Statistical Learning
LibAMM: Empirical Insights into Approximate Computing for Accelerating Matrix Multiplication
To support and inspire ongoing and future research, we introduce LibAMM, a framework within the PyTorch ecosystem.
On the Limitations of Fractal Dimension as a Measure of Generalization Charlie B. Tan University of Oxford Inés García-Redondo Imperial College London Qiquan Wang
Bounding and predicting the generalization gap of overparameterized neural networks remains a central open problem in theoretical machine learning. There is a recent and growing body of literature that proposes the framework of fractals to model optimization trajectories of neural networks, motivating generalization bounds and measures based on the fractal dimension of the trajectory. Notably, the persistent homology dimension has been proposed to correlate with the generalization gap.
Training Energy-Based Normalizing Flow with Score-Matching Objectives Chen-Hao Chao 1, Wei-Fang Sun 1,2, Yen-Chang Hsu
Our experimental results demonstrate that this approach significantly improves the training efficiency as compared to maximum likelihood training.
Parameterized Approximation Schemes for Fair-Range Clustering
It imposes lower and upper bound constraints on the number of facilities opened for each label, ensuring fair representation of all demographic groups by the selected facilities.
Decision Tree for Locally Private Estimation with Public Data Y uheng Ma
On one hand, the knowledge learned from public data is implicitly transferred into the private model.