Most of the codes related to my papers can be accessed below. Contact me if you don’t find something.

- SPOTgreedy for prototype selection with optimal transport theory. The Python version is available at here.
- ROT4C code for classification with robust optimal transport loss.
- Generating geometry-aware word meta embeddings.
- MBA code for unsupervised crosslingual word alignment.
- RASA code for Riemannian adaptive stochastic algorithm. The python version is available at here.
- GitHub repo on Riemannian stochastic optimization solvers.
- McTorch, a library for manifold constrained deep learning.
- GeoMM to learn multilingual word embeddings.
- Domain adaptation using metric learning on manifolds.
- Side information infused incremental tensor analysis.
- Low-rank geometric mean metric learning.
- A dual framework for tensor completion.
- Structured matrix learning.
- R-SVRG, accelerated stochastic gradients on manifolds.
- Decentralized subspace learning with gossiping.
- Sparse and low-rank index coding with Riemannian optimization.
- Scaled SGD for low-rank matrix completion.
- Symmetry-invariant optimization in deep networks.
- Riemannian preconditioning for tensor completion.
- Sparse plus low-rank autoregressive identification in neuroimaging time series.
- A Riemannian approach to low-rank algebraic Riccati equations.
- R3MC: A Riemannian three-factor algorithm for low-rank matrix completion.
- qGeomMC: A quotient geometric approach to low-rank matrix Completion.
- It also contains a number of computationally efficient Riemannian algorithms for fixed-rank (low-rank) matrix completion for different matrix factorizations.

- Trace norm minimization of the form f(X) + λ || X ||
_{*}, where f is a smooth convex function, || X ||_{*}is the trace (or nuclear) norm, and λ >= 0 is a regularization parameter. - Euclidean distance matrix (EDM) completion.