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pro vyhledávání: '"Tan, Vincent"'
We study a robust online convex optimization framework, where an adversary can introduce outliers by corrupting loss functions in an arbitrary number of rounds k, unknown to the learner. Our focus is on a novel setting allowing unbounded domains and
Externí odkaz:
http://arxiv.org/abs/2408.06297
Autor:
Wang, Shuche, Tan, Vincent Y. F.
Distributed gradient descent algorithms have come to the fore in modern machine learning, especially in parallelizing the handling of large datasets that are distributed across several workers. However, scant attention has been paid to analyzing the
Externí odkaz:
http://arxiv.org/abs/2407.14111
We consider a ubiquitous scenario in the study of Influence Maximization (IM), in which there is limited knowledge about the topology of the diffusion network. We set the IM problem in a multi-round diffusion campaign, aiming to maximize the number o
Externí odkaz:
http://arxiv.org/abs/2406.12835
Autor:
Chen, Zhirui, Tan, Vincent Y. F.
We consider offline reinforcement learning (RL) with preference feedback in which the implicit reward is a linear function of an unknown parameter. Given an offline dataset, our objective consists in ascertaining the optimal action for each state, wi
Externí odkaz:
http://arxiv.org/abs/2406.12205
Electromagnetic information theory (EIT) is an interdisciplinary subject that serves to integrate deterministic electromagnetic theory with stochastic Shannon's information theory. Existing EIT analysis operates in the continuous space domain, which
Externí odkaz:
http://arxiv.org/abs/2406.04881
Autor:
Bian, Jie, Tan, Vincent Y. F.
The Indexed Minimum Empirical Divergence (IMED) algorithm is a highly effective approach that offers a stronger theoretical guarantee of the asymptotic optimality compared to the Kullback--Leibler Upper Confidence Bound (KL-UCB) algorithm for the mul
Externí odkaz:
http://arxiv.org/abs/2405.15200
Accuracy and speed are critical in image editing tasks. Pan et al. introduced a drag-based image editing framework that achieves pixel-level control using Generative Adversarial Networks (GANs). A flurry of subsequent studies enhanced this framework'
Externí odkaz:
http://arxiv.org/abs/2405.13722
Autor:
Dong, Yanyan, Tan, Vincent Y. F.
We study the problem of adversarial combinatorial bandit with a switching cost $\lambda$ for a switch of each selected arm in each round, considering both the bandit feedback and semi-bandit feedback settings. In the oblivious adversarial case with $
Externí odkaz:
http://arxiv.org/abs/2404.01883
We introduce a novel extension of the canonical multi-armed bandit problem that incorporates an additional strategic element: abstention. In this enhanced framework, the agent is not only tasked with selecting an arm at each time step, but also has t
Externí odkaz:
http://arxiv.org/abs/2402.15127
Autor:
Yavas, Recep Can, Tan, Vincent Y. F.
We study variable-length feedback (VLF) codes with noiseless feedback for discrete memoryless channels. We present a novel non-asymptotic bound, which analyzes the average error probability and average decoding time of our modified Yamamoto--Itoh sch
Externí odkaz:
http://arxiv.org/abs/2401.16726