conftrace_

nonparametric inference

30 papers

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Co-occurring keywords

gaussian process (1200) bayesian inference (1907) kernel methods (1097) reproducing kernel hilbert space (334) markov chain monte carlo (725) variational inference (2069) kernel mean embedding (43) graphical model (864) state-space model (153) dynamical system (398)

Papers

Decorrelated Variable Importance JMLR 2024
Nonparametric Automatic Differentiation Variational Inference with Spline Approximation AISTATS 2024
Nonparametric Inference under B-bits Quantization JMLR 2024
Hypothesis Testing for Class-Conditional Noise Using Local Maximum Likelihood AAAI 2024
Nonparametric Extensions of Randomized Response for Private Confidence Sets ICML 2023
Doubly Robust Counterfactual Classification NIPS 2022
Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach JMLR 2022
Nonparametric Embeddings of Sparse High-Order Interaction Events ICML 2022
Nonparametric estimation of continuous DPPs with kernel methods NIPS 2021
Nonparametric Hamiltonian Monte Carlo ICML 2021
A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning ICML 2020
Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables JMLR 2020
Bayesian Deconditional Kernel Mean Embeddings ICML 2019
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation NIPS 2019
Sharp Theoretical Analysis for Nonparametric Testing under Random Projection COLT 2019
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes NIPS 2019
Adaptive Sensor Placement for Continuous Spaces ICML 2019
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes ICML 2015
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes ICML 2015
Nonparametric Bayesian inference on multivariate exponential families NIPS 2014
Recovering Distributions from Gaussian RKHS Embeddings AISTATS 2014
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC NIPS 2013
Approximate Gaussian process inference for the drift function in stochastic differential equations NIPS 2013
Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels JMLR 2013
Gaussian Processes for time-marked time-series data AISTATS 2012