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Found 7 papers, 2 papers with code
Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
We consider the task of retraining machine learning (ML) models when new batches of data become available.
BackboneLearn: A Library for Scaling Mixed-Integer Optimization-Based Machine Learning
We present BackboneLearn: an open-source software package and framework for scaling mixed-integer optimization (MIO) problems with indicator variables to high-dimensional problems.
Data Analytics with Differential Privacy
Our work includes a detailed theoretical analysis of the distributed, differentially private entropy estimator which we use in one of our algorithms, as well as a detailed experimental evaluation, using both synthetic and real-world data.
Improving Stability in Decision Tree Models
Owing to their inherently interpretable structure, decision trees are commonly used in applications where interpretability is essential.
Slowly Varying Regression under Sparsity
The algorithm is highly scalable, allowing us to train models with thousands of parameters.
Frequency Estimation in Data Streams: Learning the Optimal Hashing Scheme
We present a novel approach for the problem of frequency estimation in data streams that is based on optimization and machine learning.
The Backbone Method for Ultra-High Dimensional Sparse Machine Learning
We present the backbone method, a generic framework that enables sparse and interpretable supervised machine learning methods to scale to ultra-high dimensional problems.
