Denali Molitor — accepted AI-conference papers

2024

Massively Scalable Inverse Reinforcement Learning in Google Maps

Matt Barnes, Matthew Abueg, Oliver F. Lange, Matt Deeds, Jason Trader, Denali Molitor +2

ICLR 2024spotlight

Inverse reinforcement learning (IRL) offers a powerful and general framework for learning humans' latent preferences in route recommendation, yet no approach has successfully addressed planetary-scale problems with hundreds of millions of states and demonstration trajectories. In this paper, we intr…

Cited by 10 Source ↗PDF ↗Scholar ↗

2018

Model Agnostic Supervised Local Explanations

Gregory Plumb, Denali Molitor, Ameet S Talwalkar

NeurIPS 2018poster

Model interpretability is an increasingly important component of practical machine learning. Some of the most common forms of interpretability systems are example-based, local, and global explanations. One of the main challenges in interpretability is designing explanation systems that can capture a…

Cited by 281 Source ↗PDF ↗code ↗Scholar ↗