2 papers
cs.LG2026
MAdam: Metric-Aware Multi-Objective Adam
Fengbei Liu, Rachit Saluja, Sunwoo Kwak +5
Multi-objective optimization (MOO) underlies many machine learning problems, yet MOO solvers across the loss-balancing, gradient-balancing, and Pareto-based families almost univers…
cs.LG2025
Block-regularized 52 Cross-validated McNemar's Test for Comparing Two Classification Algorithms
Jing Yang, Ruibo Wang, Yijun Song +1
In the task of comparing two classification algorithms, the widely-used McNemar's test aims to infer the presence of a significant difference between the error rates of the two cla…