2 citations · 3 across the 8 of their papers we have counts for
8 papers
Bandit Submodular Maximization under Matroid Constraints: Learning Compressed Exchange Policy
Zongqi Wan, Zhijie Zhang
We study adversarial bandit maximization of monotone submodular functions under a matroid constraint. For a rank- matroid on elements, we give a randomized oracle-polynomial…
Approximating the Trace Distance Between Product Quantum States
Kun He, Dimitrios Myrisiotis, Junhong Nie +1
We study the trace distance \[D_{\mathrm{tr}}(ρ,σ) =\frac12\|ρ-σ\|_1, ρ=\bigotimes_{i=1}^nρ_i,\quad σ=\bigotimes_{i=1}^nσ_i, \] when the two exponentially large states are specifie…
Efficient Deterministic Algorithms for Maximizing Symmetric Submodular Functions
Zongqi Wan, Jialin Zhang, Xiaoming Sun +1
Symmetric submodular maximization is an important class of combinatorial optimization problems, including MAX-CUT on graphs and hyper-graphs. The state-of-the-art algorithm for the…
Boosting Gradient Ascent for Continuous DR-submodular Maximization
Qixin Zhang, Zongqi Wan, Zengde Deng +4
Projected Gradient Ascent (PGA) is the most commonly used optimization scheme in machine learning and operations research areas. Nevertheless, numerous studies and examples have sh…
Competitive Auctions with Imperfect Predictions
Pinyan Lu, Zongqi Wan, Jialin Zhang
The competitive auction was first proposed by Goldberg, Hartline, and Wright. In their paper, they introduce the competitive analysis framework of online algorithm designing into t…
Bandit Multi-linear DR-Submodular Maximization and Its Applications on Adversarial Submodular Bandits
Zongqi Wan, Jialin Zhang, Wei Chen +2
We investigate the online bandit learning of the monotone multi-linear DR-submodular functions, designing the algorithm that attains of $(1…
Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets
Zongqi Wan, Zhijie Zhang, Tongyang Li +2
Multi-arm bandit (MAB) and stochastic linear bandit (SLB) are important models in reinforcement learning, and it is well-known that classical algorithms for bandits with time horiz…
Bounded Memory Adversarial Bandits with Composite Anonymous Delayed Feedback
Zongqi Wan, Xiaoming Sun, Jialin Zhang
We study the adversarial bandit problem with composite anonymous delayed feedback. In this setting, losses of an action are split into components, spreading over consecutive ro…