Search Results for author:
Found 9 papers, 4 papers with code
Using Geographic Location-based Public Health Features in Survival Analysis
Time elapsed till an event of interest is often modeled using the survival analysis methodology, which estimates a survival score based on the input features.
Nearest Neighbor Search Under Uncertainty
Specifically, consider the setting in which an NNS algorithm has access only to a stochastic distance oracle that provides a noisy, unbiased estimate of the distance between any pair of points, rather than the exact distance.
Chernoff Sampling for Active Testing and Extension to Active Regression
Active learning can reduce the number of samples needed to perform a hypothesis test and to estimate the parameters of a model.
Finding All $\epsilon$-Good Arms in Stochastic Bandits
The pure-exploration problem in stochastic multi-armed bandits aims to find one or more arms with the largest (or near largest) means.
Finding All ε-Good Arms in Stochastic Bandits
Mathematically, the all-{\epsilon}-good arm identification problem presents significant new challenges and surprises that do not arise in the pure-exploration objectives studied in the past.
Optimal Confidence Regions for the Multinomial Parameter
More precisely, consider an empirical distribution $\widehat{\boldsymbol{p}}$ generated from $n$ iid realizations of a random variable that takes one of $k$ possible values according to an unknown distribution $\boldsymbol{p}$.
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
This paper studies the problem of adaptively sampling from K distributions (arms) in order to identify the largest gap between any two adjacent means.
Learning Nearest Neighbor Graphs from Noisy Distance Samples
We consider the problem of learning the nearest neighbor graph of a dataset of n items.
Privacy-Preserving Adversarial Networks
We propose a data-driven framework for optimizing privacy-preserving data release mechanisms to attain the information-theoretically optimal tradeoff between minimizing distortion of useful data and concealing specific sensitive information.
