Mathias Niepert

Learning and inference in tractable probabilistic knowledge bases

2015 · Uncertainty in Artificial Intelligence

Building efficient large-scale knowledge bases (KBs) is a longstanding goal of AI. KBs need to be first-order to be sufficiently expressive, and probabilistic to handle uncertainty, but these lead to intractable inference. Recently, tractable Markov …

Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions

2021 · arXiv (Cornell University)

Combining discrete probability distributions and combinatorial optimization problems with neural network components has numerous applications but poses several challenges. We propose Implicit Maximum Likelihood Estimation (I-MLE), a framework for end-to-end learning of models combining discrete …

RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems

2019 · IEEE Transactions on Neural Networks and Learning Systems

Data sparsity and data imbalance are practical and challenging issues in cross-domain recommender systems (RSs). This paper addresses those problems by leveraging the concepts which derive from representation learning, adversarial learning, and transfer learning (particularly, …