Search Results for author: Y. X. Rachel Wang

Found 7 papers, 2 papers with code

On hyperparameter tuning in general clustering problemsm

no code implementations ICML 2020 Xinjie Fan, Yuguang Yue, Purnamrita Sarkar, Y. X. Rachel Wang

Tuning hyperparameters for unsupervised learning problems is difficult in general due to the lack of ground truth for validation.

Clustering Community Detection +1

Adaptive conformal classification with noisy labels

1 code implementation10 Sep 2023 Matteo Sesia, Y. X. Rachel Wang, Xin Tong

This paper develops novel conformal prediction methods for classification tasks that can automatically adapt to random label contamination in the calibration sample, leading to more informative prediction sets with stronger coverage guarantees compared to state-of-the-art approaches.

Classification Conformal Prediction

Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data

no code implementations1 Oct 2022 Lijia Wang, Y. X. Rachel Wang, Jingyi Jessica Li, Xin Tong

Here, we propose a hierarchical NP (H-NP) framework and an umbrella algorithm that generally adapts to popular classification methods and controls the under-classification errors with high probability.

Binary Classification Classification +1

A Unified Framework for Tuning Hyperparameters in Clustering Problems

no code implementations17 Oct 2019 Xinjie Fan, Yuguang Yue, Purnamrita Sarkar, Y. X. Rachel Wang

In this paper, we provide a framework with provable guarantees for selecting hyperparameters in a number of distinct models.

Clustering Community Detection +1

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal

no code implementations8 Aug 2019 Tung-Yu Wu, Y. X. Rachel Wang, Wing H. Wong

To further extend the utility of the algorithm to high dimensional settings, we construct a proposal with forward and reverse moves using stochastic gradient and show that the construction leads to reasonable acceptance probabilities.

Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues

no code implementations NeurIPS 2018 Soumendu Sundar Mukherjee, Purnamrita Sarkar, Y. X. Rachel Wang, Bowei Yan

Variational approximation has been widely used in large-scale Bayesian inference recently, the simplest kind of which involves imposing a mean field assumption to approximate complicated latent structures.

Bayesian Inference Community Detection

Network modelling of topological domains using Hi-C data

2 code implementations30 Jul 2017 Y. X. Rachel Wang, Purnamrita Sarkar, Oana Ursu, Anshul Kundaje, Peter J. Bickel

However, one of the drawbacks of community detection is that most methods take exchangeability of the nodes in the network for granted; whereas the nodes in this case, i. e. the positions on the chromosomes, are not exchangeable.

Applications Genomics

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