26 citations · 33 across the 8 of their papers we have counts for
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cs.LG2026
Enhanced Byzantine-Robust Federated Learning Via Truncated-Quadratic Loss for Heterogeneous Data
Zhi-Yong Wang, Hao Nan Sheng, Werner Stefan +3
Federated learning distributes data among clients, making it vulnerable to malicious attacks and data heterogeneity, which together pose challenges for robust learning. To tack…
cs.AI2026
Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL
Zerui Yang, Weichuan Wang, Yanwei Xu +4
Existing NL2SQL systems face two critical limitations: (1) they rely on in-context learning with only correct examples, overlooking the rich signal in historical error-fix pairs th…