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05/05/2023
Optimizing Hyperparameters with Conformal Quantile Regression
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely ...
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04/24/2023
Renate: A Library for Real-World Continual Learning
Continual learning enables the incremental training of machine learning ...
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02/08/2023
Fortuna: A Library for Uncertainty Quantification in Deep Learning
We present Fortuna, an open-source library for uncertainty quantificatio...
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09/15/2022
Private Synthetic Data for Multitask Learning and Marginal Queries
We provide a differentially private algorithm for producing synthetic da...
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07/17/2022
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors
As we move away from the data, the predictive uncertainty should increas...
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07/14/2022
PASHA: Efficient HPO with Progressive Resource Allocation
Hyperparameter optimization (HPO) and neural architecture search (NAS) a...
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06/28/2022
Continual Learning with Transformers for Image Classification
In many real-world scenarios, data to train machine learning models beco...
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03/28/2022
Gradient-Matching Coresets for Rehearsal-Based Continual Learning
The goal of continual learning (CL) is to efficiently update a machine l...
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03/21/2022
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
Data-driven methods that detect anomalies in times series data are ubiqu...
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03/09/2022
Memory Efficient Continual Learning for Neural Text Classification
Learning text classifiers based on pre-trained language models has becom...
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12/23/2021
More Than Words: Towards Better Quality Interpretations of Text Classifiers
The large size and complex decision mechanisms of state-of-the-art text ...
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12/09/2021
Gradient-matching coresets for continual learning
We devise a coreset selection method based on the idea of gradient match...
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11/05/2021
Meta-Forecasting by combining Global Deep Representations with Local Adaptation
While classical time series forecasting considers individual time series...
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06/23/2021
Multi-objective Asynchronous Successive Halving
Hyperparameter optimization (HPO) is increasingly used to automatically ...
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06/10/2021
A multi-objective perspective on jointly tuning hardware and hyperparameters
In addition to the best model architecture and hyperparameters, a full A...
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06/08/2021
On the Lack of Robust Interpretability of Neural Text Classifiers
With the ever-increasing complexity of neural language models, practitio...
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04/16/2021
Overfitting in Bayesian Optimization: an empirical study and early-stopping solution
Bayesian Optimization (BO) is a successful methodology to tune the hyper...
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03/30/2021
A resource-efficient method for repeated HPO and NAS problems
In this work we consider the problem of repeated hyperparameter and neur...
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02/25/2021
Hyperparameter Transfer Learning with Adaptive Complexity
Bayesian optimization (BO) is a sample efficient approach to automatical...
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02/17/2021
BORE: Bayesian Optimization by Density-Ratio Estimation
Bayesian optimization (BO) is among the most effective and widely-used b...
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12/15/2020
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization
Tuning complex machine learning systems is challenging. Machine learning...
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12/15/2020
Amazon SageMaker Autopilot: a white box AutoML solution at scale
AutoML systems provide a black-box solution to machine learning problems...
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11/23/2020
Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization
Bayesian optimization (BO) is a popular method to optimize expensive bla...
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06/09/2020
Fair Bayesian Optimization
Given the increasing importance of machine learning (ML) in our lives, a...
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03/24/2020
Model-based Asynchronous Hyperparameter Optimization
We introduce a model-based asynchronous multi-fidelity hyperparameter op...
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03/22/2020
Cost-aware Bayesian Optimization
Bayesian optimization (BO) is a class of global optimization algorithms,...
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02/27/2020
LEEP: A New Measure to Evaluate Transferability of Learned Representations
We introduce a new measure to evaluate the transferability of representa...
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10/15/2019
Constrained Bayesian Optimization with Max-Value Entropy Search
Bayesian optimization (BO) is a model-based approach to sequentially opt...
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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...
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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...
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11/30/2017
An interpretable latent variable model for attribute applicability in the Amazon catalogue
Learning attribute applicability of products in the Amazon catalog (e.g....
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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...
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12/23/2015
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints
We present an adaptive online gradient descent algorithm to solve online...
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04/18/2015
Online Inference for Relation Extraction with a Reduced Feature Set
Access to web-scale corpora is gradually bringing robust automatic knowl...
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10/24/2011