Bayesian Inference

Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model AAAI 2021 The Base Measure Problem and its Solution AISTATS 2021 The Complexity of Bayesian Network Learning: Revisiting the Superstructure NeurIPS 2021 The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective NeurIPS 2021 The Multiple Instance Learning Gaussian Process Probit Model AISTATS 2021 The neural moving average model for scalable variational inference of state space models UAI 2021 Theoretical bounds on estimation error for meta-learning ICLR 2021 The promises and pitfalls of deep kernel learning UAI 2021 The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks JMLR 2021 Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces ICML 2021 Thompson Sampling for Bandits with Clustered Arms IJCAI 2021 Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients ICML 2021 Time-independent Generalization Bounds for SGLD in Non-convex Settings NeurIPS 2021 T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs NeurIPS 2021 Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data ICLR 2021 Top-k Ranking Bayesian Optimization AAAI 2021 Toward Optimal Solution for the Context-Attentive Bandit Problem IJCAI 2021 Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective NeurIPS 2021 Towards understanding retrosynthesis by energy-based models NeurIPS 2021 Track, Check, Repeat: An EM Approach to Unsupervised Tracking CVPR 2021 Tracking Disease Outbreaks from Sparse Data with Bayesian Inference AAAI 2021 Tractable Approximate Gaussian Inference for Bayesian Neural Networks JMLR 2021 Tractable computation of expected kernels UAI 2021 Tractable Regularization of Probabilistic Circuits NeurIPS 2021 Tractable structured natural-gradient descent using local parameterizations ICML 2021