research
∙
10/24/2022
Budget-Constrained Bounds for Mini-Batch Estimation of Optimal Transport
Optimal Transport (OT) is a fundamental tool for comparing probability d...
share
research
∙
08/04/2022
Interpretable Distribution Shift Detection using Optimal Transport
We propose a method to identify and characterize distribution shifts in ...
research
∙
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...
research
∙
10/21/2021
On Hard Episodes in Meta-Learning
Existing meta-learners primarily focus on improving the average task acc...
research
∙
09/10/2021
Rapid Model Architecture Adaption for Meta-Learning
Network Architecture Search (NAS) methods have recently gathered much at...
research
∙
05/03/2021
Initialization and Regularization of Factorized Neural Layers
Factorized layers–operations parameterized by products of two or more ma...
research
∙
10/24/2020
Gradient Flows in Dataset Space
The current practice in machine learning is traditionally model-centric,...
research
∙
10/20/2020
Model-specific Data Subsampling with Influence Functions
Model selection requires repeatedly evaluating models on a given dataset...
research
∙
03/20/2020
Weighted Meta-Learning
Meta-learning leverages related source tasks to learn an initialization ...
research
∙
02/07/2020
Geometric Dataset Distances via Optimal Transport
The notion of task similarity is at the core of various machine learning...
research
∙
08/27/2019
Feature Gradients: Scalable Feature Selection via Discrete Relaxation
In this paper we introduce Feature Gradients, a gradient-based search al...
research
∙
02/13/2019
Probabilistic Neural Architecture Search
In neural architecture search (NAS), the space of neural network archite...
research
∙
12/05/2018
Model Compression with Generative Adversarial Networks
More accurate machine learning models often demand more computation and ...
research
∙
10/28/2018
Gaussian Process Prior Variational Autoencoders
Variational autoencoders (VAE) are a powerful and widely-used class of m...
research
∙
05/15/2017
Probabilistic Matrix Factorization for Automated Machine Learning
In order to achieve state-of-the-art performance, modern machine learnin...
research
∙
09/26/2013