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Creators/Authors contains: "Mukherjee, Debarghya"

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  1. ; ; (, The Annals of Statistics)
    Free, publicly-accessible full text available December 1, 2025
  2. ; ; ; (, International Conference on Learning Representations)
    Accepted Manuscript Full Text Available
  3. ; ; ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  4. ; ; ; (, NeurIPS 2022)
    Accepted Manuscript Full Text Available
  5. ; ; (, Electronic Journal of Statistics)
    Full Text Available 
  6. ; ; ; ; (, International Conference on Machine Learning)
    Optimal transport (OT) measures distances between distributions in a way that depends on the geometry of the sample space. In light of recent advances in computational OT, OT distances are widely used as loss functions in machine learning. Despite their prevalence and advantages, OT loss functions can be extremely sensitive to outliers. In fact, a single adversarially-picked outlier can increase the standard W2-distance arbitrarily. To address this issue, we propose an outlier-robust formulation of OT. Our formulation is convex but challenging to scale at a first glance. Our main contribution is deriving an \emph{equivalent} formulation based on cost truncation that is easy to incorporate into modern algorithms for computational OT. We demonstrate the benefits of our formulation in mean estimation problems under the Huber contamination model in simulations and outlier detection tasks on real data. 
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    Accepted Manuscript Full Text Available
  7. ; ; ; (, Advances in Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  8. ; ; ; (, Advances in Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  9. ; ; ; ; (, Proceedings of the 38th International Conference on Machine Learning)
    Accepted Manuscript Full Text Available