7 accepted papers
Higher-order sensor networks are more accurate in characterizing the nonlinear dynamics of sensory time-series data in modern industrial settings by allowing multi-node connections beyond simple pairwise graph edges. In light of this, we propose a deep spatio-temporal hypergraph convolutional neural…
Designing protein sequences with a particular biological function is a long-lasting challenge for protein engineering. Recent advances in machine-learning-guided approaches focus on building a surrogate sequence-function model to reduce the burden of expensive in-lab experiments. In this paper, we s…
Inverse Reinforcement Learning (IRL) is a powerful way of learning from demonstrations. In this paper, we address IRL problems with the availability of prior knowledge that optimal policies will never violate certain constraints. Conventional approaches ignoring these constraints need many dem…
Protein sequence alignment is a fundamental problem in computational structure biology and popular for protein 3D structural prediction and protein homology detection. Most of the developed programs for detecting protein sequence alignments are based upon the likelihood information of amino acids an…
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 lear…
Many decision-making problems under uncertainty can be formulated as convex stochastic optimization, which minimizes a convex objective in expectation across exponentially many probabilistic scenarios. Despite its convexity, evaluating the objective function is #P-hard. Previous approaches use sampl…
Gradient-based approximate inference methods, such as Stein variational gradient descent (SVGD) \cite{liu2016stein}, provide simple and general-purpose inference engines for differentiable continuous distributions. However, existing forms of SVGD can not be directly applied to discrete distributions…