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Found 7 papers, 0 papers with code
Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problems
We prove that for convex functions, D-Safe-OGD achieves a dynamic regret bound of $O(T^{2/3} \sqrt{\log T} + T^{1/3}C_T^*)$, where $C_T^*$ denotes the path-length of the best minimizer sequence.
Distributed Online System Identification for LTI Systems Using Reverse Experience Replay
Inspired by this work, we study distributed online system identification of LTI systems over a multi-agent network.
Regret Analysis of Distributed Online LQR Control for Unknown LTI Systems
Consider a multi-agent network where each agent is modeled as a LTI system.
Distributed Online Linear Quadratic Control for Linear Time-invariant Systems
Recent advancement in online optimization and control has provided novel tools to study LQ problems that are robust to time-varying cost parameters.
Unconstrained Online Optimization: Dynamic Regret Analysis of Strongly Convex and Smooth Problems
The regret bound of dynamic online learning algorithms is often expressed in terms of the variation in the function sequence ($V_T$) and/or the path-length of the minimizer sequence after $T$ rounds.
RFN: A Random-Feature Based Newton Method for Empirical Risk Minimization in Reproducing Kernel Hilbert Spaces
Large-scale finite-sum problems can be solved using efficient variants of Newton method, where the Hessian is approximated via sub-samples of data.
Efficient Two-Step Adversarial Defense for Deep Neural Networks
However, the computational cost of the adversarial training with PGD and other multi-step adversarial examples is much higher than that of the adversarial training with other simpler attack techniques.
