Statistical Learning
Multi-Swap k-Means + +
Clustering is a central problem in unsupervised learning.
A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
We design a suite of benchmarks to evaluate the quality of the generated MILP instances.
Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction
This paper presents a unifying framework for heterogeneous FL algorithms with online model extraction and provides a general convergence analysis for the first time.
Calibration by Distribution Matching: Trainable Kernel Calibration Metrics Charles Marx
These metrics admit differentiable sample estimates, making it easy to incorporate a calibration objective into empirical risk minimization.
Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples Shaokui Wei
However, these works face some limitations and challenges.
Undirected Probabilistic Model for Tensor Decomposition
Tensor decompositions (TDs) serve as a powerful tool for analyzing multiway data.
