research
∙
07/26/2023
Optimal Estimation in Mixed-Membership Stochastic Block Models
Community detection is one of the most critical problems in modern netwo...
share
research
∙
06/08/2023
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
Federated Learning (FL) is a machine learning framework where many clien...
research
∙
01/13/2023
Scalable Batch Acquisition for Deep Bayesian Active Learning
In deep active learning, it is especially important to choose multiple e...
research
∙
01/09/2023
Active Learning for Abstractive Text Summarization
Construction of human-curated annotated datasets for abstractive text su...
research
∙
09/05/2022
ScaleFace: Uncertainty-aware Deep Metric Learning
The performance of modern deep learning-based systems dramatically depen...
research
∙
05/06/2022
Scalable computation of prediction intervals for neural networks via matrix sketching
Accounting for the uncertainty in the predictions of modern neural netwo...
research
∙
02/24/2022
Embedded Ensembles: Infinite Width Limit and Operating Regimes
A memory efficient approach to ensembling neural networks is to share mo...
research
∙
02/07/2022
NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks
This paper proposes a fast and scalable method for uncertainty quantific...
research
∙
11/04/2021
Ex^2MCMC: Sampling through Exploration Exploitation
We develop an Explore-Exploit Markov chain Monte Carlo algorithm (Ex^2MC...
research
∙
07/08/2021
Assigning Topics to Documents by Successive Projections
Topic models provide a useful tool to organize and understand the struct...
research
∙
06/30/2021
Monte Carlo Variational Auto-Encoders
Variational auto-encoders (VAE) are popular deep latent variable models ...
research
∙
12/31/2020
Nonreversible MCMC from conditional invertible transforms: a complete recipe with convergence guarantees
Markov Chain Monte Carlo (MCMC) is a class of algorithms to sample compl...
research
∙
09/30/2020
EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data
In this paper, we discuss how modern deep learning approaches can be app...
research
∙
03/06/2020
Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampled Implicit Ensembles
Modern machine learning models usually do not extrapolate well, i.e., th...
research
∙
02/27/2020
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference
In this contribution, we propose a new computationally efficient method ...
research
∙
01/30/2020
NCVis: Noise Contrastive Approach for Scalable Visualization
Modern methods for data visualization via dimensionality reduction, such...
research
∙
01/23/2020
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data
Financial institutions obtain enormous amounts of data about user transa...
research
∙
10/14/2019
Accuracy of Gaussian approximation in nonparametric Bernstein – von Mises Theorem
The prominent Bernstein – von Mises (BvM) result claims that the posteri...
research
∙
04/12/2019
Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension
The existing approaches to intrinsic dimension estimation usually are no...
research
∙
02/27/2019
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning
Active learning methods for neural networks are usually based on greedy ...
research
∙
10/06/2018
Constructing Graph Node Embeddings via Discrimination of Similarity Distributions
The problem of unsupervised learning node embeddings in graphs is one of...
research
∙
06/26/2018
Dropout-based Active Learning for Regression
Active learning is relevant and challenging for high-dimensional regress...
research
∙
04/27/2018
Sparse Group Inductive Matrix Completion
We consider the problem of inductive matrix completion under the assumpt...
research
∙
10/14/2017
Simultaneous Matrix Diagonalization for Structural Brain Networks Classification
This paper considers the problem of brain disease classification based o...
research
∙
09/05/2016