Large-scale empirical tuning and comparison of default optimizers for variational inference | Paper Citation Record

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This paper cites PosteriorDB.jl: A Julia package to work with posteriordb.

Large-scale empirical tuning and comparison of default optimizers for variational inference PosteriorDB.jl: A Julia package to work with posteriordb

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Large-scale empirical tuning and comparison of default optimizers for variational inference Layer Normalization

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Large-scale empirical tuning and comparison of default optimizers for variational inference Online learning rate adaptation with hypergradient descent

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Large-scale empirical tuning and comparison of default optimizers for variational inference Training neural networks for and by interpolation

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Large-scale empirical tuning and comparison of default optimizers for variational inference Julia: A fresh approach to numerical computing

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This paper cites Variational inference: A review for statisticians.

Large-scale empirical tuning and comparison of default optimizers for variational inference Variational inference: A review for statisticians

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Large-scale empirical tuning and comparison of default optimizers for variational inference On-line learning and stochastic approximations

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Large-scale empirical tuning and comparison of default optimizers for variational inference Optimization methods for large-scale machine learning

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This paper cites Quasi-Monte Carlo variational inference.

Large-scale empirical tuning and comparison of default optimizers for variational inference Quasi-Monte Carlo variational inference

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This paper cites Importance weighted autoencoders.

Large-scale empirical tuning and comparison of default optimizers for variational inference Importance weighted autoencoders

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This paper cites Sample average approximation for black-box variational inference.

Large-scale empirical tuning and comparison of default optimizers for variational inference Sample average approximation for black-box variational inference

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This paper cites EigenVI: Score-based variational inference with orthogonal function expansions.

Large-scale empirical tuning and comparison of default optimizers for variational inference EigenVI: Score-based variational inference with orthogonal function expansions

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This paper cites Batch and Match: Black-box variational inference with a score-based T. Campbell, J. H. Huggins, K. Kim, C. C. Margossian29 divergence.

Large-scale empirical tuning and comparison of default optimizers for variational inference Batch and Match: Black-box variational inference with a score-based T. Campbell, J. H. Huggins, K. Kim, C. C. Margossian29 divergence

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Large-scale empirical tuning and comparison of default optimizers for variational inference Making SGD parameter-free

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This paper cites Algorithms for computing the sample variance: Analysis and recommendations.

Large-scale empirical tuning and comparison of default optimizers for variational inference Algorithms for computing the sample variance: Analysis and recommendations

Reference 15

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Large-scale empirical tuning and comparison of default optimizers for variational inference Symbolic discovery of optimization algorithms

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Large-scale empirical tuning and comparison of default optimizers for variational inference Mechanic: A learning rate tuner

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Large-scale empirical tuning and comparison of default optimizers for variational inference The Helmholtz Machine

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This paper cites Big Batch SGD: Auto- mated inference using adaptive batch sizes.

Large-scale empirical tuning and comparison of default optimizers for variational inference Big Batch SGD: Auto- mated inference using adaptive batch sizes

Reference 19

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This paper cites SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives.

Large-scale empirical tuning and comparison of default optimizers for variational inference SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives

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This paper cites Learning-rate-free learning by D- adaptation.

Large-scale empirical tuning and comparison of default optimizers for variational inference Learning-rate-free learning by D- adaptation

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This paper cites Robust, accurate stochastic optimization for variational inference.

Large-scale empirical tuning and comparison of default optimizers for variational inference Robust, accurate stochastic optimization for variational inference

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This paper cites Challenges and opportunities in high-dimensional variational inference.

Large-scale empirical tuning and comparison of default optimizers for variational inference Challenges and opportunities in high-dimensional variational inference

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This paper cites Forward- backward Gaussian variational inference via JKO in the Bures-Wasserstein space.

Large-scale empirical tuning and comparison of default optimizers for variational inference Forward- backward Gaussian variational inference via JKO in the Bures-Wasserstein space

Reference 24

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This paper cites Variational 30Default optimizers for variational inference inference viaχ-upper bound minimization.

