)

Philippe Casgrain

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Variational approximations using Fisher divergence (2019)

Yue Yang and Ryan Martin and Howard Bondell

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Streaming Variational Monte Carlo (2019)

Yuan Zhao et al.

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Amortized Inference of Variational Bounds for Learning Noisy-OR (2019)

Yiming Yan et al.

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Approximate Variational Inference Based on a Finite Sample of Gaussian Latent Variables (2019)

Nikolaos Gianniotis and Christoph Schn\"orr and Christian Molkenthin and Sanjay Singh Bora

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Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation (2019)

Justin Domke and Daniel Sheldon

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Convex hull algorithms based on some variational models (2019)

Lingfeng Li et al.

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Convergence Rates of Variational Inference in Sparse Deep Learning (2019)

Badr-Eddine Ch\'erief-Abdellatif

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Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs (2019)

Victor Gallego and David Rios Insua

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Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation (2019)

Konstantinos Pitas

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Tightening Bounds for Variational Inference by Revisiting Perturbation Theory (2019)

Robert Bamler et al.

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Streamlined Variational Inference for Linear Mixed Models with Crossed Random Effects (2019)

Marianne Menictas et al.

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Validated Variational Inference via Practical Posterior Error Bounds (2019)

Jonathan H. Huggins et al.

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The Renyi Gaussian Process: Towards Improved Generalization (2019)

Xubo Yue et al.

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Better Approximate Inference for Partial Likelihood Models with a Latent Structure (2019)

Amrith Setlur et al.

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Variational Predictive Information Bottleneck (2019)

Alexander A. Alemi

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Asymptotic Consistency of Loss-Calibrated Variational Bayes (2019)

Prateek Jaiswal et al.

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Stochastic Variational Inference via Upper Bound (2019)

Chunlin Ji and Haige Shen

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Exact asymptotics for phase retrieval and compressed sensing with random generative priors (2019)

Benjamin Aubin et al.

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Variational Bayesian Methods for Stochastically Constrained System Design Problems (2020)

Prateek Jaiswal et al.

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An Equivalence between Bayesian Priors and Penalties in Variational Inference (2020)

Pierre Wolinski et al.

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Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations (2020)

Sebastian Farquhar et al.

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Approximation Based Variance Reduction for Reparameterization Gradients (2020)

Tomas Geffner et al.

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Variational approximations of empirical Bayes posteriors in high-dimensional linear models (2020)

Yue Yang and Ryan Martin

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Awesome Statistics Theory

Key papers

Statistics Theory

Awesome Statistics Theory

Key papers