7 accepted papers
Text-to-SQL aims to translate natural language queries into SQL statements, which is practical as it enables anyone to easily retrieve the desired information from databases. Recently, many existing approaches tackle this problem with Large Language Models (LLMs), leveraging their strong capability…
Data distillation is a technique of reducing a large dataset into a smaller dataset. The smaller dataset can then be used to train a model which can perform comparably to a model trained on the full dataset. Past works have examined this approach for image datasets, focusing on neural networks as ta…
In-context learning (ICL) is a new paradigm for natural language processing that utilizes Generative Pre-trained Transformer (GPT)-like models. This approach uses prompts that include in-context demonstrations to generate the corresponding output for a new query input. However, applying ICL in real…
A machine learning task can be viewed as a sequential pipeline of different algorithmic choices, including data preprocessing, model selection, and hyper-parameter tuning. Automated machine learning selects this sequence in an automated manner. While such approaches are natural in supervised set…
In this paper, we present FLASH, a framework which addresses for the first time the central AutoML problem of Combined Algorithm Selection and HyperParameter (HP) Optimization (CASH) in the context of Federated Learning (FL). To limit training cost, FLASH incrementally adapts the set of algorithms t…
Careful choice of feature transformations in a dataset can help predictive model performance, data understanding and data exploration. However, finding useful features is a challenge, and while recent Automated Machine Learning (AutoML) systems provide some limited automation for feature engineering…
We address the problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss SuRface Aggregation (FLoRA), a general FL-HPO solution framework that can address use cases of tabular data and any Machine Learning (ML) model including gradient boosting traini…