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Found 6 papers, 3 papers with code
Robust Estimation under the Wasserstein Distance
We study the problem of robust distribution estimation under the Wasserstein metric, a popular discrepancy measure between probability distributions rooted in optimal transport (OT) theory.
Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances
The goal of this work is to quantify this scalability from three key aspects: (i) empirical convergence rates; (ii) robustness to data contamination; and (iii) efficient computational methods.
Learning in Stackelberg Games with Non-myopic Agents
Although learning in Stackelberg games is well-understood when the agent is myopic, non-myopic agents pose additional complications.
Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis
The Wasserstein distance, rooted in optimal transport (OT) theory, is a popular discrepancy measure between probability distributions with various applications to statistics and machine learning.
Smooth $p$-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications
Discrepancy measures between probability distributions, often termed statistical distances, are ubiquitous in probability theory, statistics and machine learning.
Learning with Comparison Feedback: Online Estimation of Sample Statistics
We study an online version of the noisy binary search problem where feedback is generated by a non-stochastic adversary rather than perturbed by random noise.
