Search Results for author:
Found 11 papers, 7 papers with code
Ensembling Finetuned Language Models for Text Classification
Finetuning is a common practice widespread across different communities to adapt pretrained models to particular tasks.
Dynamic Post-Hoc Neural Ensemblers
In this study, we explore employing neural networks as ensemble methods, emphasizing the significance of dynamic ensembling to leverage diverse model predictions adaptively.
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
Interpretable Mesomorphic Networks for Tabular Data
Even though neural networks have been long deployed in applications involving tabular data, still existing neural architectures are not explainable by design.
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.
