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AdaMSS: Adaptive Multi-Subspace Approach for Parameter-Efficient Fine-Tuning
In this paper, we propose AdaMSS, an adaptive multi-subspace approach for parameter-efficient fine-tuning of large models. Unlike …
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Differentiable Decision Tree via ``ReLU+Argmin'' Reformulation
Decision tree, despite its unmatched interpretability and lightweight structure, faces two key issues: non-differentiability and low …
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Handling the non-smooth challenge in tensor svd: A multi-objective tensor recovery framework
Recently, numerous tensor singular value decomposition (t-SVD)-based tensor recovery methods have shown promise in processing visual …
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Bayesian-Driven Learning of A New Weighted Tensor Norm for Tensor Recovery
This study addresses the performance limitations of t-SVD-based tensor recovery caused by non-smooth changes and imbalanced …
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A scalable deterministic global optimization algorithm for training optimal decision tree
The training of optimal decision tree via mixed-integer programming (MIP) has attracted much attention in recent literature. However, …
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Global optimal k-medoids clustering of one million samples
We study the deterministic global optimization of the K-Medoids clustering problem. This work proposes a branch and bound (BB) scheme, …
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Global optimization of k-center clustering
k-center problem is a well-known clustering method and can be formulated as a mixed-integer nonlinear programming problem. This work …
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A Scalable deterministic global optimization algorithm for clustering problems
The minimum sum-of-squares clustering (MSSC) task, which can be treated as a Mixed Integer Second Order Cone Programming (MISOCP) …
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