221 citations · 572 across the 8 of their papers we have counts for
8 papers
Optimal control for sampling the transition path process and estimating rates
Jiaxin Yuan, Amar Shah, Channing Bentz +1
Many processes in nature such as conformal changes in biomolecules and clusters of interacting particles, genetic switches, mechanical or electromechanical oscillators with added n…
Recovery of Meteorites Using an Autonomous Drone and Machine Learning
Robert I. Citron, Peter Jenniskens, Christopher Watkins +5
The recovery of freshly fallen meteorites from tracked and triangulated meteors is critical to determining their source asteroid families. However, locating meteorite fragments in…
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions
Amar Shah, Zoubin Ghahramani
We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions. At each iteration, PPES aims to select…
Unitary Evolution Recurrent Neural Networks
Martin Arjovsky, Amar Shah, Yoshua Bengio
Recurrent neural networks (RNNs) are notoriously difficult to train. When the eigenvalues of the hidden to hidden weight matrix deviate from absolute value 1, optimization becomes…
Predictive Entropy Search for Multi-objective Bayesian Optimization
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah +1
We present PESMO, a Bayesian method for identifying the Pareto set of multi-objective optimization problems, when the functions are expensive to evaluate. The central idea of PESMO…
An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process
Amar Shah, David A. Knowles, Zoubin Ghahramani
Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large datasets. However, the performanc…
Student-t Processes as Alternatives to Gaussian Processes
Amar Shah, Andrew Gordon Wilson, Zoubin Ghahramani
We investigate the Student-t process as an alternative to the Gaussian process as a nonparametric prior over functions. We derive closed form expressions for the marginal likelihoo…
Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering
Amar Shah, Zoubin Ghahramani
Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often un…