research
∙
05/26/2023
Three Towers: Flexible Contrastive Learning with Pretrained Image Models
We introduce Three Towers (3T), a flexible method to improve the contras...
share
research
∙
03/03/2023
When does Privileged Information Explain Away Label Noise?
Leveraging privileged information (PI), or features available during tra...
research
∙
01/30/2023
Massively Scaling Heteroscedastic Classifiers
Heteroscedastic classifiers, which learn a multivariate Gaussian distrib...
research
∙
10/19/2022
On the Adversarial Robustness of Mixture of Experts
Adversarial robustness is a key desirable property of neural networks. I...
research
∙
06/06/2022
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts
Large sparsely-activated models have obtained excellent performance in m...
research
∙
02/18/2022
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
Supervised learning datasets often have privileged information, in the f...
research
∙
12/15/2021
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach
Black box optimization requires specifying a search space to explore for...
research
∙
10/07/2021
Sparse MoEs meet Efficient Ensembles
Machine learning models based on the aggregated outputs of submodels, ei...
research
∙
10/06/2021
Deep Classifiers with Label Noise Modeling and Distance Awareness
Uncertainty estimation in deep learning has recently emerged as a crucia...
research
∙
06/10/2021
Scaling Vision with Sparse Mixture of Experts
Sparsely-gated Mixture of Experts networks (MoEs) have demonstrated exce...
research
∙
06/07/2021
Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning
High-quality estimates of uncertainty and robustness are crucial for num...
research
∙
05/19/2021
Correlated Input-Dependent Label Noise in Large-Scale Image Classification
Large scale image classification datasets often contain noisy labels. We...
research
∙
12/15/2020
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization
Tuning complex machine learning systems is challenging. Machine learning...
research
∙
12/15/2020
Amazon SageMaker Autopilot: a white box AutoML solution at scale
AutoML systems provide a black-box solution to machine learning problems...
research
∙
10/13/2020
Training independent subnetworks for robust prediction
Recent approaches to efficiently ensemble neural networks have shown tha...
research
∙
06/24/2020
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Ensembles over neural network weights trained from different random init...
research
∙
06/10/2020
On Mixup Regularization
Mixup is a data augmentation technique that creates new examples as conv...
research
∙
02/07/2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximatin...
research
∙
02/06/2020
How Good is the Bayes Posterior in Deep Neural Networks Really?
During the past five years the Bayesian deep learning community has deve...
research
∙
01/14/2020
Hydra: Preserving Ensemble Diversity for Model Distillation
Ensembles of models have been empirically shown to improve predictive pe...
research
∙
10/15/2019
Constrained Bayesian Optimization with Max-Value Entropy Search
Bayesian optimization (BO) is a model-based approach to sequentially opt...
research
∙
09/27/2019
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Bayesian optimization (BO) is a successful methodology to optimize black...
research
∙
12/08/2017
Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start
Bayesian optimization (BO) is a model-based approach for gradient-free b...
research
∙
02/17/2016
Online optimization and regret guarantees for non-additive long-term constraints
We consider online optimization in the 1-lookahead setting, where the ob...
research
∙
12/23/2015
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints
We present an adaptive online gradient descent algorithm to solve online...
research
∙
07/19/2014
Sparse and spurious: dictionary learning with noise and outliers
A popular approach within the signal processing and machine learning com...
research
∙
03/20/2014
On The Sample Complexity of Sparse Dictionary Learning
In the synthesis model signals are represented as a sparse combinations ...
research
∙
12/13/2013
Sample Complexity of Dictionary Learning and other Matrix Factorizations
Many modern tools in machine learning and signal processing, such as spa...
research
∙
10/02/2012
Local stability and robustness of sparse dictionary learning in the presence of noise
A popular approach within the signal processing and machine learning com...
research
∙
09/12/2011
Structured sparsity through convex optimization
Sparse estimation methods are aimed at using or obtaining parsimonious r...
research
∙
08/03/2011
Optimization with Sparsity-Inducing Penalties
Sparse estimation methods are aimed at using or obtaining parsimonious r...
research
∙
05/02/2011
Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity
Inverse inference, or "brain reading", is a recent paradigm for analyzin...
research
∙
04/11/2011
Convex and Network Flow Optimization for Structured Sparsity
We consider a class of learning problems regularized by a structured spa...
research
∙
09/11/2010
Proximal Methods for Hierarchical Sparse Coding
Sparse coding consists in representing signals as sparse linear combinat...
research
∙
08/31/2010
Network Flow Algorithms for Structured Sparsity
We consider a class of learning problems that involve a structured spars...
research
∙
09/08/2009