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Found 16 papers, 4 papers with code
Learning Transfers over Several Programming Languages
Third, which characteristics of a language pair are predictive of transfer performance, and how does that depend on the given task.
A Suite of Fairness Datasets for Tabular Classification
There have been many papers with algorithms for improving fairness of machine-learning classifiers for tabular data.
AI for Low-Code for AI
We task 20 developers with varying levels of AI expertise with implementing four ML pipelines using LowCoder, replacing the LowCoder_NL component with a simple keyword search in half the tasks.
Navigating Ensemble Configurations for Algorithmic Fairness
Bias mitigators can improve algorithmic fairness in machine learning models, but their effect on fairness is often not stable across data splits.
An Empirical Study of Modular Bias Mitigators and Ensembles
A popular approach to train more stable models is ensemble learning.
Pipeline Combinators for Gradual AutoML
Automated machine learning (AutoML) can make data scientists more productive.
RASL: Relational Algebra in Scikit-Learn Pipelines
Integrating data preparation with machine-learning (ML) pipelines has been a long- standing challenge.
Learning GraphQL Query Costs (Extended Version)
GraphQL is a query language for APIs and a runtime for executing those queries, fetching the requested data from existing microservices, REST APIs, databases, or other sources.
Lale: Consistent Automated Machine Learning
Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies.
Mining Documentation to Extract Hyperparameter Schemas
Our vision is to reduce the burden to manually create and maintain such schemas for AI automation tools and broaden the reach of automation to larger libraries and richer schemas.
Type-Driven Automated Learning with Lale
Machine-learning automation tools, ranging from humble grid-search to hyperopt, auto-sklearn, and TPOT, help explore large search spaces of possible pipelines.
A semi-supervised deep learning algorithm for abnormal EEG identification
Systems that can automatically analyze EEG signals can aid neurologists by reducing heavy workload and delays.
Yaps: Python Frontend to Stan
Stan is a popular probabilistic programming language with a self-contained syntax and semantics that is close to graphical models.
Programming Languages
Sub-O(log n) Out-of-Order Sliding-Window Aggregation
This paper presents the design, analysis, and implementation of FiBA, a novel sliding-window aggregation algorithm with an amortized upper bound of $O(\log d)$ time per insert or evict, where $d$ is the distance of the inserted or evicted value to the closer end of the window.
Data Structures and Algorithms Databases
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
We use our compilation scheme to build two new backends for the Stanc3 compiler targeting Pyro and NumPyro.
Deep Probabilistic Programming Languages: A Qualitative Study
Deep probabilistic programming languages try to combine the advantages of deep learning with those of probabilistic programming languages.
