← Back to all news
arXiv Machine Learning By Jiajie Chen, Jinfeng Li

The Note-Chord-Voice Framework: Structured Source Separation and Causal Inference for EV Charging Data

Read the original on arXiv Machine Learning →

arXiv:2608. 14756v1 Announce Type: cross Abstract: Real-world EV charging data exhibit three interlocking pathologies: hardware fragmentation (network timeouts and billing resets split sessions), physical violations (independent energy/duration models produce impossible states like 50 kWh in 10 min on a 7 kW charger), and collider bias (clustering on post-treatment outcomes opens backdoor paths for price elasticity).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Jul 31

DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series

arXiv:2607. 27263v1 Announce Type: new Abstract: Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation under-served exactly where it matters most, such as in healthcare, policy evaluation, and climate science.

By Dennis Thumm, Billy Tim Anthony, Ying Chen
benchmarks
More like this →
arXiv AI
6d ago

Local verification cannot detect non-transportability: a cohomological theory of context preservation in agentic reasoning

arXiv:2608. 11252v1 Announce Type: new Abstract: Agentic AI systems routinely transport conclusions across biological, clinical and financial contexts, and the emerging safeguard is local verification: checking at each step that the entity is representable in the chosen tool, that parameters are compatible, and that outputs cohere with the plan.

By Suyash Mishra
agents
More like this →