Search Results for author:
Found 6 papers, 3 papers with code
A Data-Centric Online Market for Machine Learning: From Discovery to Pricing
We envision that the synergy of our data and model discovery algorithm and pricing mechanism will be an important step towards building a new data-centric online market that serves ML users effectively.
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm
Data markets help with identifying valuable training data: model consumers pay to train a model, the market uses that budget to identify data and train the model (the budget allocation problem), and finally the market compensates data providers according to their data contribution (revenue allocation problem).
METAM: Goal-Oriented Data Discovery
Data is a central component of machine learning and causal inference tasks.
Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach
All in all, the technique is a stepping stone towards building learned discovery systems.
ARDA: Automatic Relational Data Augmentation for Machine Learning
Our system has two distinct components: (1) a framework to search and join data with the input data, based on various attributes of the input, and (2) an efficient feature selection algorithm that prunes out noisy or irrelevant features from the resulting join.
Smallify: Learning Network Size while Training
As neural networks become widely deployed in different applications and on different hardware, it has become increasingly important to optimize inference time and model size along with model accuracy.
