1.6k citations · 4.2k across the 71 of their papers we have counts for
6 papers · 2 filters
A New Framework for Query Efficient Active Imitation Learning
Daniel Hsu
We seek to align agent policy with human expert behavior in a reinforcement learning (RL) setting, without any prior knowledge about dynamics, reward function, and unsafe states. T…
Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health
Giannis Karamanolakis, Daniel Hsu, Luis Gravano
In many review classification applications, a fine-grained analysis of the reviews is desirable, because different segments (e.g., sentences) of a review may focus on different asp…
Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training
Giannis Karamanolakis, Daniel Hsu, Luis Gravano
User-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, ap…
A cryptographic approach to black box adversarial machine learning
Kevin Shi, Daniel Hsu, Allison Bishop
We propose a new randomized ensemble technique with a provable security guarantee against black-box transfer attacks. Our proof constructs a new security problem for random binary…
Diameter-based Interactive Structure Discovery
Christopher Tosh, Daniel Hsu
We introduce interactive structure discovery, a generic framework that encompasses many interactive learning settings, including active learning, top-k item identification, interac…
Two models of double descent for weak features
Mikhail Belkin, Daniel Hsu, Ji Xu
The "double descent" risk curve was proposed to qualitatively describe the out-of-sample prediction accuracy of variably-parameterized machine learning models. This article provide…