3 citations · 7 across the 8 of their papers we have counts for
8 papers
Scalable Kernel-Based Distances for Statistical Inference and Integration
Masha Naslidnyk
Representing, comparing, and measuring the distance between probability distributions is a key task in computational statistics and machine learning. The choice of representation a…
Kernel Quantile Embeddings and Associated Probability Metrics
Masha Naslidnyk, Siu Lun Chau, François-Xavier Briol +1
Embedding probability distributions into reproducing kernel Hilbert spaces (RKHS) has enabled powerful nonparametric methods such as the maximum mean discrepancy (MMD), a statistic…
Nested Expectations with Kernel Quadrature
Zonghao Chen, Masha Naslidnyk, François-Xavier Briol
This paper considers the challenging computational task of estimating nested expectations. Existing algorithms, such as nested Monte Carlo or multilevel Monte Carlo, are known to b…
Conditional Bayesian Quadrature
Zonghao Chen, Masha Naslidnyk, Arthur Gretton +1
We propose a novel approach for estimating conditional or parametric expectations in the setting where obtaining samples or evaluating integrands is costly. Through the framework o…
Comparing Scale Parameter Estimators for Gaussian Process Interpolation with the Brownian Motion Prior: Leave-One-Out Cross Validation and Maximum Likelihood
Masha Naslidnyk, Motonobu Kanagawa, Toni Karvonen +1
Gaussian process (GP) regression is a Bayesian nonparametric method for regression and interpolation, offering a principled way of quantifying the uncertainties of predicted functi…
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Ayush Bharti, Masha Naslidnyk, Oscar Key +2
Likelihood-free inference methods typically make use of a distance between simulated and real data. A common example is the maximum mean discrepancy (MMD), which has previously bee…
Invariant Priors for Bayesian Quadrature
Masha Naslidnyk, Javier Gonzalez, Maren Mahsereci
Bayesian quadrature (BQ) is a model-based numerical integration method that is able to increase sample efficiency by encoding and leveraging known structure of the integration task…
Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets
Esma Balkir, Masha Naslidnyk, Dave Palfrey +1
Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion. However, they require large batch sizes, which becomes a performance bottlen…