Bayesian Inference

Improving Gibbs Sampler Scan Quality with DoGS ICML 2017 Improving Learning-from-Crowds through Expert Validation IJCAI 2017 Improving Semantic Composition with Offset Inference ACL 2017 Improving Stochastic Block Models by Incorporating Power-Law Degree Characteristic IJCAI 2017 Improving the Expected Improvement Algorithm NeurIPS 2017 Improving Variational Methods via Pairwise Linear Response Identities JMLR 2017 Incorporating Uncertainty into Deep Learning for Spoken Language Assessment ACL 2017 Inferring Human Attention by Learning Latent Intentions IJCAI 2017 Information-theoretic limits of Bayesian network structure learning AISTATS 2017 Introspective Classification with Convolutional Nets NeurIPS 2017 Introspective Evaluation of Perception Performance for Parameter Tuning without Ground Truth RSS 2017 Inverse Filtering for Hidden Markov Models NeurIPS 2017 Joint Discriminative Bayesian Dictionary and Classifier Learning CVPR 2017 Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks NeurIPS 2017 Latent Intention Dialogue Models ICML 2017 Lean Crowdsourcing: Combining Humans and Machines in an Online System CVPR 2017 Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo ICML 2017 Learning in POMDPs with Monte Carlo Tree Search ICML 2017 Learning Kernels over Strings using Gaussian Processes IJCNLP 2017 Learning Optimal Interventions AISTATS 2017 Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding NeurIPS 2017 Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields AISTATS 2017 Learning to Reconstruct 3D Structures for Occupancy Mapping RSS 2017 Learning to select data for transfer learning with Bayesian Optimization EMNLP 2017 Learning Unknown Markov Decision Processes: A Thompson Sampling Approach NeurIPS 2017