4 citations · 7 across the 4 of their papers we have counts for
4 papers
Fuzzing the brain: Automated stress testing for the safety of ML-driven neurostimulation
Mara Downing, Matthew Peng, Jacob Granley +2
Objective: Machine learning (ML) models are increasingly used to generate electrical stimulation patterns in neuroprosthetic devices such as visual prostheses. While these models p…
The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances
Allen Nie, Yash Chandak, Miroslav Suzara +6
Large language models (LLMs) are quickly being adopted in a wide range of learning experiences, especially via ubiquitous and broadly accessible chat interfaces like ChatGPT and Co…
Minimax Optimal Online Imitation Learning via Replay Estimation
Gokul Swamy, Nived Rajaraman, Matthew Peng +5
Online imitation learning is the problem of how best to mimic expert demonstrations, given access to the environment or an accurate simulator. Prior work has shown that in the infi…
Linear Representation Meta-Reinforcement Learning for Instant Adaptation
Matt Peng, Banghua Zhu, Jiantao Jiao
This paper introduces Fast Linearized Adaptive Policy (FLAP), a new meta-reinforcement learning (meta-RL) method that is able to extrapolate well to out-of-distribution tasks witho…