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Found 16 papers, 1 papers with code
Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity
To deal with complicated constraints via locally light computation in distributed online learning, recent study has presented a projection-free algorithm called distributed online conditional gradient (D-OCG), and achieved an $O(T^{3/4})$ regret bound, where $T$ is the number of prediction rounds.
Improved Regret for Bandit Convex Optimization with Delayed Feedback
Previous studies have established a regret bound of $O(T^{3/4}+d^{1/3}T^{2/3})$ for this problem, where $d$ is the maximum delay, by simply feeding delayed loss values to the classical bandit gradient descent (BGD) algorithm.
Nearly Optimal Regret for Decentralized Online Convex Optimization
Previous studies have established $O(n^{5/4}\rho^{-1/2}\sqrt{T})$ and ${O}(n^{3/2}\rho^{-1}\log T)$ regret bounds for convex and strongly convex functions respectively, where $n$ is the number of local learners, $\rho<1$ is the spectral gap of the communication matrix, and $T$ is the time horizon.
Adversarial Erasing with Pruned Elements: Towards Better Graph Lottery Ticket
Graph Lottery Ticket (GLT), a combination of core subgraph and sparse subnetwork, has been proposed to mitigate the computational cost of deep Graph Neural Networks (GNNs) on large input graphs while preserving original performance.
Improved Projection-free Online Continuous Submodular Maximization
We investigate the problem of online learning with monotone and continuous DR-submodular reward functions, which has received great attention recently.
Non-stationary Online Convex Optimization with Arbitrary Delays
Despite its simplicity, our novel analysis shows that the dynamic regret of DOGD can be automatically bounded by $O(\sqrt{\bar{d}T}(P_T+1))$ under mild assumptions, and $O(\sqrt{dT}(P_T+1))$ in the worst case, where $\bar{d}$ and $d$ denote the average and maximum delay respectively, $T$ is the time horizon, and $P_T$ is the path length of comparators.
Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees
Specifically, we first provide a novel dynamic regret analysis for an existing projection-free method named $\text{BOGD}_\text{IP}$, and establish an $\mathcal{O}(T^{3/4}(1+P_T))$ dynamic regret bound, where $P_T$ denotes the path-length of the comparator sequence.
Improved Dynamic Regret for Online Frank-Wolfe
In this way, we first show that the dynamic regret bound of OFW can be improved to $O(\sqrt{T(1+V_T)})$ for smooth functions.
Projection-free Online Learning with Arbitrary Delays
Projection-free online learning, which eschews the projection operation via less expensive computations such as linear optimization (LO), has received much interest recently due to its efficiency in handling high-dimensional problems with complex constraints.
Coupling Online-Offline Learning for Multi-distributional Data Streams
CO$_2$ extracts knowledge by training an offline expert for each offline interval and update an online expert by an off-the-shelf online optimization method in the online interval.
Online Strongly Convex Optimization with Unknown Delays
Specifically, we first extend the delayed variant of OGD for strongly convex functions, and establish a better regret bound of $O(d\log T)$, where $d$ is the maximum delay.
Online Convex Optimization with Continuous Switching Constraint
To control the switching cost, we introduce the problem of online convex optimization with continuous switching constraint, where the goal is to achieve a small regret given a budget on the \emph{overall} switching cost.
Projection-free Distributed Online Learning with Sublinear Communication Complexity
In this paper, we propose an improved variant of D-OCG, namely D-BOCG, which can attain the same $O(T^{3/4})$ regret bound with only $O(\sqrt{T})$ communication rounds for convex losses, and a better regret bound of $O(T^{2/3}(\log T)^{1/3})$ with fewer $O(T^{1/3}(\log T)^{2/3})$ communication rounds for strongly convex losses.
Projection-free Online Learning over Strongly Convex Sets
In this paper, we study the special case of online learning over strongly convex sets, for which we first prove that OFW enjoys a better regret bound of $O(T^{2/3})$ for general convex losses.
Approximate Multiplication of Sparse Matrices with Limited Space
In this paper, we propose to reduce the time complexity by exploiting the sparsity of the input matrices.
Matrix Completion from Non-Uniformly Sampled Entries
Then, for each partially observed column, we recover it by finding a vector which lies in the recovered column space and consists of the observed entries.
