Bayesian & Probabilistic

RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs ICLR 2021 RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting ICML 2021 Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization CVPR 2021 Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time ICLR 2021 Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models AAAI 2021 Scalable Bayesian GPFA with automatic relevance determination and discrete noise models NeurIPS 2021 Scalable Bayesian Inverse Reinforcement Learning ICLR 2021 Scalable gradient matching based on state space Gaussian Processes ACML 2021 Scalable Inference of Sparsely-changing Gaussian Markov Random Fields NeurIPS 2021 Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes ICLR 2021 Scalable Quasi-Bayesian Inference for Instrumental Variable Regression NeurIPS 2021 Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition ICML 2021 Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets NeurIPS 2021 Self-Adaptable Point Processes with Nonparametric Time Decays NeurIPS 2021 Self-Supervised Self-Supervision by Combining Deep Learning and Probabilistic Logic AAAI 2021 Sequential core-set Monte Carlo UAI 2021 Set Prediction without Imposing Structure as Conditional Density Estimation ICLR 2021 Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition ICCV 2021 SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data ICML 2021 Signaling in Bayesian Network Congestion Games: the Subtle Power of Symmetry AAAI 2021 Significance of Gradient Information in Bayesian Optimization AISTATS 2021 Similarity measure for sparse time course data based on Gaussian processes UAI 2021 Sinusoidal Flow: A Fast Invertible Autoregressive Flow ACML 2021 SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes ICML 2021 Sliced Kernelized Stein Discrepancy ICLR 2021