SPADA lab at ICML in Vienna

I am excited to be a part of three papers at the International Conference of Machine Learning this July in Vienna.

Congratulations to Can Yaras for having his work on compression in deep low-rank learning, with co-authors Peng Wang and Qing Qu, accepted as an oral presentation for Tuesday afternoon! This work proves that when training deep linear networks, the gradient descent dynamics are limited to an invariant subspace. This subspace can be leveraged to make training and overparameterization more efficient, and allows us to reap the benefits of deep overparameterization without the computational burden. The code is available on Can’s github site. I talked about this work for the 1W-Minds seminar in April.

Peng Wang and Huikang Liu led our work on symmetric matrix completion with ReLU sampling that will be presented as a poster on Wednesday. We showed that it is possible to recover a low-rank matrix with sampling that is highly dependent on the matrix entries — we focus on ReLU sampling (and variants) where only positive entries are observed.

Finally, Wisconsin-Madison PhD student Yuchen Li will be presenting his work on block Riemannian MM methods, also with a poster on Wednesday. He proved iteration guarantees for convergence to a stationary point for general multi-block MM algorithms where any number of blocks may be constrained to a Riemannian manifold. His complexity results reduce to well-known results in the Euclidean case. This work is broadly applicable to alternating MM algorithms for machine learning problems.