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Found 9 papers, 4 papers with code
Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics
Our work provides a new perspective of model robustness through the lens of Wasserstein geodesic-based interpolation with a practical off-the-shelf strategy that can be combined with existing robust training methods.
Towards Algorithmic Fairness in Space-Time: Filling in Black Holes
New technologies and the availability of geospatial data have drawn attention to spatio-temporal biases present in society.
Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields
Analysis of heterogeneous patterns in complex spatio-temporal data finds usage across various domains in applied science and engineering, including training autonomous vehicles to navigate in complex traffic scenarios.
Functional optimal transport: map estimation and domain adaptation for functional data
We introduce a formulation of optimal transport problem for distributions on function spaces, where the stochastic map between functional domains can be partially represented in terms of an (infinite-dimensional) Hilbert-Schmidt operator mapping a Hilbert space of functions to another.
Outlier Robust Optimal Transport
In light of recent advances in solving the OT problem, OT distances are widely used as loss functions in minimum distance estimation.
Robust Unsupervised Learning of Temporal Dynamic Interactions
These distance metrics can serve as an objective for assessing the stability of an interaction learning algorithm.
Dirichlet Simplex Nest and Geometric Inference
We propose Dirichlet Simplex Nest, a class of probabilistic models suitable for a variety of data types, and develop fast and provably accurate inference algorithms by accounting for the model's convex geometry and low dimensional simplicial structure.
Scalable inference of topic evolution via models for latent geometric structures
We develop new models and algorithms for learning the temporal dynamics of the topic polytopes and related geometric objects that arise in topic model based inference.
Conic Scan-and-Cover algorithms for nonparametric topic modeling
We propose new algorithms for topic modeling when the number of topics is unknown.
