Acyclic directed mixed graph - An acyclic directed mixed graph, or ADMG, contains directed edges such as X → Y and bidirected edges such as X ↔ Y, with no cycle made entirely of...
Adaptive Markov chain Monte Carlo - Adaptive Markov chain Monte Carlo, or adaptive MCMC, changes aspects of a sampling algorithm using information gathered during its run.
Adaptive rejection sampling - Adaptive rejection sampling, or ARS, draws from a target distribution by constructing and refining an upper envelope used for rejection sampling.
Adaptive resampling - Adaptive resampling, in sequential Monte Carlo and particle filtering, means deciding whether to resample a population of weighted particles using its...
Advantage actor-critic - Advantage actor-critic is a reinforcement-learning approach in which an actor learns a policy and a critic helps estimate whether an action performed better...
Alpha divergence - Alpha divergence is a parameterized family of measures for comparing probability distributions. Changing alpha changes how discrepancies are penalized.
Amortized variational inference - Amortized variational inference learns a shared function that maps observations or other relevant context to parameters of approximate posterior distributions.
Annealed importance sampling - Annealed importance sampling, or AIS, estimates quantities associated with a difficult probability distribution by moving through a sequence of intermediate...
ARD kernel - An ARD kernel is a covariance function with separate relevance-related parameters for different input dimensions.
Automatic differentiation variational inference - Automatic differentiation variational inference, or ADVI, is a method for automating approximate Bayesian inference in differentiable probabilistic models.
Autoregressive flow - An autoregressive flow is a normalizing flow whose invertible transformations use an ordered dependence structure across coordinates.
Auxiliary variable Markov chain Monte Carlo - Auxiliary variable Markov chain Monte Carlo is a family of sampling methods that introduces additional variables to help explore a probability distribution.
Barber-Agakov lower bound - The Barber-Agakov lower bound is a variational lower bound on mutual information that replaces an often difficult conditional distribution with a tractable...
Barlow Twins loss - Barlow Twins loss is a self-supervised learning objective that encourages two augmented views of the same data to produce consistent representations while...
Batch active learning - Batch active learning is an approach in which a learning system selects a group of examples for labeling before receiving those labels and updating its model.
BatchBALD - BatchBALD is a Bayesian active-learning acquisition method that selects a batch of examples by estimating how much their labels jointly reveal about a...
Bayesian active learning by disagreement - Bayesian active learning by disagreement, usually abbreviated BALD, is an acquisition criterion that selects an example whose unknown label is expected to...
Bayesian hidden Markov model - A Bayesian hidden Markov model is a probabilistic sequence model that places prior distributions on uncertain model parameters and updates them using...
Bayesian lasso - The Bayesian lasso is a Bayesian regression approach that uses Laplace, or double-exponential, priors to shrink coefficients toward zero.
Bayesian logistic regression - Bayesian logistic regression models a binary outcome using a logistic probability function and prior distributions over regression coefficients.
Bayesian optimization acquisition function - A Bayesian optimization acquisition function scores possible evaluations using the current probabilistic model of an objective.
Bayesian t-test - A Bayesian t-test is a Bayesian approach to comparing population means in a setting traditionally analyzed with a t-test.
Bernoulli mixture model - A Bernoulli mixture model represents binary observations as coming from a mixture of hidden components.
Beta-binomial model - A beta-binomial model combines a binomial model for a count of successes with a beta distribution for the underlying success probability.
Bi-tempered loss - Bi-tempered loss is a generalization of logistic classification loss that uses two temperature parameters: one changes how prediction errors are penalized...
Binomial regression - Binomial regression models how explanatory variables relate to the probability of a two-outcome event, using a binomial distribution for the observed number...
Black-box variational inference - Black-box variational inference is an approach to approximate Bayesian inference that estimates optimization gradients through sampling, reducing the need...
Blind inverse problem - A blind inverse problem seeks to recover an underlying signal or object when the process that produced the measurements is also partly or wholly unknown.
Blocked Gibbs sampling - Blocked Gibbs sampling is a Markov chain Monte Carlo method that updates a group of variables together from their joint conditional distribution, given the...
Boltzmann exploration - Boltzmann exploration is an action-selection strategy that samples actions with probabilities determined by a softmax of their estimated values.
Centered kernel alignment - Centered kernel alignment, or CKA, measures similarity between two sets of representations by comparing their centered similarity structures over the same...
Chain graphical model - A chain graphical model represents a probability distribution using a graph that can contain both directed and undirected edges, subject to restrictions...
Class incremental learning - Class incremental learning is a continual-learning setting in which a model learns additional classes over time while remaining able to distinguish the...
Classification with rejection - Classification with rejection allows a classifier to abstain from assigning a class to selected inputs.
Classifier-free guidance - Classifier-free guidance is a diffusion-generation technique that combines conditional and unconditional model predictions to strengthen the influence of a...
Classifier guidance - Classifier guidance is a technique for steering diffusion-model generation using gradients from a separate classifier.
Cluster assumption - The cluster assumption is a semi-supervised learning assumption that examples within the same meaningful high-density cluster tend to share a class.
Collapsed Gibbs sampling - Collapsed Gibbs sampling is a Markov chain Monte Carlo method that integrates some unknown variables out of a probability model, then repeatedly samples the...
Common random numbers - Common random numbers is a simulation technique that deliberately shares random inputs between alternatives being compared.
Compact-support kernel - A compact-support kernel is a kernel that is exactly zero outside a bounded neighborhood.
Compactly supported kernel - A compactly supported kernel is a kernel whose nonzero values are confined to a bounded neighborhood.
Conditional diffusion model - A conditional diffusion model is a generative model that uses a diffusion-style noise-removal process to produce outputs in response to supplied information.
Conformalized quantile regression - Conformalized quantile regression, or CQR, combines estimated lower and upper conditional quantiles with a conformal calibration step to produce prediction...
Consistent estimator - A consistent estimator is an estimation rule whose output converges in probability to the quantity being estimated as the sample size grows, under stated...
Continuous-time normalizing flow - A continuous-time normalizing flow is a generative density model that transforms a simple probability distribution through an ordinary differential equation.