Bayesian Inference

Information Bottleneck Approach to Spatial Attention Learning IJCAI 2021 Information Bottleneck Disentanglement for Identity Swapping CVPR 2021 Information Directed Reward Learning for Reinforcement Learning NeurIPS 2021 Information Directed Sampling for Sparse Linear Bandits NeurIPS 2021 Information is Power: Intrinsic Control via Information Capture NeurIPS 2021 Information theoretic meta learning with Gaussian processes UAI 2021 Instance-Optimal Compressed Sensing via Posterior Sampling ICML 2021 Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels ICLR 2021 Investigating Methods to Improve Language Model Integration for Attention-Based Encoder-Decoder ASR Models Interspeech 2021 Isometric Gaussian Process Latent Variable Model for Dissimilarity Data ICML 2021 Is SGD a Bayesian sampler? Well, almost JMLR 2021 Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods NeurIPS 2021 Joint Inference for Neural Network Depth and Dropout Regularization NeurIPS 2021 Kanerva++: Extending the Kanerva Machine With Differentiable, Locally Block Allocated Latent Memory ICLR 2021 Kernel Continual Learning ICML 2021 Kernel Functional Optimisation NeurIPS 2021 Kernel Interpolation for Scalable Online Gaussian Processes AISTATS 2021 Knowledge-Adaptation Priors NeurIPS 2021 Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms JMLR 2021 Langevin Monte Carlo: random coordinate descent and variance reduction JMLR 2021 Laplace Redux - Effortless Bayesian Deep Learning NeurIPS 2021 Large-width functional asymptotics for deep Gaussian neural networks ICLR 2021 Latent Gaussian process with composite likelihoods and numerical quadrature AISTATS 2021 Learnable uncertainty under Laplace approximations UAI 2021 Learning Accurate Dense Correspondences and When To Trust Them CVPR 2021