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Found 33 papers, 15 papers with code
The Behavior and Convergence of Local Bayesian Optimization
A recent development in Bayesian optimization is the use of local optimization strategies, which can deliver strong empirical performance on high-dimensional problems compared to traditional global strategies.
A Visual Active Search Framework for Geospatial Exploration
Many problems can be viewed as forms of geospatial search aided by aerial imagery, with examples ranging from detecting poaching activity to human trafficking.
Local Bayesian optimization via maximizing probability of descent
Local optimization presents a promising approach to expensive, high-dimensional black-box optimization by sidestepping the need to globally explore the search space.
A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias
Our paper seeks to fill in this missing gap by comparing and ranking eight user modeling algorithms based on their performance on a diverse set of four user study datasets.
Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery
Active search is a setting in adaptive experimental design where we aim to uncover members of rare, valuable class(es) subject to a budget constraint.
Nonmyopic Multifidelity Active Search
Active search is a learning paradigm where we seek to identify as many members of a rare, valuable class as possible given a labeling budget.
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
In this paper, we provide the first efficient implementation of general multi-step lookahead Bayesian optimization, formulated as a sequence of nested optimization problems within a multi-step scenario tree.
GPIRT: A Gaussian Process Model for Item Response Theory
This allows us to simultaneously relax assumptions about the shape of the IRFs while preserving the ability to estimate latent traits.
Automated Measurement of Quasar Redshift with a Gaussian Process
Our technique is an extension of an earlier Gaussian process method for detecting damped Lyman-alpha absorbers (DLAs) in quasar spectra with known redshifts.
Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Detecting Multiple DLAs per Spectrum in SDSS DR12 with Gaussian Processes
We present a revised version of our automated technique using Gaussian processes (GPs) to detect Damped Lyman-$\alpha$ absorbers (DLAs) along quasar (QSO) sightlines.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Data Analysis, Statistics and Probability
Cost Effective Active Search
We study a special paradigm of active learning, called cost effective active search, where the goal is to find a given number of positive points from a large unlabeled pool with minimum labeling cost.
BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design
Finite-horizon sequential experimental design (SED) arises naturally in many contexts, including hyperparameter tuning in machine learning among more traditional settings.
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Graph structured data are abundant in the real world.
Automated Model Selection with Bayesian Quadrature
We present a novel technique for tailoring Bayesian quadrature (BQ) to model selection.
Propagation kernels: efficient graph kernels from propagated information
We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data.
Ranked #8 on
Graph Classification
on NCI109
Efficient nonmyopic batch active search
A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery.
Automating Bayesian optimization with Bayesian optimization
Bayesian optimization is a powerful tool for global optimization of expensive functions.
Efficient nonmyopic active search with applications in drug and materials discovery
We also study the batch setting for the first time, where a batch of $b>1$ points can be queried at each iteration.
Improving Quadrature for Constrained Integrands
We focus on quadrature with nonnegative functions, a common task in Bayesian inference.
Efficient Nonmyopic Active Search
Active search is an active learning setting with the goal of identifying as many members of a given class as possible under a labeling budget.
Active Search for Sparse Signals with Region Sensing
Autonomous systems can be used to search for sparse signals in a large space; e. g., aerial robots can be deployed to localize threats, detect gas leaks, or respond to distress calls.
Bayesian optimization for automated model selection
Despite the success of kernel-based nonparametric methods, kernel selection still requires considerable expertise, and is often described as a “black art.” We present a sophisticated method for automatically searching for an appropriate kernel from an infinite space of potential choices.
Exact Sampling from Determinantal Point Processes
Determinantal point processes (DPPs) are an important concept in random matrix theory and combinatorics.
Detecting Damped Lyman-$α$ Absorbers with Gaussian Processes
We develop an automated technique for detecting damped Lyman-$\alpha$ absorbers (DLAs) along spectroscopic lines of sight to quasi-stellar objects (QSOs or quasars).
Cosmology and Nongalactic Astrophysics Data Analysis, Statistics and Probability
Bayesian Active Model Selection with an Application to Automated Audiometry
Using this and a previously published model for healthy responses, the proposed method is shown to be capable of diagnosing the presence or absence of NIHL with drastically fewer samples than existing approaches.
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes
This paper proposes a novel Gaussian process approach to fault removal in time-series data.
Differentially Private Bayesian Optimization
The success of machine learning has led practitioners in diverse real-world settings to learn classifiers for practical problems.
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
We propose a novel sampling framework for inference in probabilistic models: an active learning approach that converges more quickly (in wall-clock time) than Markov chain Monte Carlo (MCMC) benchmarks.
Propagation Kernels
We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data.
Σ-Optimality for Active Learning on Gaussian Random Fields
For active learning on GRFs, the commonly used V-optimality criterion queries nodes that reduce the L2 (regression) loss.
Active Learning of Linear Embeddings for Gaussian Processes
We propose an active learning method for discovering low-dimensional structure in high-dimensional Gaussian process (GP) tasks.
Active Learning of Model Evidence Using Bayesian Quadrature
Numerical integration is an key component of many problems in scientific computing, statistical modelling, and machine learning.
Bayesian Optimal Active Search and Surveying
In the second, active surveying, our goal is to actively query points to ultimately predict the proportion of a given class.
