Search Results for author:
Found 13 papers, 6 papers with code
A Flexible and Scalable Approach for Collecting Wildlife Advertisements on the Web
Furthermore, given that the volume of data is staggering, we need scalable mechanisms to acquire, filter, and store the ads, as well as to make them available for analysis.
ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models
We introduce ArcheType, a simple, practical method for context sampling, prompt serialization, model querying, and label remapping, which enables large language models to solve CTA problems in a fully zero-shot manner.
Ranked #1 on
Column Type Annotation
on WDC SOTAB
(Weighted F1 metric)
eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems
Recent advancements in software and hardware technologies have enabled the use of AI/ML models in everyday applications has significantly improved the quality of service rendered.
AlphaD3M: Machine Learning Pipeline Synthesis
We introduce AlphaD3M, an automatic machine learning (AutoML) system based on meta reinforcement learning using sequence models with self play.
Correlation Sketches for Approximate Join-Correlation Queries
The increasing availability of structured datasets, from Web tables and open-data portals to enterprise data, opens up opportunities~to enrich analytics and improve machine learning models through relational data augmentation.
Auctus: A Dataset Search Engine for Data Augmentation
The large volumes of structured data currently available, from Web tables to open-data portals and enterprise data, open up new opportunities for progress in answering many important scientific, societal, and business questions.
PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines
In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to search and generate end-to-end learning pipelines.
Human-Computer Interaction
Debugging Machine Learning Pipelines
Machine learning tasks entail the use of complex computational pipelines to reach quantitative and qualitative conclusions.
AutoML using Metadata Language Embeddings
We use these embeddings in a neural architecture to learn the distance between best-performing pipelines.
Visus: An Interactive System for Automatic Machine Learning Model Building and Curation
In this paper, we present Visus, a system designed to support the model building process and curation of ML data processing pipelines generated by AutoML systems.
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Automatic machine learning is an important problem in the forefront of machine learning.
A Topic-Agnostic Approach for Identifying Fake News Pages
Fake news and misinformation have been increasingly used to manipulate popular opinion and influence political processes.
Bootstrapping Domain-Specific Content Discovery on the Web
Given a domain of interest $D$, subject-matter experts (SMEs) must search for relevant websites and collect a set of representative Web pages to serve as training examples for creating a classifier that recognizes pages in $D$, as well as a set of pages to seed the crawl.
