Search Results for author:
Found 7 papers, 2 papers with code
Network modelling of topological domains using Hi-C data
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
Adaptive conformal classification with noisy labels
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.
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues
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.
Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal
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.
A Unified Framework for Tuning Hyperparameters in Clustering Problems
In this paper, we provide a framework with provable guarantees for selecting hyperparameters in a number of distinct models.
On hyperparameter tuning in general clustering problemsm
Tuning hyperparameters for unsupervised learning problems is difficult in general due to the lack of ground truth for validation.
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data
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.
