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

Minimax Theory of Likelihood-Based Deep Learning for Speckle Regression (2026)

Soham Jana

4.39

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

Yihang Gao et al.

4.39

Missing Data Imputation under Manifold Hypothesis (2026)

Zelong Bi et al.

2.00

Overdispersed Black-Box Variational Inference (2016)

Francisco J. R. Ruiz et al.

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

Christophe Dupuy (SIERRA) et al.

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Gaussian variational approximation with sparse precision matrices (2016)

Linda S. L. Tan and David J. Nott

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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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Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages (2016)

Yin Cheng Ng et al.

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Learning Boltzmann Machine with EM-like Method (2016)

Jinmeng Song et al.

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A Non-generative Framework and Convex Relaxations for Unsupervised Learning (2016)

Elad Hazan et al.

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The Generalized Reparameterization Gradient (2016)

Francisco J. R. Ruiz and Michalis K. Titsias and David M. Blei

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Truncated Variational Expectation Maximization (2016)

J\"org L\"ucke

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

Adji B. Dieng et al.

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Stochastic Variational Deep Kernel Learning (2016)

Andrew Gordon Wilson 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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Two Methods For Wild Variational Inference (2016)

Qiang Liu et al.

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Probabilistic Diffeomorphic Registration: Representing Uncertainty (2017)

Demian Wassermann (ATHENA) et al.

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Gaussian-Dirichlet Posterior Dominance in Sequential Learning (2017)

Ian Osband and Benjamin Van Roy

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A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings (2017)

Sami Remes et al.

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Sequential Local Learning for Latent Graphical Models (2017)

Sejun Park et al.

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Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference (2017)

Geoffrey Roeder et al.

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$k$-means as a variational EM approximation of Gaussian mixture models (2017)

J\"org L\"ucke et al.

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Importance Sampled Stochastic Optimization for Variational Inference (2017)

Joseph Sakaya and Arto Klami

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Unimodal probability distributions for deep ordinal classification (2017)

Christopher Beckham et al.

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Stochastic Gradient Monomial Gamma Sampler (2017)

Yizhe Zhang et al.

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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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A Contemporary Overview of Probabilistic Latent Variable Models (2017)

Rick Farouni

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

Pierre Alquier and James Ridgway

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Perturbative Black Box Variational Inference (2017)

Robert Bamler et al.

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Recency-weighted Markovian inference (2017)

Kristjan Kalm

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Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data (2017)

G. Revillon et al.

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Embedding Words as Distributions with a Bayesian Skip-gram Model (2017)

Arthur Bra\v{z}inskas et al.

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Convergence Rates of Variational Posterior Distributions (2017)

Fengshuo Zhang and Chao Gao

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On Statistical Optimality of Variational Bayes (2017)

Debdeep Pati et al.

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Black-box Variational Inference for Stochastic Differential Equations (2018)

Thomas Ryder et al.

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Variational Inference for Policy Gradient (2018)

Tianbing Xu

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Alpha-Beta Divergence For Variational Inference (2018)

Jean-Baptiste Regli et al.

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GumBolt: Extending Gumbel trick to Boltzmann priors (2018)

Amir H. Khoshaman and Mohammad H. Amin

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Semi-Implicit Variational Inference (2018)

Mingzhang Yin and Mingyuan Zhou

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Forward Amortized Inference for Likelihood-Free Variational Marginalization (2018)

Luca Ambrogioni et al.

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Adversarial confidence and smoothness regularizations for scalable unsupervised discriminative learning (2018)

Yi-Qing Wang

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Sequential sampling of Gaussian process latent variable models (2018)

Martin Tegner and Benjamin Bloem-Reddy and Stephen Roberts

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Stochastic Variational Optimization (2018)

Thomas Bird et al.

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Predictive Uncertainty through Quantization (2018)

Bastiaan S. Veeling et al.

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Wasserstein variational gradient descent: From semi-discrete optimal transport to ensemble variational inference (2018)

Luca Ambrogioni et al.

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

Arash Mehrjou et al.

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Mixed Likelihood Gaussian Process Latent Variable Model (2018)

Samuel Murray and Hedvig Kjellstr\"om

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Spread Divergence (2018)

Mingtian Zhang et al.

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Regularized Wasserstein Means for Aligning Distributional Data (2018)

Liang Mi et al.

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To Relieve Your Headache of Training an MRF, Take AdVIL (2019)

Chongxuan Li et al.

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Provable Smoothness Guarantees for Black-Box Variational Inference (2019)

Justin Domke

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On the Limitations of Representing Functions on Sets (2019)

Edward Wagstaff et al.

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Understanding MCMC Dynamics as Flows on the Wasserstein Space (2019)

Chang Liu et al.

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Multilevel Monte Carlo Variational Inference (2019)

Masahiro Fujisawa and Issei Sato

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Rates of Convergence for Sparse Variational Gaussian Process Regression (2019)

David R. Burt 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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Awesome Statistics Theory

Datasets & benchmarks

Key papers

Statistics Theory

Awesome Statistics Theory

Datasets & benchmarks

Key papers