26 citations · 80 across the 15 of their papers we have counts for
16 papers
Fidelity-Diversity Metrics for Text
Amanda Wang, Tudor Manole, Florentina Bunea +1
As language modeling technology matures, there is an increasing research focus on the composition and curation of datasets used to train these models. For instance, practitioners c…
How much can we learn from quantum random circuit sampling?
Tudor Manole, Daniel K. Mark, Wenjie Gong +3
Benchmarking quantum devices is a foundational task for the sustained development of quantum technologies. However, accurate in situ characterization of large-scale quantum devices…
Testing Imprecise Hypotheses
Lucas Kania, Tudor Manole, Larry Wasserman +1
Many scientific applications involve testing theories that are only partially specified. This task often amounts to testing the goodness-of-fit of a candidate distribution while al…
Local Poisson Deconvolution for Discrete Signals
Shayan Hundrieser, Tudor Manole, Danila Litskevich +1
We analyze the statistical problem of recovering an atomic signal, modeled as a discrete uniform distribution , from a binned Poisson convolution model. This question is motivat…
Statistical Inference for Optimal Transport Maps: Recent Advances and Perspectives
Sivaraman Balakrishnan, Tudor Manole, Larry Wasserman
In many applications of optimal transport (OT), the object of primary interest is the optimal transport map. This map rearranges mass from one probability distribution to another i…
Stability Bounds for Smooth Optimal Transport Maps and their Statistical Implications
Sivaraman Balakrishnan, Tudor Manole
We study estimators of the optimal transport (OT) map between two probability distributions. We focus on plugin estimators derived from the OT map between estimates of the underlyi…
Central Limit Theorems for Smooth Optimal Transport Maps
Tudor Manole, Sivaraman Balakrishnan, Jonathan Niles-Weed +1
One of the central objects in the theory of optimal transport is the Brenier map: the unique monotone transformation which pushes forward an absolutely continuous probability law o…
Randomized and Exchangeable Improvements of Markov's, Chebyshev's and Chernoff's Inequalities
Aaditya Ramdas, Tudor Manole
We present simple randomized and exchangeable improvements of Markov's inequality, as well as Chebyshev's inequality and Chernoff bounds. Our variants are never worse and typically…
Background Modeling for Double Higgs Boson Production: Density Ratios and Optimal Transport
Tudor Manole, Patrick Bryant, John Alison +2
We study the problem of data-driven background estimation, arising in the search of physics signals predicted by the Standard Model at the Large Hadron Collider. Our work is motiva…
Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models
Tudor Manole, Nhat Ho
We revisit the classical problem of deriving convergence rates for the maximum likelihood estimator (MLE) in finite mixture models. The Wasserstein distance has become a standard l…
Plugin Estimation of Smooth Optimal Transport Maps
Tudor Manole, Sivaraman Balakrishnan, Jonathan Niles-Weed +1
We analyze a number of natural estimators for the optimal transport map between two distributions and show that they are minimax optimal. We adopt the plugin approach: our estimato…
Sharp Convergence Rates for Empirical Optimal Transport with Smooth Costs
Tudor Manole, Jonathan Niles-Weed
We revisit the question of characterizing the convergence rate of plug-in estimators of optimal transport costs. It is well known that an empirical measure comprising independent s…
Martingale Methods for Sequential Estimation of Convex Functionals and Divergences
Tudor Manole, Aaditya Ramdas
We present a unified technique for sequential estimation of convex divergences between distributions, including integral probability metrics like the kernel maximum mean discrepanc…
Uniform Convergence Rates for Maximum Likelihood Estimation under Two-Component Gaussian Mixture Models
Tudor Manole, Nhat Ho
We derive uniform convergence rates for the maximum likelihood estimator and minimax lower bounds for parameter estimation in two-component location-scale Gaussian mixture models w…
Estimating the Number of Components in Finite Mixture Models via the Group-Sort-Fuse Procedure
Tudor Manole, Abbas Khalili
Estimation of the number of components (or order) of a finite mixture model is a long standing and challenging problem in statistics. We propose the Group-Sort-Fuse (GSF) procedure…
Minimax Confidence Intervals for the Sliced Wasserstein Distance
Tudor Manole, Sivaraman Balakrishnan, Larry Wasserman
Motivated by the growing popularity of variants of the Wasserstein distance in statistics and machine learning, we study statistical inference for the Sliced Wasserstein distance--…