2 research outputs found
Cooperative Thresholded Lasso for Sparse Linear Bandit
We present a novel approach to address the multi-agent sparse contextual
linear bandit problem, in which the feature vectors have a high dimension
whereas the reward function depends on only a limited set of features -
precisely . Furthermore, the learning follows under
information-sharing constraints. The proposed method employs Lasso regression
for dimension reduction, allowing each agent to independently estimate an
approximate set of main dimensions and share that information with others
depending on the network's structure. The information is then aggregated
through a specific process and shared with all agents. Each agent then resolves
the problem with ridge regression focusing solely on the extracted dimensions.
We represent algorithms for both a star-shaped network and a peer-to-peer
network. The approaches effectively reduce communication costs while ensuring
minimal cumulative regret per agent. Theoretically, we show that our proposed
methods have a regret bound of order
with high probability, where is the time horizon. To our best knowledge, it
is the first algorithm that tackles row-wise distributed data in sparse linear
bandits, achieving comparable performance compared to the state-of-the-art
single and multi-agent methods. Besides, it is widely applicable to
high-dimensional multi-agent problems where efficient feature extraction is
critical for minimizing regret. To validate the effectiveness of our approach,
we present experimental results on both synthetic and real-world datasets
Recommended from our members
Blockade of vascular endothelial growth factor receptors by tivozanib has potential anti-tumour effects on human glioblastoma cells
Glioblastoma (GBM) remains one of the most fatal human malignancies due to its high angiogenic and infiltrative capacities. Even with optimal therapy including surgery, radiotherapy and temozolomide, it is essentially incurable. GBM is among the most neovascularised neoplasms and its malignant progression associates with striking neovascularisation, evidenced by vasoproliferation and endothelial cell hyperplasia. Targeting the pro-angiogenic pathways is therefore a promising anti-glioma strategy. Here we show that tivozanib, a pan-inhibitor of vascular endothelial growth factor (VEGF) receptors, inhibited proliferation of GBM cells through a G2/M cell cycle arrest via inhibition of polo-like kinase 1 (PLK1) signalling pathway and down-modulation of Aurora kinases A and B, cyclin B1 and CDC25C. Moreover, tivozanib decreased adhesive potential of these cells through reduction of intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1). Tivozanib diminished GBM cell invasion through impairing the proteolytic cascade of cathepsin B/urokinase-type plasminogen activator (uPA)/matrix metalloproteinase-2 (MMP-2). Combination of tivozanib with EGFR small molecule inhibitor gefitinib synergistically increased sensitivity to gefitinib. Altogether, these findings suggest that VEGFR blockade by tivozanib has potential anti-glioma effects in vitro. Further in vivo studies are warranted to explore the anti-tumour activity of tivozanib in combinatorial approaches in GBM