Large-scale empirical tuning and comparison of default optimizers for variational inference Variational 30Default optimizers for variational inference inference viaχ-upper bound minimization

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Large-scale empirical tuning and comparison of default optimizers for variational inference bridging the gap between constant step size stochastic gradient descent and Markov chains

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Large-scale empirical tuning and comparison of default optimizers for variational inference Provable gradient variance guarantees for black-box variational inference

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Large-scale empirical tuning and comparison of default optimizers for variational inference Provable smoothness guarantees for black-box variational inference

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Large-scale empirical tuning and comparison of default optimizers for variational inference Provable convergence guarantees for black-box variational inference

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Large-scale empirical tuning and comparison of default optimizers for variational inference Importance weighting and variational inference

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Large-scale empirical tuning and comparison of default optimizers for variational inference Divide and Couple: using Monte Carlo variational objectives for posterior approximation

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This paper cites Adaptive subgradient methods for online learning and stochastic optimization.

Large-scale empirical tuning and comparison of default optimizers for variational inference Adaptive subgradient methods for online learning and stochastic optimization

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Large-scale empirical tuning and comparison of default optimizers for variational inference Batch means and spectral variance estimators in Markov chain Monte Carlo

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Large-scale empirical tuning and comparison of default optimizers for variational inference Multilevel Monte Carlo variational inference

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Large-scale empirical tuning and comparison of default optimizers for variational inference Don’t be so monotone: Relax- ing stochastic line search in over-parametrized models

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Large-scale empirical tuning and comparison of default optimizers for variational inference Empirical evaluation of biased methods for alpha divergence minimization

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Large-scale empirical tuning and comparison of default optimizers for variational inference MCMC variational inference via uncorrected Hamiltonian annealing

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Large-scale empirical tuning and comparison of default optimizers for variational inference On the difficulty of unbiased alpha divergence minimization

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Large-scale empirical tuning and comparison of default optimizers for variational inference Inference from iterative simulation using multiple sequences

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Large-scale empirical tuning and comparison of default optimizers for variational inference Black box variational inference with a deterministic objective: Faster, more accurate, and even more black box

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Large-scale empirical tuning and comparison of default optimizers for variational inference Practical variational inference for neural networks

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Large-scale empirical tuning and comparison of default optimizers for variational inference Shampoo: Preconditioned stochastic tensor optimization

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Large-scale empirical tuning and comparison of default optimizers for variational inference Revisiting the Polyak step size

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Large-scale empirical tuning and comparison of default optimizers for variational inference Deep residual learning for image recognition

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Large-scale empirical tuning and comparison of default optimizers for variational inference Black-Box alpha divergence minimization

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Large-scale empirical tuning and comparison of default optimizers for variational inference Keeping the neural networks simple by minimizing the description length of the weights

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Large-scale empirical tuning and comparison of default optimizers for variational inference Perturbation analysis and optimization of queueing networks

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Large-scale empirical tuning and comparison of default optimizers for variational inference Validated variational inference via practical posterior error bounds

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Large-scale empirical tuning and comparison of default optimizers for variational inference Variational Inference using Implicit Distributions

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Large-scale empirical tuning and comparison of default optimizers for variational inference DoG is SGD’s best friend: A parameter-free dynamic step size schedule

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Large-scale empirical tuning and comparison of default optimizers for variational inference Accelerating stochastic gradient descent using predictive variance reduction

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Large-scale empirical tuning and comparison of default optimizers for variational inference Muon: An optimizer for hidden layers in neural networks

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Large-scale empirical tuning and comparison of default optimizers for variational inference An introduction to variational methods for graphical models

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Large-scale empirical tuning and comparison of default optimizers for variational inference DoWG unleashed: An effi- cient universal parameter-free gradient descent method

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Large-scale empirical tuning and comparison of default optimizers for variational inference The Bayesian learning rule

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Large-scale empirical tuning and comparison of default optimizers for variational inference Linear convergence of black- box variational inference: Should we stick the landing?

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Large-scale empirical tuning and comparison of default optimizers for variational inference On the convergence of black-box variational inference

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Large-scale empirical tuning and comparison of default optimizers for variational inference A guide to sample average approximation

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This paper cites Adam: A method for stochastic optimization.

