Search Results for author: Amar Shah

Found 10 papers, 2 papers with code

An Eager Satisfiability Modulo Theories Solver for Algebraic Datatypes

no code implementations18 Oct 2023 Amar Shah, Federico Mora, Sanjit A. Seshia

Specifically, our solver reduces ADT queries to a simpler logical theory, uninterpreted functions (UF), and then uses an existing solver on the reduced query.

Urban Driving with Conditional Imitation Learning

no code implementations30 Nov 2019 Jeffrey Hawke, Richard Shen, Corina Gurau, Siddharth Sharma, Daniele Reda, Nikolay Nikolov, Przemyslaw Mazur, Sean Micklethwaite, Nicolas Griffiths, Amar Shah, Alex Kendall

As our main contribution, we present an end-to-end conditional imitation learning approach, combining both lateral and longitudinal control on a real vehicle for following urban routes with simple traffic.

Autonomous Driving Imitation Learning +1

Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions

no code implementations NeurIPS 2015 Amar Shah, Zoubin Ghahramani

We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions.

Bayesian Optimization

Unitary Evolution Recurrent Neural Networks

2 code implementations20 Nov 2015 Martin Arjovsky, Amar Shah, Yoshua Bengio

When the eigenvalues of the hidden to hidden weight matrix deviate from absolute value 1, optimization becomes difficult due to the well studied issue of vanishing and exploding gradients, especially when trying to learn long-term dependencies.

Sequential Image Classification

Predictive Entropy Search for Multi-objective Bayesian Optimization

no code implementations17 Nov 2015 Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams

The results show that PESMO produces better recommendations with a smaller number of evaluations of the objectives, and that a decoupled evaluation can lead to improvements in performance, particularly when the number of objectives is large.

Bayesian Optimization

An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process

no code implementations26 Jun 2015 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.

Topic Models Variational Inference

Student-t Processes as Alternatives to Gaussian Processes

no code implementations18 Feb 2014 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.

Bayesian Optimization Gaussian Processes +1

Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering

no code implementations26 Sep 2013 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.


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