8 accepted papers
Conformal prediction methods provide statistically rigorous marginal coverage guarantees for machine learning models, but such guarantees fail to account for algorithmic biases, thereby undermining fairness and trust. This paper introduces a fair conformal inference framework for classification task…
Large language models (LLMs) have recently demonstrated remarkable progress in formal theorem proving. Yet their ability to serve as practical assistants for mathematicians—filling in missing steps within complex proofs—remains underexplored. We identify this challenge as the task of subgoal complet…
The advent of large language models has significantly advanced automatic code generation, transforming the way programmers writing code. Inspired by natural language processing, mainstream code generation approaches represent code as a linear sequence of tokens. In this paper, we propose to represen…
Satisfiability Modulo Theories (SMT) solvers are crucial in many applications, yet their performance is often a bottleneck. This paper introduces SIRISMT, a novel framework that employs machine learning techniques for the automatic synthesis of efficient SMT-solving strategies. Specifically, SIRISMT
Recent studies have started to explore the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss function. However, existing approaches tend to vacuously satisfy logical constraints through shortcuts, failing to fully exploit the knowledge. In thi…
Neuro-symbolic systems combine the abilities of neural perception and logical reasoning. However, end-to-end learning of neuro-symbolic systems is still an unsolved challenge. This paper proposes a natural framework that fuses neural network training, symbol grounding, and logical constraint synthes…
Neuro-symbolic learning generally consists of two separated worlds, i.e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI. This paper presents a novel, softened symbol grounding process, bridging the gap between the two…
Neural code generation models are nowadays widely adopted to generate code from natural language descriptions automatically. Recently, pre-trained neural models equipped with token-level retrieval capabilities have exhibited great potentials in neural machine translation. However, applying them dir…