2 citations · 3 across the 15 of their papers we have counts for
6 papers · 1 filter
Co-Supervised Tree Synthesis for Interpretable Subgroup Identification and Honest Treatment Effect Inference in Randomized Trials
Evgenii Kuriabov, Zhaoxue Tong, Jia Li
Identifying patient subgroups with heterogeneous treatment effects is central to precision medicine, yet existing approaches face a tension. Black-box methods estimate individualiz…
Optimized Instance Alteration for Explaining and Assessing Robustness of Classifiers
Evgenii Kuriabov, David Miller, Jia Li
In this work, we propose a unified approach for diagnosing misclassification and assessing the robustness of black-box classifiers. Central to our method is an optimization framewo…
PackMonitor: Enabling Zero Package Hallucinations Through Decoding-Time Monitoring
Xiting Liu, Yuetong Liu, Yitong Zhang +2
As Large Language Models (LLMs) are increasingly integrated into software development workflows, their trustworthiness has become a critical concern. However, in dependency recomme…
Towards Agentic Intelligence for Materials Science
Huan Zhang, Yizhan Li, Wenhao Huang +18
The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in discovery requires moving beyond task-iso…
Scaling the Scaling Logic: Agentic Meta-Synthesis of Logic Reasoning
Bowen Liu, Zhi Wu, Runquan Xie +2
Reinforcement Learning from Verifiable Rewards (RLVR) is bottlenecked by data: existing synthesis pipelines rely on expert-written code or fixed templates, confining growth to inst…
Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendation
Benyu Zhang, Qiang Zhang, Jianpeng Cheng +10
Large Language Models (LLMs) represent a promising frontier for recommender systems, yet their development has been impeded by the absence of predictable scaling laws, which are cr…