16 citations · 33 across the 4 of their papers we have counts for
5 papers
Amazon SageMaker Autopilot: a white box AutoML solution at scale
Piali Das, Valerio Perrone, Nikita Ivkin +22
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of…
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization
Valerio Perrone, Huibin Shen, Aida Zolic +12
Tuning complex machine learning systems is challenging. Machine learning typically requires to set hyperparameters, be it regularization, architecture, or optimization parameters,…
Constrained Bayesian Optimization with Max-Value Entropy Search
Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton +2
Bayesian optimization (BO) is a model-based approach to sequentially optimize expensive black-box functions, such as the validation error of a deep neural network with respect to i…
Convexification of Learning from Constraints
Iaroslav Shcherbatyi, Bjoern Andres
Regularized empirical risk minimization with constrained labels (in contrast to fixed labels) is a remarkably general abstraction of learning. For common loss and regularization fu…
GazeDPM: Early Integration of Gaze Information in Deformable Part Models
Iaroslav Shcherbatyi, Andreas Bulling, Mario Fritz
An increasing number of works explore collaborative human-computer systems in which human gaze is used to enhance computer vision systems. For object detection these efforts were s…