Resilient Concurrent Causal Discovery for Topological Event Sequences
该研究针对网络事件序列中的因果关系发现难题，提出了一种鲁棒的并发因果发现方法RCCD，通过引入注意力机制和自监督掩码重构框架来提高对缺失数据的鲁棒性和准确性。
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Stochasticity Is Not the Hard Part: Reduction and Complexity in Instructional Sequencing over Prerequisite DAGs
This study addresses the problem of finding the optimal teaching sequence that minimizes learning cost in scenarios with prerequisite dependencies. The problem is modeled as a stochastic shortest path problem, and we propose an exact reduction based on lattice theory that transforms it into a deterministic shortest path problem, revealing that the computational difficulty stems not from stochasticity but from the combinatorial complexity inherent in the dependency structure. We theoretically prove the problem to be NP-hard; however, by leveraging dynamic programming, A* search, and feedback arc set reductions, we identify a “doubly simple” regime in real-world course data where A* efficiently solves instances with state-space size linear in the number of concepts. A computable diagnostic metric, \( m\Delta \), further enables practical assessment of instance hardness.
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Resilient Concurrent Causal Discovery for Topological Event Sequences
该研究针对网络事件序列中的因果关系发现难题，提出了一种鲁棒的并发因果发现方法RCCD，通过引入注意力机制和自监督掩码重构框架来提高对缺失数据的鲁棒性和准确性。
0 citationsRead paper
Stochasticity Is Not the Hard Part: Reduction and Complexity in Instructional Sequencing over Prerequisite DAGs
This study addresses the problem of finding the optimal teaching sequence that minimizes learning cost in scenarios with prerequisite dependencies. The problem is modeled as a stochastic shortest path problem, and we propose an exact reduction based on lattice theory that transforms it into a deterministic shortest path problem, revealing that the computational difficulty stems not from stochasticity but from the combinatorial complexity inherent in the dependency structure. We theoretically prove the problem to be NP-hard; however, by leveraging dynamic programming, A* search, and feedback arc set reductions, we identify a “doubly simple” regime in real-world course data where A* efficiently solves instances with state-space size linear in the number of concepts. A computable diagnostic metric, \( m\Delta \), further enables practical assessment of instance hardness.
0 citationsRead paper