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Found 9 papers, 1 papers with code
Risk-averse Learning with Non-Stationary Distributions
Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time.
Neural optimal controller for stochastic systems via pathwise HJB operator
The aim of this work is to develop deep learning-based algorithms for high-dimensional stochastic control problems based on physics-informed learning and dynamic programming.
Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater's Condition
Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to $\mathcal{O}(\log(T))$, and the network cumulative constraint violation bound is reduced to $\mathcal{O}(\sqrt{\log(T)T})$ and $\mathcal{O}(\log(T))$ without and with Slater's condition, respectively.
DeceFL: A Principled Decentralized Federated Learning Framework
Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication pressure and high vulnerability when there exists a failure at or attack on the central client.
Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints
A novel algorithm is first proposed and it achieves an $\mathcal{O}(T^{\max\{c, 1-c\}})$ bound for static regret and an $\mathcal{O}(T^{(1-c)/2})$ bound for cumulative constraint violation, where $c\in(0, 1)$ is a user-defined trade-off parameter, and thus has improved performance compared with existing results.
Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization
This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions.
A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization
The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of $n$ local cost functions by using local information exchange is considered.
Optimization and Control
Distributed Online Convex Optimization with Time-Varying Coupled Inequality Constraints
Assuming Slater's condition, we show that the algorithm achieves smaller bounds on the constraint violation.
Stability of Analytic Neural Networks with Event-triggered Synaptic Feedbacks
In this paper, we investigate stability of a class of analytic neural networks with the synaptic feedback via event-triggered rules.
