Skip to yearly menu bar Skip to main content


XOR-CD: Linearly Convergent Constrained Structure Generation

Fan Ding · Jianzhu Ma · Jinbo Xu · Yexiang Xue

[ ] [ Livestream: Visit Probabilistic Methods 1 ] [ Paper ]
[ Paper ]


We propose XOR-Contrastive Divergence learning (XOR-CD), a provable approach for constrained structure generation, which remains difficult for state-of-the-art neural network and constraint reasoning approaches. XOR-CD harnesses XOR-Sampling to generate samples from the model distribution in CD learning and is guaranteed to generate valid structures. In addition, XOR-CD has a linear convergence rate towards the global maximum of the likelihood function within a vanishing constant in learning exponential family models. Constraint satisfaction enabled by XOR-CD also boosts its learning performance. Our real-world experiments on data-driven experimental design, dispatching route generation, and sequence-based protein homology detection demonstrate the superior performance of XOR-CD compared to baseline approaches in generating valid structures as well as capturing the inductive bias in the training set.

Chat is not available.