location: Discovery Building
Backward Feature Correction: How can Deep Learning performs Deep Learning
How does a 110-layer ResNet learn a high-complexity classifier using relatively few training examples and short training time? We present a theory towards explaining this learning process in terms of hierarchical learning. We refer to hierarchical learning as the learner learns to represent a complicated target function by decomposing it into a …
Why some robust estimators are efficiently computable?
Recent advances of computational robust statistics have produced efficient estimators with provable near-optimal statistical guarantees for a variety of problems. These estimators often involve non-convex optimization, and it is not clear why these non-convex problems are efficiently solvable, but many classical non-convex formulations are not. We make an attempt to …
Biologically interpretable machine learning modeling for understanding functional genomics
Robust phenotype-genotype associations have been established for a number of human diseases including brain disorders (e.g., schizophrenia, bipolar disorder). However, understanding the cellular and molecular causes from genotype to phenotype remains elusive. To address this, recent scientific projects have generated large multi-omic datasets — e.g., the PsychENCODE consortium generated ~5,500 genotype, …