Large-scale empirical tuning and comparison of default optimizers for variational inference Adam: A method for stochastic optimization

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Large-scale empirical tuning and comparison of default optimizers for variational inference Auto-encoding variational Bayes

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Large-scale empirical tuning and comparison of default optimizers for variational inference Automatic differentiation variational inference

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Large-scale empirical tuning and comparison of default optimizers for variational inference Optimization guarantees for square-root natural-gradient variational inference

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Large-scale empirical tuning and comparison of default optimizers for variational inference Varia- tional inference via Wasserstein gradient flows

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Large-scale empirical tuning and comparison of default optimizers for variational inference A stochastic gradient method with an exponential convergence rate for finite training sets

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Large-scale empirical tuning and comparison of default optimizers for variational inference Rényi divergence variational inference

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Large-scale empirical tuning and comparison of default optimizers for variational inference Fast and simple natural-gradient variational inference with mixture of exponential-family approximations

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Large-scale empirical tuning and comparison of default optimizers for variational inference Batch size selection for variance estimators in MCMC

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Large-scale empirical tuning and comparison of default optimizers for variational inference Stochastic Polyak step-size for SGD: An adaptive learning rate for fast convergence

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Large-scale empirical tuning and comparison of default optimizers for variational inference The power of interpolation: Un- derstanding the effectiveness of SGD in modern over-parametrized learning

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Large-scale empirical tuning and comparison of default optimizers for variational inference posteriordb: Testing, benchmarking and developing Bayesian inference algorithms

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Large-scale empirical tuning and comparison of default optimizers for variational inference Torsten: A platform for Bayesian inference of pharmacometric models

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Large-scale empirical tuning and comparison of default optimizers for variational inference Expectation propagation for approximate Bayesian inference

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Large-scale empirical tuning and comparison of default optimizers for variational inference Variational Inference with Gaussian Score Matching

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Large-scale empirical tuning and comparison of default optimizers for variational inference Monte Carlo Gradient Estimation in Machine Learning

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Large-scale empirical tuning and comparison of default optimizers for variational inference Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients

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Large-scale empirical tuning and comparison of default optimizers for variational inference Rectified linear units improve restricted Boltz- mann machines

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Large-scale empirical tuning and comparison of default optimizers for variational inference Gaussian Variational Approximation with a Factor Covariance Structure

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Large-scale empirical tuning and comparison of default optimizers for variational inference Parameter-free Stochastic Optimization of Variationally Coherent Functions

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Large-scale empirical tuning and comparison of default optimizers for variational inference Training deep networks without learning 34Default optimizers for variational inference rates through coin betting

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Large-scale empirical tuning and comparison of default optimizers for variational inference R package

Reference 80

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Large-scale empirical tuning and comparison of default optimizers for variational inference Explorations of the mean field theory learning algorithm

Reference 81

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Large-scale empirical tuning and comparison of default optimizers for variational inference On the determination of the step size in stochastic quasigradient methods

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Large-scale empirical tuning and comparison of default optimizers for variational inference Acceleration of stochastic approximation by averaging

Reference 83

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Large-scale empirical tuning and comparison of default optimizers for variational inference Blackboxvariationalinference

Reference 84

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Large-scale empirical tuning and comparison of default optimizers for variational inference Hierarchical variational models

Reference 85

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Large-scale empirical tuning and comparison of default optimizers for variational inference On the convergence of Adam and beyond

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Large-scale empirical tuning and comparison of default optimizers for variational inference Variational Inference with Normalizing Flows

Reference 87

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Large-scale empirical tuning and comparison of default optimizers for variational inference Stochastic Backpropaga- tion and Approximate Inference in Deep Generative Models

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Large-scale empirical tuning and comparison of default optimizers for variational inference A stochastic approximation method

Reference 89

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Reference 90

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Large-scale empirical tuning and comparison of default optimizers for variational inference Sticking the landing: Simple, lower-variance gradient estimators for variational inference

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Large-scale empirical tuning and comparison of default optimizers for variational inference BridgeStan: Efficient in-memory access to the methods of a Stan model

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Large-scale empirical tuning and comparison of default optimizers for variational inference Sensitivity analysis of discrete event systems by the “push out

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Large-scale empirical tuning and comparison of default optimizers for variational inference Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

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Large-scale empirical tuning and comparison of default optimizers for variational inference Mean field theory for sigmoid belief networks

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Large-scale empirical tuning and comparison of default optimizers for variational inference Descending through a crowded valley—Benchmarking deep learning optimizers

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Large-scale empirical tuning and comparison of default optimizers for variational inference Stan Reference Manual, v2.38

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Large-scale empirical tuning and comparison of default optimizers for variational inference Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation

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Large-scale empirical tuning and comparison of default optimizers for variational inference Gaussian variational approximation with sparse precision matrices

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Large-scale empirical tuning and comparison of default optimizers for variational inference Doubly stochastic variational Bayes for non-conjugate inference

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