49 citations · 104 across the 16 of their papers we have counts for
16 papers
Optimal Value Inference for Reinforcement Learning
Nan Lu, Ethan Lee, James M. Robins +2
We study offline inference for the optimal value in reinforcement learning under finite state and action spaces. Two new nuisances are derived as fixed points of a self-induced Bel…
Accelerated Dynamic Importance Weighting with Versatile Divergence-Minimizing Estimators
Tongtong Fang, Nan Lu, Gang Niu +2
Importance weighting (IW) is a golden solver for joint distribution shift, where the joint distributions differ between the training and test data. To solve this problem, IW estima…
TED-TTS: Training-Free Intra-Utterance Emotion and Duration Control for Text-to-Speech Synthesis
Qifan Liang, Yuansen Liu, Ruixin Wei +3
While controllable Text-to-Speech (TTS) has achieved notable progress, most existing methods remain limited to inter-utterance-level control, making fine-grained intra-utterance ex…
Electric field tunable magnetoexcitons in Xenes-hBN-TMDC, Xenes-hBN-BP, and Xenes-hBN-TMTC heterostructures
Roman Ya. Kezerashvili, Anastasia Spiridonova, Klaus Ziegler
In this work, we propose novel van der Waals (vdW) heterostructures composed of Xenes, transition metal dichalcogenides (TMDCs), phosphorene, and transition metal trichalcogenides…
Learning from Ambiguous Data with Hard Labels
Zeke Xie, Zheng He, Nan Lu +5
Real-world data often contains intrinsic ambiguity that the common single-hard-label annotation paradigm ignores. Standard training using ambiguous data with these hard labels may…
Corruptions of Supervised Learning Problems: Typology and Mitigations
Laura Iacovissi, Nan Lu, Robert C. Williamson
Corruption is notoriously widespread in data collection. Despite extensive research, the existing literature predominantly focuses on specific settings and learning scenarios, lack…