Bridging the Data Gap in AI Reliability Research and Establishing DR-AIR, a Comprehensive Data Repository for AI Reliability

1 code implementation • 17 Feb 2025 • Simin Zheng, Jared M. Clark, Fatemeh Salboukh, Priscila Silva, Karen da Mata, Fenglian Pan, Jie Min, Jiayi Lian, Caleb B. King, Lance Fiondella, Jian Liu, Xinwei Deng, Yili Hong

To address this gap, this paper focuses on conducting a comprehensive review of available AI reliability data and establishing DR-AIR: a data repository for AI reliability.

Predicting Resilience with Neural Networks

no code implementations • 11 Aug 2023 • Karen da Mata, Priscila Silva, Lance Fiondella

Goodness-of-fit measures are computed to evaluate the models and compared with a classical statistical model, including mean squared error and adjusted R squared.

Automating Staged Rollout with Reinforcement Learning

no code implementations • 1 Apr 2022 • Shadow Pritchard, Vidhyashree Nagaraju, Lance Fiondella

This paper demonstrates the potential to automate staged rollout with multi-objective reinforcement learning in order to dynamically balance stakeholder needs such as time to deliver new features and downtime incurred by failures due to latent defects.

Multi-Objective Reinforcement Learning reinforcement-learning +1