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Found 18 papers, 3 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.
Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition
By progressively estimating the underlying label distribution and optimizing its alignment with model predictions, we tackle the diverse distribution of unlabeled data in real-world scenarios.
Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization
Existing projection-free algorithms for solving this problem suffer from two limitations: 1) they solely focus on the gradient mapping criterion and fail to match the optimal sample complexities in unconstrained settings; 2) their analysis is exclusively applicable to non-convex functions, without considering convex and strongly convex objectives.
Optimal and Efficient Algorithms for Decentralized Online Convex Optimization
Our analysis reveals that the projection-free variant can achieve ${O}(nT^{3/4})$ and ${O}(nT^{2/3}(\log T)^{1/3})$ regret bounds for convex and strongly convex functions with nearly optimal $\tilde{O}(\rho^{-1/2}\sqrt{T})$ and $\tilde{O}(\rho^{-1/2}T^{1/3}(\log T)^{2/3})$ communication rounds, respectively.
Improved Regret for Bandit Convex Optimization with Delayed Feedback
We investigate bandit convex optimization (BCO) with delayed feedback, where only the loss value of the action is revealed under an arbitrary delay.
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 $O(\sqrt{T(V_T+1)})$ 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.
Mixture of Online and Offline Experts for Non-stationary Time Series
MOOE learns static offline experts from offline intervals and maintains a dynamic online expert for the current online interval.
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.
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.
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 can enjoy 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.
