activity
20162022
most citedInteractive Weak Supervision: Learning Useful Heuristics for Data Labeling

8 citations · 31 across the 13 of their papers we have counts for

collaborators
Showing 2019Show all

5 papers · 1 filter

cs.LG20193 cited

Pairwise Feedback for Data Programming

Benedikt Boecking, Artur Dubrawski

The scalability of the labeling process and the attainable quality of labels have become limiting factors for many applications of machine learning. The programmatic creation of la…

cs.LG20191 cited

Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning

Chufan Gao, Fabian Falck, Mononito Goswami +3

Monitoring physiological responses to hemodynamic stress can help in determining appropriate treatment and ensuring good patient outcomes. Physicians' intuition suggests that the h…

cs.LG20193 cited

Zeroth Order Non-convex optimization with Dueling-Choice Bandits

Yichong Xu, Aparna Joshi, Aarti Singh +1

We consider a novel setting of zeroth order non-convex optimization, where in addition to querying the function value at a given point, we can also duel two points and get the poin…

cs.LG2019

Thresholding Bandit Problem with Both Duels and Pulls

Yichong Xu, Xi Chen, Aarti Singh +1

The Thresholding Bandit Problem (TBP) aims to find the set of arms with mean rewards greater than a given threshold. We consider a new setting of TBP, where in addition to pulling…

cs.LG20193 cited

Nonlinear Semi-Parametric Models for Survival Analysis

Chirag Nagpal, Rohan Sangave, Amit Chahar +3

Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis. These methods involve fitting…