Probability

On Testing of Samplers NeurIPS 2020 On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method NeurIPS 2020 On the Relationship Between Probabilistic Circuits and Determinantal Point Processes UAI 2020 Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets NeurIPS 2020 Privately Learning Markov Random Fields ICML 2020 Residual Neural Processes AAAI 2020 Risk Bounds for Reservoir Computing JMLR 2020 Simulator Calibration under Covariate Shift with Kernels AISTATS 2020 Spatial-Temporal Gaussian Scale Mixture Modeling for Foreground Estimation AAAI 2020 Stable behaviour of infinitely wide deep neural networks AISTATS 2020 Stable Reinforcement Learning with Unbounded State Space L4DC 2020 System Identification with Time-Aware Neural Sequence Models AAAI 2020 TellTail: Fast Scoring and Detection of Dense Subgraphs AAAI 2020 The Discrete Gaussian for Differential Privacy NeurIPS 2020 True Nonlinear Dynamics from Incomplete Networks AAAI 2020 Variational Inference for Sparse Gaussian Process Modulated Hawkes Process AAAI 2020 Wasserstein Control of Mirror Langevin Monte Carlo COLT 2020 Why Are Learned Indexes So Effective? ICML 2020 $β^3$-IRT: A New Item Response Model and its Applications AISTATS 2019 A Unified Mathematical Approach for Foraging and Construction Systems in a 1,000,000 Robot Swarm IJCAI 2019 Band-Limited Gaussian Processes: The Sinc Kernel NeurIPS 2019 CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling AAAI 2019 Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory NeurIPS 2019 Concentration of risk measures: A Wasserstein distance approach NeurIPS 2019 Continuous-time Models for Stochastic Optimization Algorithms NeurIPS 2019