54 citations · 62 across the 7 of their papers we have counts for
8 papers
Optimal Dorfman Group Testing for Symmetric Distributions
Nicholas C. Landolfi, Sanjay Lall
We study Dorfman's classical group testing protocol in a novel setting where individual specimen statuses are modeled as exchangeable random variables. We are motivated by infectio…
Unsupervised language models for disease variant prediction
Allan Zhou, Nicholas C. Landolfi, Daniel C. O'Neill
There is considerable interest in predicting the pathogenicity of protein variants in human genes. Due to the sparsity of high quality labels, recent approaches turn to \textit{uns…
Probabilistic Modeling Using Tree Linear Cascades
Nicholas C. Landolfi, Sanjay Lall
We introduce tree linear cascades, a class of linear structural equation models for which the error variables are uncorrelated but need not be Gaussian nor independent. We show tha…
Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences
Erdem Bıyık, Dylan P. Losey, Malayandi Palan +3
Reward functions are a common way to specify the objective of a robot. As designing reward functions can be extremely challenging, a more promising approach is to directly learn re…
Asking Easy Questions: A User-Friendly Approach to Active Reward Learning
Erdem Bıyık, Malayandi Palan, Nicholas C. Landolfi +2
Robots can learn the right reward function by querying a human expert. Existing approaches attempt to choose questions where the robot is most uncertain about the human's response;…
A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning
Nicholas C. Landolfi, Garrett Thomas, Tengyu Ma
The aim of multi-task reinforcement learning is two-fold: (1) efficiently learn by training against multiple tasks and (2) quickly adapt, using limited samples, to a variety of new…
Learning Reward Functions by Integrating Human Demonstrations and Preferences
Malayandi Palan, Nicholas C. Landolfi, Gleb Shevchuk +1
Our goal is to accurately and efficiently learn reward functions for autonomous robots. Current approaches to this problem include inverse reinforcement learning (IRL), which uses…
Social Cohesion in Autonomous Driving
Nicholas C. Landolfi, Anca D. Dragan
Autonomous cars can perform poorly for many reasons. They may have perception issues, incorrect dynamics models, be unaware of obscure rules of human traffic systems, or follow cer…