278 citations · 618 across the 32 of their papers we have counts for
8 papers · 1 filter
DBSCAN++: Towards fast and scalable density clustering
Jennifer Jang, Heinrich Jiang
DBSCAN is a classical density-based clustering procedure with tremendous practical relevance. However, DBSCAN implicitly needs to compute the empirical density for each sample poin…
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
Andrew Cotter, Heinrich Jiang, Serena Wang +4
We show that many machine learning goals, such as improved fairness metrics, can be expressed as constraints on the model's predictions, which we call rate constraints. We study th…
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Andrew Cotter, Maya Gupta, Heinrich Jiang +5
Classifiers can be trained with data-dependent constraints to satisfy fairness goals, reduce churn, achieve a targeted false positive rate, or other policy goals. We study the gene…
Interpretable Set Functions
Andrew Cotter, Maya Gupta, Heinrich Jiang +4
We propose learning flexible but interpretable functions that aggregate a variable-length set of permutation-invariant feature vectors to predict a label. We use a deep lattice net…
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Heinrich Jiang, Jennifer Jang, Samory Kpotufe
We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data. Such seedings act as more stable and expressive…
To Trust Or Not To Trust A Classifier
Heinrich Jiang, Been Kim, Melody Y. Guan +1
Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI. While the bulk of the effort in machine learning research has…
Two-Player Games for Efficient Non-Convex Constrained Optimization
Andrew Cotter, Heinrich Jiang, Karthik Sridharan
In recent years, constrained optimization has become increasingly relevant to the machine learning community, with applications including Neyman-Pearson classification, robust opti…
Nonparametric Stochastic Contextual Bandits
Melody Y. Guan, Heinrich Jiang
We analyze the -armed bandit problem where the reward for each arm is a noisy realization based on an observed context under mild nonparametric assumptions. We attain tight resu…