Search Results for author:
Found 10 papers, 4 papers with code
Falcon: Fair Active Learning using Multi-armed Bandits
Given a user-specified group fairness measure, Falcon identifies samples from "target groups" (e. g., (attribute=female, label=positive)) that are the most informative for improving fairness.
iFlipper: Label Flipping for Individual Fairness
We then propose an approximate linear programming algorithm and provide theoretical guarantees on how close its result is to the optimal solution in terms of the number of label flips.
OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning
Existing techniques for producing fair ML models either are limited to the type of fairness constraints they can handle (e. g., preprocessing) or require nontrivial modifications to downstream ML training algorithms (e. g., in-processing).
ALEX: An Updatable Adaptive Learned Index
The original work by Kraska et al. shows that a learned index beats a B+Tree by a factor of up to three in search time and by an order of magnitude in memory footprint.
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning (Technical Report)
Learning from the data stored in a database is an important function increasingly available in relational engines.
PSFGAN: a generative adversarial network system for separating quasar point sources and host galaxy light
We test the method using Sloan Digital Sky Survey (SDSS) r-band images with artificial AGN point sources added which are then removed using the GAN and with parametric methods using GALFIT.
Astrophysics of Galaxies Data Analysis, Statistics and Probability
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning
We examine training at reduced precision, both from a theoretical and practical perspective, and ask: is it possible to train models at end-to-end low precision with provable guarantees?
MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data?
We then compare the performance of the top winning code available from Kaggle with that of running machine learning clouds from both Azure and Amazon on mlbench.
Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit
Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data.
The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning
When applied to linear models together with double sampling, we save up to another 1. 7x in data movement compared with uniform quantization.
