1 citations · 1 across the 6 of their papers we have counts for
5 papers · 1 filter
Covariance Learning for Direction-of-Arrival Estimation with Sparse Linear Arrays
Majdoddin Esfandiari, Esa Ollila, Geert Leus +1
Covariance learning (CL) estimates source powers and sensor-noise variance by minimizing the Type-II (marginal) negative log-likelihood of the array measurements. We develop both g…
LLM-Derived Preference Judgments Are Not Self-Consistent
Matthew T. Ford, Francis Bahk, Jingjing Wang +4
Agents increasingly interpret a person's natural-language preferences by querying an LLM for numerical preference judgments, e.g., by asking how much the person would be willing to…
Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application
Tinghan Ye, Shuaicheng Tong, Changkun Guan +2
Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose consequences propagate into future…
Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches
Tinghan Ye, Arnaud Deza, Ved Mohan +2
Optimization models developed by operations research (OR) experts are often deployed as decision-support systems in industrial settings. However, real-world environments are dynami…
HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation
Zewei Deng, Tinghan Ye, Liyan Xie
Agentic text-simulation systems write in sequence, with each item becoming possible context for later steps. That makes uncertainty path-dependent: an early ambiguity can affect la…