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Found 13 papers, 8 papers with code
Otter: Generating Tests from Issues to Validate SWE Patches
While there has been plenty of work on generating tests from existing code, there has been limited work on generating tests from issues.
TDD-Bench Verified: Can LLMs Generate Tests for Issues Before They Get Resolved?
Ideally, tests for TDD should be fail-to-pass (i. e., fail before the issue is resolved and pass after) and have good adequacy with respect to covering the code changed during issue resolution.
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.
Formalization of a Stochastic Approximation Theorem
Stochastic approximation algorithms are iterative procedures which are used to approximate a target value in an environment where the target is unknown and direct observations are corrupted by noise.
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.
CertRL: Formalizing Convergence Proofs for Value and Policy Iteration in Coq
Reinforcement learning algorithms solve sequential decision-making problems in probabilistic environments by optimizing for long-term reward.
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.
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.
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
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.
Ariadne: Analysis for Machine Learning Program
We report on Ariadne: applying a static framework, WALA, to machine learning code that uses TensorFlow.
Programming Languages
