20 citations · 33 across the 7 of their papers we have counts for
10 papers
Where Do Two Populations of Persistence Diagrams Differ? Calibrated Local Inference at a Fixed Budget
Pramita Bagchi, Edward Bae, Atish Mitra +3
Many two-sample tests for populations of persistence diagrams assess global differences without identifying the regions of the birth-death plane that contribute to them. We study s…
The Null Is the Hard Part: Exact Tests for Memorization in Generative Models
Sushovan Majhi, Pramita Bagchi
Memorization audits of generative models read similarity scores against thresholds, with no null distribution, and the conclusions they support can be wrong. By MemBench's rule, th…
COMPLEX: A Closed-Form Certified Embedding of Multiparameter Persistence Modules
Sushovan Majhi, Atish Mitra, Žiga Virk +1
Every multiparameter persistence vectorization we know of carries a one-sided Lipschitz upper bound and nothing below it: without a lower gauge there is no sense in which the featu…
Statistical Inference for Persistence Diagrams via Landmark Embeddings: Minimax Theory and Finite Approximation
Pramita Bagchi, Sushovan Majhi, Atish Mitra +1
Hilbert-space embeddings enable inference for populations of persistence diagrams, but separation between individual diagrams need not survive population averaging. We develop a fr…
A Closed-Form Adaptive-Landmark Kernel for Certified Point-Cloud and Graph Classification
Sushovan Majhi, Atish Mitra, Žiga Virk +1
We introduce PALACE (Persistence Adaptive-Landmark Analytic Classification Engine), the data-adaptive companion to PLACE, paying a small cross-validation tier on three knobs (budge…
Adaptive Frequency Band Analysis for Functional Time Series
Pramita Bagchi, Scott A. Bruce
The frequency-domain properties of nonstationary functional time series often contain valuable information. These properties are characterized through its time-varying power spectr…
A Test for Separability in Covariance Operators of Random Surfaces
Pramita Bagchi, Holger Dette
The assumption of separability is a simplifying and very popular assumption in the analysis of spatio-temporal or hypersurface data structures. It is often made in situations where…
A nonparametric test for stationarity in functional time series
Anne van Delft, Vaidotas Characiejus, Holger Dette
We propose a new measure for stationarity of a functional time series, which is based on an explicit representation of the -distance between the spectral density operator of a…
A simple test for white noise in functional time series
Pramita Bagchi, Vaidotas Characiejus, Holger Dette
We propose a new procedure for white noise testing of a functional time series. Our approach is based on an explicit representation of the -distance between the spectral densi…
Inference for Monotone Trends Under Dependence
Pramita Bagchi, Moulinath Banerjee, Stilian Stoev
We focus on the problem estimating a monotone trend function under additive and dependent noise. New point-wise confidence interval estimators under both short- and long-range depe…