Search Results for author:
Found 9 papers, 4 papers with code
A Global Geometric Analysis of Maximal Coding Rate Reduction
The maximal coding rate reduction (MCR$^2$) objective for learning structured and compact deep representations is drawing increasing attention, especially after its recent usage in the derivation of fully explainable and highly effective deep network architectures.
On the Estimation Performance of Generalized Power Method for Heteroscedastic Probabilistic PCA
The heteroscedastic probabilistic principal component analysis (PCA) technique, a variant of the classic PCA that considers data heterogeneity, is receiving more and more attention in the data science and signal processing communities.
ReSync: Riemannian Subgradient-based Robust Rotation Synchronization
This work presents ReSync, a Riemannian subgradient-based algorithm for solving the robust rotation synchronization problem, which arises in various engineering applications.
Differential Privacy via Distributionally Robust Optimization
We show that the problem affords a strong dual, and we exploit this duality to develop converging hierarchies of finite-dimensional upper and lower bounding problems.
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
This observation allows us to provide an approximation bound for the distance between the fixed-point set of BAPG and the critical point set of GW.
Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering
The K-subspaces (KSS) method is a generalization of the K-means method for subspace clustering.
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data
In this paper, we study the design and analysis of a class of efficient algorithms for computing the Gromov-Wasserstein (GW) distance tailored to large-scale graph learning tasks.
A Unified Approach to Synchronization Problems over Subgroups of the Orthogonal Group
In this paper, we consider the class of synchronization problems in which the group is a closed subgroup of the orthogonal group.
Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods
To determine the convergence rate of these methods, we give an explicit estimate of the exponent in a Lojasiewicz inequality for the (non-convex) set of critical points of the aforementioned class of problems.
