
Efficient Rollout Strategies for Bayesian Optimization
Bayesian optimization (BO) is a class of sampleefficient global optimiz...
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On the Distribution of Minima in IntrinsicMetric Rotation Averaging
Rotation Averaging is a nonconvex optimization problem that determines ...
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Scaling Gaussian Process Regression with Derivatives
Gaussian processes (GPs) with derivatives are useful in many application...
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GPyTorch: Blackbox MatrixMatrix Gaussian Process Inference with GPU Acceleration
Despite advances in scalable models, the inference tools used for Gaussi...
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pySOT and POAP: An eventdriven asynchronous framework for surrogate optimization
This paper describes Plumbing for Optimization with Asynchronous Paralle...
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Scalable Log Determinants for Gaussian Process Kernel Learning
For applications as varied as Bayesian neural networks, determinantal po...
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Robust Spectral Inference for Joint Stochastic Matrix Factorization
Spectral inference provides fast algorithms and provable optimality for ...
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Prioraware Dual Decomposition: Documentspecific Topic Inference for Spectral Topic Models
Spectral topic modeling algorithms operate on matrices/tensors of word c...
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David Bindel
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