4 papers
Approximating Pareto Frontiers in Stochastic Multi-Objective Optimization via Hashing and Randomization
Jinzhao Li, Nan Jiang, Yexiang Xue
Stochastic Multi-Objective Optimization (SMOO) is critical for decision-making trading off multiple potentially conflicting objectives in uncertain environments. SMOO aims at ident…
Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits
Jinzhao Li, Nan Jiang, Yexiang Xue
Satisfiability Modulo Counting (SMC) is a recently proposed general language to reason about problems integrating statistical and symbolic Artificial Intelligence. An SMC problem i…
A Tighter Convergence Proof of Reverse Experience Replay
Nan Jiang, Jinzhao Li, Yexiang Xue
In reinforcement learning, Reverse Experience Replay (RER) is a recently proposed algorithm that attains better sample complexity than the classic experience replay method. RER req…
Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration With Provable Guarantees
Jinzhao Li, Nan Jiang, Yexiang Xue
Satisfiability Modulo Counting (SMC) encompasses problems that require both symbolic decision-making and statistical reasoning. Its general formulation captures many real-world pro…