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Found 8 papers, 0 papers with code
Extragradient Type Methods for Riemannian Variational Inequality Problems
In the context of Euclidean space, it is established that the last-iterates of both the extragradient (EG) and past extragradient (PEG) methods converge to the solution of monotone variational inequality problems at a rate of $O\left(\frac{1}{\sqrt{T}}\right)$ (Cai et al., 2022).
Randomized Quantization is All You Need for Differential Privacy in Federated Learning
To the best of our knowledge, this is the first study that solely relies on randomized quantization without incorporating explicit discrete noise to achieve Renyi DP guarantees in Federated Learning systems.
On Riemannian Projection-free Online Learning
The projection operation is a critical component in a wide range of optimization algorithms, such as online gradient descent (OGD), for enforcing constraints and achieving optimal regret bounds.
Faster Margin Maximization Rates for Generic and Adversarially Robust Optimization Methods
To address this limitation, we present a series of state-of-the-art implicit bias rates for mirror descent and steepest descent algorithms.
Minimizing Dynamic Regret on Geodesic Metric Spaces
In this paper, we consider the sequential decision problem where the goal is to minimize the general dynamic regret on a complete Riemannian manifold.
Adaptive Oracle-Efficient Online Learning
The classical algorithms for online learning and decision-making have the benefit of achieving the optimal performance guarantees, but suffer from computational complexity limitations when implemented at scale.
Supervised Hashing based on Energy Minimization
Recently, supervised hashing methods have attracted much attention since they can optimize retrieval speed and storage cost while preserving semantic information.
Bayesian Supervised Hashing
To address this problem, we present a novel fully Bayesian treatment for supervised hashing problem, named Bayesian Supervised Hashing (BSH), in which hyperparameters are automatically tuned during optimization.
