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pro vyhledávání: '"Tan, Vincent"'
Semi-supervised learning (SSL), exemplified by FixMatch (Sohn et al., 2020), has shown significant generalization advantages over supervised learning (SL), particularly in the context of deep neural networks (DNNs). However, it is still unclear, from
Externí odkaz:
http://arxiv.org/abs/2410.11206
Autor:
Gao, Yihang, Tan, Vincent Y. F.
Kolmogorov--Arnold Networks (KANs), a recently proposed neural network architecture, have gained significant attention in the deep learning community, due to their potential as a viable alternative to multi-layer perceptrons (MLPs) and their broad ap
Externí odkaz:
http://arxiv.org/abs/2410.08041
We propose a {\em novel} piecewise stationary linear bandit (PSLB) model, where the environment randomly samples a context from an unknown probability distribution at each changepoint, and the quality of an arm is measured by its return averaged over
Externí odkaz:
http://arxiv.org/abs/2410.07638
We study EgalMAB, an egalitarian assignment problem in the context of stochastic multi-armed bandits. In EgalMAB, an agent is tasked with assigning a set of users to arms. At each time step, the agent must assign exactly one arm to each user such tha
Externí odkaz:
http://arxiv.org/abs/2410.05856
Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret. This innovative variant of the multi-armed bandit problem elegantly amalgamates two of i
Externí odkaz:
http://arxiv.org/abs/2409.18909
We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a $K$-armed bandit model where some subset of $K$ arms is partitioned into $M$ groups. Within each group, the random variable associate
Externí odkaz:
http://arxiv.org/abs/2409.05072
In this work, we study the robust phase retrieval problem where the task is to recover an unknown signal $\theta^* \in \mathbb{R}^d$ in the presence of potentially arbitrarily corrupted magnitude-only linear measurements. We propose an alternating mi
Externí odkaz:
http://arxiv.org/abs/2409.04733
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