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Found 51 papers, 17 papers with code
AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting
This study aims to tackle these critical limitations by introducing adapters; feature-space transformations that facilitate the effective use of pre-trained univariate time series FMs for multivariate tasks.
Multivariate Time Series Forecasting
Representation Learning
+3
Zero-shot Model-based Reinforcement Learning using Large Language Models
The emerging zero-shot capabilities of Large Language Models (LLMs) have led to their applications in areas extending well beyond natural language processing tasks.
Robust Classification by Coupling Data Mollification with Label Smoothing
Introducing training-time augmentations is a key technique to enhance generalization and prepare deep neural networks against test-time corruptions.
A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning
In model-based reinforcement learning, most algorithms rely on simulating trajectories from one-step models of the dynamics learned on data.
Variational DAG Estimation via State Augmentation With Stochastic Permutations
Estimating the structure of a Bayesian network, in the form of a directed acyclic graph (DAG), from observational data is a statistically and computationally hard problem with essential applications in areas such as causal discovery.
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets.
Spatial Bayesian Neural Networks
We propose several variants of SBNNs, most of which are able to match the finite-dimensional distribution of the target process at the selected grid better than conventional BNNs of similar complexity.
Multi-timestep models for Model-based Reinforcement Learning
In model-based reinforcement learning (MBRL), most algorithms rely on simulating trajectories from one-step dynamics models learned on data.
One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models
Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impressive realistic-looking images.
When is Importance Weighting Correction Needed for Covariate Shift Adaptation?
Classic results show that the IW correction is needed when the model is parametric and misspecified.
Continuous-Time Functional Diffusion Processes
We introduce Functional Diffusion Processes (FDPs), which generalize score-based diffusion models to infinite-dimensional function spaces.
Ranked #26 on
Image Generation
on CelebA 64x64
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Autoencoders and their variants are among the most widely used models in representation learning and generative modeling.
Locally Smoothed Gaussian Process Regression
We develop a novel framework to accelerate Gaussian process regression (GPR).
How Much is Enough? A Study on Diffusion Times in Score-based Generative Models
Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data.
Local Random Feature Approximations of the Gaussian Kernel
A fundamental drawback of kernel-based statistical models is their limited scalability to large data sets, which requires resorting to approximations.
Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel
Commonly used approaches avoid computing the high-dimensional tensor product explicitly, resulting in a suboptimal dependence of $\mathcal{O}(3^p)$ in the embedding dimension.
Improved Random Features for Dot Product Kernels
These variance formulas elucidate conditions under which certain approximations (e. g., TensorSRHT) achieve lower variances than others (e. g., Rademacher sketches), and conditions under which the use of complex features leads to lower variances than real features.
Revisiting the Effects of Stochasticity for Hamiltonian Samplers
We revisit the theoretical properties of Hamiltonian stochastic differential equations (SDES) for Bayesian posterior sampling, and we study the two types of errors that arise from numerical SDE simulation: the discretization error and the error due to noisy gradient estimates in the context of data subsampling.
Model Selection for Bayesian Autoencoders
We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization.
All You Need is a Good Functional Prior for Bayesian Deep Learning
This poses a challenge because modern neural networks are characterized by a large number of parameters, and the choice of these priors has an uncontrolled effect on the induced functional prior, which is the distribution of the functions obtained by sampling the parameters from their prior distribution.
Parametric Bootstrap Ensembles as Variational Inference
In this paper, we employ variational arguments to establish a connection between ensemble methods for Neural Networks and Bayesian inference.
Functional Priors for Bayesian Neural Networks through Wasserstein Distance Minimization to Gaussian Processes
The Bayesian treatment of neural networks dictates that a prior distribution is considered over the weight and bias parameters of the network.
Sparse within Sparse Gaussian Processes using Neighbor Information
In this work, we address one limitation of sparse GPs, which is due to the challenge in dealing with a large number of inducing variables without imposing a special structure on the inducing inputs.
An Identifiable Double VAE For Disentangled Representations
A large part of the literature on learning disentangled representations focuses on variational autoencoders (VAE).
