1 citations · 1 across the 1 of their papers we have counts for
5 papers
Learning in the Recurrent State: Gradient Descent with Linear Recurrent Networks
Yudou Tian, Neeraj Mohan Sushma, Harshvardhan Mestha +3
Linear recurrent networks (LRNNs) offer linear-time sequence modeling, but standard recurrent updates do not directly expose the supervised products needed for in-context gradient…
Residual Reweighted Conformal Prediction for Graph Neural Networks
Zheng Zhang, Jie Bao, Zhixin Zhou +3
Graph Neural Networks (GNNs) excel at modeling relational data but face significant challenges in high-stakes domains due to unquantified uncertainty. Conformal prediction (CP) off…
Assumption-free fidelity bounds for hardware noise characterization
Nicolo Colombo
In the Quantum Supremacy regime, quantum computers may overcome classical machines on several tasks if we can estimate, mitigate, or correct unavoidable hardware noise. Estimating…
Enhanced Route Planning with Calibrated Uncertainty Set
Lingxuan Tang, Rui Luo, Zhixin Zhou +1
This paper investigates the application of probabilistic prediction methodologies in route planning within a road network context. Specifically, we introduce the Conformalized Quan…
Structured Learning of Compositional Sequential Interventions
Jialin Yu, Andreas Koukorinis, Nicolò Colombo +2
We consider sequential treatment regimes where each unit is exposed to combinations of interventions over time. When interventions are described by qualitative labels, such as "clo…