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Found 11 papers, 5 papers with code
Massively Scalable Inverse Reinforcement Learning in Google Maps
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.
Smartphone-based Hard-braking Event Detection at Scale for Road Safety Services
Hard-braking events have been widely used as a safety surrogate due to their relatively high prevalence and ease of detection with embedded vehicle sensors.
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data.
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation
In many environments, only a relatively small subset of the complete state space is necessary in order to accomplish a given task.
Adaptive Robot-Assisted Feeding: An Online Learning Framework for Acquiring Previously Unseen Food Items
A successful robot-assisted feeding system requires bite acquisition of a wide variety of food items.
Imitation Learning as $f$-Divergence Minimization
We show that the state-of-the-art methods such as GAIL and behavior cloning, due to their choice of loss function, often incorrectly interpolate between such modes.
On the Interaction Effects Between Prediction and Clustering
Machine learning systems increasingly depend on pipelines of multiple algorithms to provide high quality and well structured predictions.
Performance Bounds for Graphical Record Linkage
Record linkage involves merging records in large, noisy databases to remove duplicate entities.
Clustering on the Edge: Learning Structure in Graphs
With the recent popularity of graphical clustering methods, there has been an increased focus on the information between samples.
A Practioner's Guide to Evaluating Entity Resolution Results
This paper provides practitioners the basic knowledge to begin evaluating their entity resolution results.
Performance Bounds for Pairwise Entity Resolution
One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labeled reference datasets.