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling
In this work we define a unified mathematical framework to deepen our understanding of the role of stochastic gradient (SG) noise on the behavior of Markov chain Monte Carlo sampling (SGMCMC) algorithms.
A Variational View on Bootstrap Ensembles as Bayesian Inference
In this paper, we employ variational arguments to establish a connection between ensemble methods for Neural Networks and Bayesian inference.
Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting
Accurate travel products price forecasting is a highly desired feature that allows customers to take informed decisions about purchases, and companies to build and offer attractive tour packages.
Applications
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
Variational inference techniques based on inducing variables provide an elegant framework for scalable posterior estimation in Gaussian process (GP) models.
Efficient Approximate Inference with Walsh-Hadamard Variational Inference
Variational inference offers scalable and flexible tools to tackle intractable Bayesian inference of modern statistical models like Bayesian neural networks and Gaussian processes.
LIBRE: Learning Interpretable Boolean Rule Ensembles
We present a novel method - LIBRE - to learn an interpretable classifier, which materializes as a set of Boolean rules.
Kernel computations from large-scale random features obtained by Optical Processing Units
Approximating kernel functions with random features (RFs)has been a successful application of random projections for nonparametric estimation.
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD
Large scale machine learning is increasingly relying on distributed optimization, whereby several machines contribute to the training process of a statistical model.
Deep Compositional Spatial Models
Spatial processes with nonstationary and anisotropic covariance structure are often used when modelling, analysing and predicting complex environmental phenomena.
Walsh-Hadamard Variational Inference for Bayesian Deep Learning
Over-parameterized models, such as DeepNets and ConvNets, form a class of models that are routinely adopted in a wide variety of applications, and for which Bayesian inference is desirable but extremely challenging.
A comparative evaluation of novelty detection algorithms for discrete sequences
The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection.
Variational Calibration of Computer Models
Bayesian calibration of black-box computer models offers an established framework to obtain a posterior distribution over model parameters.
Good Initializations of Variational Bayes for Deep Models
Stochastic variational inference is an established way to carry out approximate Bayesian inference for deep models.
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification.
Calibrating Deep Convolutional Gaussian Processes
The wide adoption of Convolutional Neural Networks (CNNs) in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the ability of these models to accurately quantify the uncertainty in their predictions.
Constraining the Dynamics of Deep Probabilistic Models
We introduce a novel generative formulation of deep probabilistic models implementing "soft" constraints on their function dynamics.
Pseudo-extended Markov chain Monte Carlo
In this paper, we introduce the pseudo-extended MCMC method as a simple approach for improving the mixing of the MCMC sampler for multi-modal posterior distributions.
Entropic Trace Estimates for Log Determinants
The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal point processes, Gaussian processes, generalised Markov random fields, graph models and many others.
Bayesian Inference of Log Determinants
The log-determinant of a kernel matrix appears in a variety of machine learning problems, ranging from determinantal point processes and generalized Markov random fields, through to the training of Gaussian processes.
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
We investigate the capabilities and limitations of Gaussian process models by jointly exploring three complementary directions: (i) scalable and statistically efficient inference; (ii) flexible kernels; and (iii) objective functions for hyperparameter learning alternative to the marginal likelihood.
Random Feature Expansions for Deep Gaussian Processes
The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty.
Mini-Batch Spectral Clustering
The cost of computing the spectrum of Laplacian matrices hinders the application of spectral clustering to large data sets.
MCMC for Variationally Sparse Gaussian Processes
This paper simultaneously addresses these, using a variational approximation to the posterior which is sparse in support of the function but otherwise free-form.
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)
In applications of Gaussian processes where quantification of uncertainty is of primary interest, it is necessary to accurately characterize the posterior distribution over covariance parameters.
Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC
The results empirically demonstrate that compared to importance sampling, annealed importance sampling can reduce the variance of the estimate of the marginal likelihood exponentially in the number of data at a computational cost that scales only polynomially.
Pseudo-Marginal Bayesian Inference for Gaussian Processes
The main challenges that arise when adopting Gaussian Process priors in probabilistic modeling are how to carry out exact Bayesian inference and how to account for uncertainty on model parameters when making model-based predictions on out-of-sample data.
