278 citations · 618 across the 32 of their papers we have counts for
5 papers · 1 filter
Stochastic Bandits with Linear Constraints
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett +1
We study a constrained contextual linear bandit setting, where the goal of the agent is to produce a sequence of policies, whose expected cumulative reward over the course of r…
Faster DBSCAN via subsampled similarity queries
Heinrich Jiang, Jennifer Jang, Jakub Łącki
DBSCAN is a popular density-based clustering algorithm. It computes the -neighborhood graph of a dataset and uses the connected components of the high-degree nodes to decide the…
Learning the Truth From Only One Side of the Story
Heinrich Jiang, Qijia Jiang, Aldo Pacchiano
Learning under one-sided feedback (i.e., where we only observe the labels for examples we predicted positively on) is a fundamental problem in machine learning -- applications incl…
Deep k-NN for Noisy Labels
Dara Bahri, Heinrich Jiang, Maya Gupta
Modern machine learning models are often trained on examples with noisy labels that hurt performance and are hard to identify. In this paper, we provide an empirical study showing…
Robustness Guarantees for Mode Estimation with an Application to Bandits
Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan
Mode estimation is a classical problem in statistics with a wide range of applications in machine learning. Despite this, there is little understanding in its robustness properties…