Search Results for author:
Found 7 papers, 2 papers with code
Pseudo-Bayesian Optimization
Its key idea is to use a surrogate model to approximate the objective and, importantly, quantify the associated uncertainty that allows a sequential search of query points that balance exploitation-exploration.
Hybrid Random Features
We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel estimation to provide most accurate approximation in the defined regions of interest.
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
In this paper we provide, to the best of our knowledge, the first comprehensive approach for incorporating various masking mechanisms into Transformers architectures in a scalable way.
Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set
Stochastic simulation aims to compute output performance for complex models that lack analytical tractability.
Learning Prediction Intervals for Regression: Generalization and Calibration
We study the generation of prediction intervals in regression for uncertainty quantification.
Interpret-able feedback for AutoML systems
Automated machine learning (AutoML) systems aim to enable training machine learning (ML) models for non-ML experts.
Demystifying Orthogonal Monte Carlo and Beyond
Orthogonal Monte Carlo (OMC) is a very effective sampling algorithm imposing structural geometric conditions (orthogonality) on samples for variance reduction.
