13 citations · 52 across the 19 of their papers we have counts for
7 papers · 1 filter
Benchmarking and Enhancing Agentic Causal Discovery
Pingchuan Ma, Rui Ding, Shuai Wang
LLM agents can now run causal discovery algorithms on a dataset and report what comes out. Whether they do more than that, such as noticing where an algorithm went wrong and fixing…
Demistifying Data and Simulator Assumptions in Supervised Causal Discovery
Pingchuan Ma, Rui Ding, Bojun Huang +1
Supervised causal discovery learns to infer causal structure for a new dataset from training datasets paired with structural labels. These training pairs are typically simulated, m…
Sound Debloating of Redundant Checks in Zero-Knowledge Machine-Learning Circuits
Zhantong Xue, Pingchuan Ma, Zhaoyu Wang +3
Zero-knowledge (ZK) proof systems for neural-network inference compile the model into a system of arithmetic constraints. Many of these constraints are redundant checks: range proo…
Instruction Alignment for Binary Code Representation Learning
Huaijin Wang, Shuai Wang
Binary code representation learning is a fundamental problem in software security and reverse engineering. Existing methods mainly learn function-level embeddings that capture coar…
KQFuzz: Knowledge-Guided Fuzzing for Quantum Libraries via Large Language Models
Fuyuan Xia, Qixin Zhang, Chenhao Ying +5
As quantum computing continually improves, ensuring the reliability and correctness of quantum libraries has become increasingly critical. To this end, many LLM-based fuzzing appro…
ZK-Value: A Practical Zero-Knowledge System for Verifiable Data Valuation
Zhaoyu Wang, Pingchuan Ma, Zhantong Xue +4
Data valuation is a foundational task in data marketplaces, where a Shapley-value attribution determines how a buyer's payment is distributed among data providers. Typically, the m…
Efficient Differentiable Causal Discovery via Reliable Super-Structure Learning
Pingchuan Ma, Qixin Zhang, Shuai Wang +1
Recently, differentiable causal discovery has emerged as a promising approach to improve the accuracy and efficiency of existing methods. However, when applied to high-dimensional…