64 citations · 81 across the 6 of their papers we have counts for
6 papers
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach
Syrine Belakaria, Janardhan Rao Doppa, Nicolo Fusi +1
The rising growth of deep neural networks (DNNs) and datasets in size motivates the need for efficient solutions for simultaneous model selection and training. Many methods for hyp…
Direct loss minimization algorithms for sparse Gaussian processes
Yadi Wei, Rishit Sheth, Roni Khardon
The paper provides a thorough investigation of Direct loss minimization (DLM), which optimizes the posterior to minimize predictive loss, in sparse Gaussian processes. For the conj…
Weighted Meta-Learning
Diana Cai, Rishit Sheth, Lester Mackey +1
Meta-learning leverages related source tasks to learn an initialization that can be quickly fine-tuned to a target task with limited labeled examples. However, many popular meta-le…
Feature Gradients: Scalable Feature Selection via Discrete Relaxation
Rishit Sheth, Nicolo Fusi
In this paper we introduce Feature Gradients, a gradient-based search algorithm for feature selection. Our approach extends a recent result on the estimation of learnability in the…
Probabilistic Matrix Factorization for Automated Machine Learning
Nicolo Fusi, Rishit Sheth, Huseyn Melih Elibol
In order to achieve state-of-the-art performance, modern machine learning techniques require careful data pre-processing and hyperparameter tuning. Moreover, given the ever increas…
Monte Carlo Structured SVI for Two-Level Non-Conjugate Models
Rishit Sheth, Roni Khardon
The stochastic variational inference (SVI) paradigm, which combines variational inference, natural gradients, and stochastic updates, was recently proposed for large-scale data ana…