6 citations · 17 across the 9 of their papers we have counts for
13 papers
Adaptive Partitioning Schemes for Optimistic Optimization
Raja Sunkara, Ardhendu Tripathy
Applications such as engineering design often require us to optimize a black-box function, i.e., a system whose inner processing is not analytically known and whose gradients are n…
Using Geographic Location-based Public Health Features in Survival Analysis
Navid Seidi, Ardhendu Tripathy, Sajal K. Das
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. There is a resurgence…
Nearest Neighbor Search Under Uncertainty
Blake Mason, Ardhendu Tripathy, Robert Nowak
Nearest Neighbor Search (NNS) is a central task in knowledge representation, learning, and reasoning. There is vast literature on efficient algorithms for constructing data structu…
Chernoff Sampling for Active Testing and Extension to Active Regression
Subhojyoti Mukherjee, Ardhendu Tripathy, Robert Nowak
Active learning can reduce the number of samples needed to perform a hypothesis test and to estimate the parameters of a model. In this paper, we revisit the work of Chernoff that…
Optimal Confidence Regions for the Multinomial Parameter
Matthew L. Malloy, Ardhendu Tripathy, Robert D. Nowak
Construction of tight confidence regions and intervals is central to statistical inference and decision making. This paper develops new theory showing minimum average volume confid…
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
Sumeet Katariya, Ardhendu Tripathy, Robert Nowak
This paper studies the problem of adaptively sampling from K distributions (arms) in order to identify the largest gap between any two adjacent means. We call this the MaxGap-bandi…
Learning Nearest Neighbor Graphs from Noisy Distance Samples
Blake Mason, Ardhendu Tripathy, Robert Nowak
We consider the problem of learning the nearest neighbor graph of a dataset of n items. The metric is unknown, but we can query an oracle to obtain a noisy estimate of the distance…
Zero-error Function Computation on a Directed Acyclic Network
Ardhendu Tripathy, Aditya Ramamoorthy
We study the rate region of variable-length source-network codes that are used to compute a function of messages observed over a network. The particular network considered here is…
Privacy-Preserving Adversarial Networks
Ardhendu Tripathy, Ye Wang, Prakash Ishwar
We propose a data-driven framework for optimizing privacy-preserving data release mechanisms to attain the information-theoretically optimal tradeoff between minimizing distortion…
Sum-networks from undirected graphs: construction and capacity analysis
Ardhendu Tripathy, Aditya Ramamoorthy
We consider a directed acyclic network with multiple sources and multiple terminals where each terminal is interested in decoding the sum of independent sources generated at the so…
Sum-networks from incidence structures: construction and capacity analysis
Ardhendu Tripathy, Aditya Ramamoorthy
A sum-network is an instance of a network coding problem over a directed acyclic network in which each terminal node wants to compute the sum over a finite field of the information…
On Computation Rates for Arithmetic Sum
Ardhendu Tripathy, Aditya Ramamoorthy
For zero-error function computation over directed acyclic networks, existing upper and lower bounds on the computation capacity are known to be loose. In this work we consider the…
Capacity of Sum-networks for Different Message Alphabets
Ardhendu Tripathy, Aditya Ramamoorthy
A sum-network is a directed acyclic network in which all terminal nodes demand the `sum' of the independent information observed at the source nodes. Many characteristics of the we…