A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Improves hyperparameter learning in GP models with non-conjugate likelihoods.
PriorCVAE uses deep generative models to infer hyperparameters in MCMC.
Combines VI and EP for better Gaussian process hyperparameter learning.
Learning in Gaussian Process models occurs through the adaptation of hyperparameters of the mean and the covariance function. The classical approach entails maximizing the marginal likelihood yielding fixed point estimates (an approach called \textit{Type II maximum likelihood} or ML-II). An alternative learning proced…
We introduce a Bayesian framework for inference with a supervised version of the Gaussian process latent variable model. The framework overcomes the high correlations between latent variables and hyperparameters by using an unbiased pseudo estimate for the marginal likelihood that approximately integrates over the late…
In likelihood-free settings where likelihood evaluations are intractable, approximate Bayesian computation (ABC) addresses the formidable inference task to discover plausible parameters of simulation programs that explain the observations. However, they demand large quantities of simulation calls. Critically, hyperpara…
New algorithm tunes SGMCMC hyperparameters for scalable Bayesian inference.
Conditional kernel mean embeddings are nonparametric models that encode conditional expectations in a reproducing kernel Hilbert space. While they provide a flexible and powerful framework for probabilistic inference, their performance is highly dependent on the choice of kernel and regularization hyperparameters. Neve…
Study assesses hyperparameter tuning for causal inference with DML.
Stochastic variational inference allows for fast posterior inference in complex Bayesian models. However, the algorithm is prone to local optima which can make the quality of the posterior approximation sensitive to the choice of hyperparameters and initialization. We address this problem by replacing the natural gradi…
MR estimator simplifies causal inference by combining models without hyperparameter tuning.
AutoPC optimizes hyperparameters for the PC algorithm to improve its performance.
Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings. All existing methods for learning under the assumption of structured sparsity rely on prior knowledge on how to weight (or how to penalize) individual subsets…
Deep learning models are full of hyperparameters, which are set manually before the learning process can start. To find the best configuration for these hyperparameters in such a high dimensional space, with time-consuming and expensive model training / validation, is not a trivial challenge. Bayesian optimization is a…
With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to a…
Machine learning algorithms are very sensitive to the hyperparameters, and their evaluations are generally expensive. Users desperately need intelligent methods to quickly optimize hyperparameter settings according to known evaluation information, and thus reduce computational cost and promote optimization efficiency. …
Statistical guarantees for hyperparameter selection
Improved Kalman filtering with hierarchical variational approach.
Out-of-distribution (OOD) detection approaches usually present special requirements (e.g., hyperparameter validation, collection of outlier data) and produce side effects (e.g., classification accuracy drop, slower energy-inefficient inferences). We argue that these issues are a consequence of the SoftMax loss anisotro…
A new RNN architecture reduces model size and improves performance.
The behavior of many Bayesian models used in machine learning critically depends on the choice of prior distributions, controlled by some hyperparameters that are typically selected by Bayesian optimization or cross-validation. This requires repeated, costly, posterior inference. We provide an alternative for selecting…
Efficiently optimizes hyperparameters for PDE and inverse problems using Gaussian processes.
VisEvol uses evolutionary optimization to find optimal hyperparameters for machine learning models.
Sparse Gaussian process hyperparameters optimized using MCMC.
BayesFlow 2 speeds up Bayesian inference for complex models.
FlowVAT improves variational inference for multi-modal distributions.
Proposes a method to optimize neural network initialization using marginal likelihood maximization.
New method upscales models and transfers hyperparameters efficiently.
BDC uses Distance Correlation for efficient Bayesian optimization of expensive functions.
The paper proposes a method to improve Bayesian inference for periodic data using data-driven priors.
Improves Bayesian inference for deep models to better approximate posterior distributions.
We present the particle stochastic approximation EM (PSAEM) algorithm for learning of dynamical systems. The method builds on the EM algorithm, an iterative procedure for maximum likelihood inference in latent variable models. By combining stochastic approximation EM and particle Gibbs with ancestor sampling (PGAS), PS…
New method for adaptive estimation and inference in econometric models without knowing smoothness.
Both feature selection and hyperparameter tuning are key tasks in machine learning. Hyperparameter tuning is often useful to increase model performance, while feature selection is undertaken to attain sparse models. Sparsity may yield better model interpretability and lower cost of data acquisition, data handling and m…
We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional features …
MuyGPs efficiently estimates GP hyperparameters using local cross-validation.
Conditional kernel mean embeddings form an attractive nonparametric framework for representing conditional means of functions, describing the observation processes for many complex models. However, the recovery of the original underlying function of interest whose conditional mean was observed is a challenging inferenc…
Improved Bayesian inference for neuronal ensemble inference reduces computational cost.
Rodent hippocampal population codes represent important spatial information about the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and pro…
Bayesian optimisation improves with fully-Bayesian treatment of hyperparameters.
New method improves uncertainty quantification in latent variable models.
The paper proposes a method to learn hyperparameters without validation sets, improving efficiency and accuracy.
Online Passive-Aggressive (PA) learning is a class of online margin-based algorithms suitable for a wide range of real-time prediction tasks, including classification and regression. PA algorithms are formulated in terms of deterministic point-estimation problems governed by a set of user-defined hyperparameters: the a…
Bayesian TNKMs automatically infer model complexity and feature relevance.
Develops fully Bayesian LVGP for better uncertainty quantification.
Unified Bayesian-AI framework improves epidemiological risk prediction and uncertainty quantification.
This paper introduces ASCAI, a novel adaptive sampling methodology that can learn how to effectively compress Deep Neural Networks (DNNs) for accelerated inference on resource-constrained platforms. Modern DNN compression techniques comprise various hyperparameters that require per-layer customization to ensure high ac…
A new method for kernel tests without data splitting increases power.