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Jinzhao Li

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ICML Conference 2025 Conference Paper

Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits

  • Jinzhao Li
  • Nan Jiang 0012
  • 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 is an extended SAT problem in which the truth values of a few Boolean variables are determined by probabilistic inference. Approximate solvers may return solutions that violate constraints. Directly integrating available SAT solvers and probabilistic inference solvers gives exact solutions but results in slow performance because of many back-and-forth invocations of both solvers. We propose KOCO-SMC, an integrated exact SMC solver that efficiently tracks lower and upper bounds in the probabilistic inference process. It enhances computational efficiency by enabling early estimation of probabilistic inference using only partial variable assignments, whereas existing methods require full variable assignments. In the experiment, we compare KOCO-SMC with currently available approximate and exact SMC solvers on large-scale datasets and real-world applications. The proposed KOCO-SMC finds exact solutions with much less time.

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RLJ Journal 2024 Journal Article

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 requires the learning algorithm to update the parameters through consecutive state-action-reward tuples in reverse order. However, the most recent theoretical analysis only holds for a minimal learning rate and short consecutive steps, which converge slower than those large learning rate algorithms without RER. In view of this theoretical and empirical gap, we provide a tighter analysis that mitigate the limitation on the learning rate and the length of consecutive steps. Furthermore, we show theoretically that RER converges with a larger learning rate and a longer sequence.

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RLC Conference 2024 Conference Paper

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 requires the learning algorithm to update the parameters through consecutive state-action-reward tuples in reverse order. However, the most recent theoretical analysis only holds for a minimal learning rate and short consecutive steps, which converge slower than those large learning rate algorithms without RER. In view of this theoretical and empirical gap, we provide a tighter analysis that mitigate the limitation on the learning rate and the length of consecutive steps. Furthermore, we show theoretically that RER converges with a larger learning rate and a longer sequence.

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AAAI Conference 2024 Conference Paper

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 problems at the intersection of symbolic and statistical AI. SMC searches for policy interventions to control probabilistic outcomes. Solving SMC is challenging because of its highly intractable nature (NP^PP-complete), incorporating statistical inference and symbolic reasoning. Previous research on SMC solving lacks provable guarantees and/or suffers from suboptimal empirical performance, especially when combinatorial constraints are present. We propose XOR-SMC, a polynomial algorithm with access to NP-oracles, to solve highly intractable SMC problems with constant approximation guarantees. XOR-SMC transforms the highly intractable SMC into satisfiability problems by replacing the model counting in SMC with SAT formulae subject to randomized XOR constraints. Experiments on solving important SMC problems in AI for social good demonstrate that XOR-SMC outperforms several baselines both in solution quality and running time.

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AAMAS Conference 2023 Conference Paper

Provable Optimization of Quantal Response Leader-Follower Games with Exponentially Large Action Spaces

  • Jinzhao Li
  • Daniel Fink
  • Christopher Wood
  • Carla P. Gomes
  • Yexiang Xue

Leader-follower games involve a leader committing strategies before her followers. We consider quantal response leader-follower games, where the followers’ response is probabilistic due to their bounded rationality. Moreover, both the leader’s and followers’ action spaces are exponentially large with respect to the problem size, hence rendering the overall complexity to solve these games beyond NP-complete. We propose the XOR-Game algorithm, which converges in linear speed towards the equilibrium of convex quantal response leader-follower games (#P-hard to find the equilibrium even though convex). XOR-Game combines stochastic gradient descent with XOR-sampling, a provable sampling approach which transforms highly intractable probabilistic inference into queries to NP oracles. We tested XOR-Game on zero-sum and distribution matching leader-follower games. Experiments show XOR-Game converges faster to a good leader’s strategy compared to several baselines. In particular, XOR-Game helps to find the optimal reward allocations for the Avicaching game in the citizen science domain, which harnesses rewards to motivate bird watchers towards tasks of high scientific value.

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