Probabilistic Modeling

Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions NeurIPS 2021 Improving Dialog Systems for Negotiation with Personality Modeling ACL 2021 Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations WACV 2021 Incorporating External Information in Tissue Subtyping: A Topic Modeling Approach MLHC 2021 Infinite Gaussian Mixture Modeling with an Improved Estimation of the Number of Clusters AAAI 2021 Information Laundering for Model Privacy ICLR 2021 Instance-Optimal Compressed Sensing via Posterior Sampling ICML 2021 Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models NeurIPS 2021 Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty AAAI 2021 Juice: A Julia Package for Logic and Probabilistic Circuits AAAI 2021 Justicia: A Stochastic SAT Approach to Formally Verify Fairness AAAI 2021 Latent variable modeling with random features AISTATS 2021 Learning-based State Reconstruction for a Scalar Hyperbolic PDE under noisy Lagrangian Sensing L4DC 2021 Learning Density Distribution of Reachable States for Autonomous Systems CoRL 2021 Learning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler AAAI 2021 Learning Fast-Inference Bayesian Networks NeurIPS 2021 Learning GPLVM with arbitrary kernels using the unscented transformation AISTATS 2021 Learning Nonparametric Volterra Kernels with Gaussian Processes NeurIPS 2021 Learning partial correlation graphs and graphical models by covariance queries JMLR 2021 Learning the Parameters of Bayesian Networks from Uncertain Data AAAI 2021 Learning to Learn Dense Gaussian Processes for Few-Shot Learning NeurIPS 2021 Learning to Sample from Censored Markov Random Fields COLT 2021 LEO: Learning Energy-based Models in Factor Graph Optimization CoRL 2021 Lifted reasoning meets weighted model integration UAI 2021 Linearly Constrained Gaussian Processes with Boundary Conditions AISTATS 2021