Nicolo Fusi | DeepAI

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∙ 10/24/2022

Budget-Constrained Bounds for Mini-Batch Estimation of Optimal Transport

Optimal Transport (OT) is a fundamental tool for comparing probability d...

0 David Alvarez-Melis, et al. ∙

∙ 08/04/2022

Interpretable Distribution Shift Detection using Optimal Transport

We propose a method to identify and characterize distribution shifts in ...

29 Neha Hulkund, et al. ∙

∙ 06/25/2022

Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach

The rising growth of deep neural networks (DNNs) and datasets in size mo...

0 Syrine Belakaria, et al. ∙

∙ 10/21/2021

On Hard Episodes in Meta-Learning

Existing meta-learners primarily focus on improving the average task acc...

0 Samyadeep Basu, et al. ∙

∙ 09/10/2021

Rapid Model Architecture Adaption for Meta-Learning

Network Architecture Search (NAS) methods have recently gathered much at...

0 Yiren Zhao, et al. ∙

∙ 05/03/2021

Initialization and Regularization of Factorized Neural Layers

Factorized layers–operations parameterized by products of two or more ma...

0 Mikhail Khodak, et al. ∙

∙ 10/24/2020

Gradient Flows in Dataset Space

The current practice in machine learning is traditionally model-centric,...

29 David Alvarez-Melis, et al. ∙

∙ 10/20/2020

Model-specific Data Subsampling with Influence Functions

Model selection requires repeatedly evaluating models on a given dataset...

0 Anant Raj, et al. ∙

∙ 03/20/2020

Weighted Meta-Learning

Meta-learning leverages related source tasks to learn an initialization ...

0 Diana Cai, et al. ∙

∙ 02/07/2020

Geometric Dataset Distances via Optimal Transport

The notion of task similarity is at the core of various machine learning...

0 David Alvarez-Melis, et al. ∙

∙ 08/27/2019

Feature Gradients: Scalable Feature Selection via Discrete Relaxation

In this paper we introduce Feature Gradients, a gradient-based search al...

0 Rishit Sheth, et al. ∙

∙ 02/13/2019

Probabilistic Neural Architecture Search

In neural architecture search (NAS), the space of neural network archite...

1 Francesco Paolo Casale, et al. ∙

∙ 12/05/2018

Model Compression with Generative Adversarial Networks

More accurate machine learning models often demand more computation and ...

0 Ruishan Liu, et al. ∙

∙ 10/28/2018

Gaussian Process Prior Variational Autoencoders

Variational autoencoders (VAE) are a powerful and widely-used class of m...

1 Francesco Paolo Casale, et al. ∙

∙ 05/15/2017

Probabilistic Matrix Factorization for Automated Machine Learning

In order to achieve state-of-the-art performance, modern machine learnin...

0 Nicolo Fusi, et al. ∙

∙ 09/26/2013

Gaussian Processes for Big Data

We introduce stochastic variational inference for Gaussian process model...

0 James Hensman, et al. ∙

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