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Found 17 papers, 9 papers with code
Scalable Bayesian Transformed Gaussian Processes
The Bayesian transformed Gaussian process (BTG) model, proposed by Kedem and Oliviera, is a fully Bayesian counterpart to the warped Gaussian process (WGP) and marginalizes out a joint prior over input warping and kernel hyperparameters.
On-the-Fly Rectification for Robust Large-Vocabulary Topic Inference
Across many data domains, co-occurrence statistics about the joint appearance of objects are powerfully informative.
Surveillance Evasion Through Bayesian Reinforcement Learning
We consider a task of surveillance-evading path-planning in a continuous setting.
Scaling Gaussian Processes with Derivative Information Using Variational Inference
We demonstrate the full scalability of our approach on a variety of tasks, ranging from a high dimensional stellarator fusion regression task to training graph convolutional neural networks on Pubmed using Bayesian optimization.
Density of States Graph Kernels
A fundamental problem on graph-structured data is that of quantifying similarity between graphs.
On the Distribution of Minima in Intrinsic-Metric Rotation Averaging
This is an extension of the results of [24], which used local convexity as a proxy to study the difficulty of problem.
Efficient Rollout Strategies for Bayesian Optimization
Non-myopic acquisition functions consider the impact of the next $h$ function evaluations and are typically computed through rollout, in which $h$ steps of BO are simulated.
Practical Correlated Topic Modeling and Analysis via the Rectified Anchor Word Algorithm
Despite great scalability on large data and their ability to understand correlations between topics, spectral topic models have not been widely used due to the absence of reliability in real data and lack of practical implementations.
pySOT and POAP: An event-driven asynchronous framework for surrogate optimization
This paper describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT).
Network Density of States
Much of spectral graph theory descends directly from spectral geometry, the study of differentiable manifolds through the spectra of associated differential operators.
Social and Information Networks Numerical Analysis
Scaling Gaussian Process Regression with Derivatives
Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction.
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware.
Krylov Subspace Approximation for Local Community Detection
Community detection is an important information mining task in many fields including computer science, social sciences, biology and physics.
Social and Information Networks
Prior-aware Dual Decomposition: Document-specific Topic Inference for Spectral Topic Models
Spectral topic modeling algorithms operate on matrices/tensors of word co-occurrence statistics to learn topic-specific word distributions.
Scalable Log Determinants for Gaussian Process Kernel Learning
For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compute a log determinant of an $n \times n$ positive definite matrix, and its derivatives - leading to prohibitive $\mathcal{O}(n^3)$ computations.
Robust Spectral Inference for Joint Stochastic Matrix Factorization
Spectral inference provides fast algorithms and provable optimality for latent topic analysis.
Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach
Nowadays, as we often explore networks with billions of vertices and find communities of size hundreds, it is crucial to shift our attention from macroscopic structure to microscopic structure when dealing with large networks.
Social and Information Networks Data Structures and Algorithms Physics and Society G.2.2; H.3.3
