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Found 5 papers, 2 papers with code
Optimal Rates for Robust Stochastic Convex Optimization
Machine learning algorithms in high-dimensional settings are highly susceptible to the influence of even a small fraction of structured outliers, making robust optimization techniques essential.
A Mathematical Framework, a Taxonomy of Modeling Paradigms, and a Suite of Learning Techniques for Neural-Symbolic Systems
There is a pressing need for a unifying theory to illuminate the commonalities and differences in approaches and enable further progress.
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses
In this context, every silo (e. g. hospital) has data from several people (e. g. patients) and needs to protect the privacy of each person's data (e. g. health records), even if the server and/or other silos try to uncover this data.
Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning
We leverage convex and bilevel optimization techniques to develop a general gradient-based parameter learning framework for neural-symbolic (NeSy) systems.
Differentially Private Optimization for Smooth Nonconvex ERM
We develop simple differentially private optimization algorithms that move along directions of (expected) descent to find an approximate second-order solution for nonconvex ERM.
