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Found 21 papers, 17 papers with code
Artificial Intelligence and Aesthetic Judgment
In this essay, we focus on an unresolved tension when we bring this dilemma to bear in the context of generative AI: are we looking for proof that generated media reflects something about the conditions that created it or some eternal human essence?
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
A recent alternative formulation instead treats federated learning as a distributed inference problem, where the goal is to infer a global posterior from partitioned client data (Al-Shedivat et al., 2021).
The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning
We conclude by discussing risks that arise when sources of errors are misdiagnosed and the need to acknowledge the role of human inductive biases in learning and reform.
Toward a Taxonomy of Trust for Probabilistic Machine Learning
Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond.
Pathfinder: Parallel quasi-Newton variational inference
Pathfinder returns draws from the approximation with the lowest estimated Kullback-Leibler (KL) divergence to the true posterior.
Bayesian hierarchical stacking: Some models are (somewhere) useful
We show that stacking is most effective when model predictive performance is heterogeneous in inputs, and we can further improve the stacked mixture with a hierarchical model.
What are the most important statistical ideas of the past 50 years?
We review the most important statistical ideas of the past half century, which we categorize as: counterfactual causal inference, bootstrapping and simulation-based inference, overparameterized models and regularization, Bayesian multilevel models, generic computation algorithms, adaptive decision analysis, robust inference, and exploratory data analysis.
Causal Inference
Methodology
Adaptive Path Sampling in Metastable Posterior Distributions
The normalizing constant plays an important role in Bayesian computation, and there is a large literature on methods for computing or approximating normalizing constants that cannot be evaluated in closed form.
Computation Methodology
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-based approximate inferences will understate posterior uncertainty.
Improving multilevel regression and poststratification with structured priors
A central theme in the field of survey statistics is estimating population-level quantities through data coming from potentially non-representative samples of the population.
Methodology
Rank-normalization, folding, and localization: An improved $\widehat{R}$ for assessing convergence of MCMC
In this paper we show that the convergence diagnostic $\widehat{R}$ of Gelman and Rubin (1992) has serious flaws.
Computation Methodology
Validating Bayesian Inference Algorithms with Simulation-Based Calibration
Verifying the correctness of Bayesian computation is challenging.
Methodology
Yes, but Did It Work?: Evaluating Variational Inference
While it's always possible to compute a variational approximation to a posterior distribution, it can be difficult to discover problems with this approximation.
Visualization in Bayesian workflow
Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains.
Methodology Applications
Using stacking to average Bayesian predictive distributions
The widely recommended procedure of Bayesian model averaging is flawed in the M-open setting in which the true data-generating process is not one of the candidate models being fit.
Methodology Computation
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values.
Computation Methodology
Pareto Smoothed Importance Sampling
Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the importance ratios have a heavy right tail.
Automatic Variational Inference in Stan
With ADVI we can use variational inference on any model we write in Stan.
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to obtain a global posterior approximation.
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by first-order gradient information.
