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Found 12 papers, 1 papers with code
Simultaneous Clustering and Model Selection for Tensor Affinities
Estimating the number of clusters remains a difficult model selection problem.
Online Learning and Optimization Under a New Linear-Threshold Model with Negative Influence
We show that in these models, the expected positive influence spread is a monotone submodular function of the seed set.
Online Learning with Cumulative Oversampling: Application to Budgeted Influence Maximization
We propose a cumulative oversampling (CO) method for online learning.
Revenue Maximization and Learning in Products Ranking
When the conditional purchase probabilities are not known and may depend on consumer and product features, we devise an online learning algorithm that achieves $\tilde{\mathcal{O}}(\sqrt{T})$ regret relative to the approximation algorithm, despite the censoring of information: the attention span of a customer who purchases an item is not observable.
Online Learning of Independent Cascade Models with Node-level Feedback
We propose a detailed analysis of the online-learning problem for Independent Cascade (IC) models under node-level feedback.
Bridging Adversarial and Nonstationary Multi-armed Bandit
In the multi-armed bandit framework, there are two formulations that are commonly employed to handle time-varying reward distributions: adversarial bandit and nonstationary bandit.
Data-Driven Minimax Optimization with Expectation Constraints
We propose a class of efficient primal-dual algorithms to tackle the minimax expectation-constrained problem, and show that our algorithms converge at the optimal rate of $\mathcal{O}(\frac{1}{\sqrt{N}})$.
Information-Directed Selection for Top-Two Algorithms
We consider the best-k-arm identification problem for multi-armed bandits, where the objective is to select the exact set of k arms with the highest mean rewards by sequentially allocating measurement effort.
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks
This paper studies the problem of distributed bilevel optimization over a network where agents can only communicate with neighbors, including examples from multi-task, multi-agent learning and federated learning.
Stochastic Compositional Optimization with Compositional Constraints
Stochastic compositional optimization (SCO) has attracted considerable attention because of its broad applicability to important real-world problems.
Online Linearized LASSO
Sparse regression has been a popular approach to perform variable selection and enhance the prediction accuracy and interpretability of the resulting statistical model.
Federated Multi-Level Optimization over Decentralized Networks
Multi-level optimization has gained increasing attention in recent years, as it provides a powerful framework for solving complex optimization problems that arise in many fields, such as meta-learning, multi-player games, reinforcement learning, and nested composition optimization.
