Search Results for author:
Found 9 papers, 7 papers with code
Chronos: Learning the Language of Time Series
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models.
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
With the ever-increasing number of pretrained models, machine learning practitioners are continuously faced with which pretrained model to use, and how to finetune it for a new dataset.
Deep Pipeline Embeddings for AutoML
As a remedy, this paper proposes a novel neural architecture that captures the deep interaction between the components of a Machine Learning pipeline.
Automatic Machine Learning Model Selection
Bayesian Optimization
+2
Breaking the Paradox of Explainable Deep Learning
Through extensive experiments, we demonstrate that our explainable deep networks are as accurate as state-of-the-art classifiers on tabular data.
Deep Ranking Ensembles for Hyperparameter Optimization
Automatically optimizing the hyperparameters of Machine Learning algorithms is one of the primary open questions in AI.
Transformers Can Do Bayesian Inference
Our method restates the objective of posterior approximation as a supervised classification problem with a set-valued input: it repeatedly draws a task (or function) from the prior, draws a set of data points and their labels from it, masks one of the labels and learns to make probabilistic predictions for it based on the set-valued input of the rest of the data points.
Transfer Learning for Bayesian HPO with End-to-End Meta-Features
As a result, our novel DKLM can learn contextualized dataset-specific similarity representations for hyperparameter configurations.
Multimodal Meta-Learning for Time Series Regression
Recent work has shown the efficiency of deep learning models such as Fully Convolutional Networks (FCN) or Recurrent Neural Networks (RNN) to deal with Time Series Regression (TSR) problems.
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
Hyperparameter optimization (HPO) is a core problem for the machine learning community and remains largely unsolved due to the significant computational resources required to evaluate hyperparameter configurations.
