16 citations · 38 across the 12 of their papers we have counts for
12 papers
Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility
Georgi Ganev, Kai Xu, Emiliano De Cristofaro
Generative models trained with Differential Privacy (DP) can produce synthetic data while reducing privacy risks. However, navigating their privacy-utility tradeoffs makes finding…
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
Charlotte Loh, Seungwook Han, Shivchander Sudalairaj +5
Deep ensembles (DE) have been successful in improving model performance by learning diverse members via the stochasticity of random initialization. While recent works have attempte…
Repairing Systematic Outliers by Learning Clean Subspaces in VAEs
Simao Eduardo, Kai Xu, Alfredo Nazabal +1
Data cleaning often comprises outlier detection and data repair. Systematic errors result from nearly deterministic transformations that occur repeatedly in the data, e.g. specific…
dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generation
Sofiane Mahiou, Kai Xu, Georgi Ganev
We propose a general, flexible, and scalable framework dpart, an open source Python library for differentially private synthetic data generation. Central to the approach is autoreg…
DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training
Joya Chen, Kai Xu, Yuhui Wang +2
A standard hardware bottleneck when training deep neural networks is GPU memory. The bulk of memory is occupied by caching intermediate tensors for gradient computation in the back…
Decoupling Makes Weakly Supervised Local Feature Better
Kunhong Li, Longguang Wang, Li Liu +3
Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences. However, since weak supe…
Couplings for Multinomial Hamiltonian Monte Carlo
Kai Xu, Tor Erlend Fjelde, Charles Sutton +1
Hamiltonian Monte Carlo (HMC) is a popular sampling method in Bayesian inference. Recently, Heng & Jacob (2019) studied Metropolis HMC with couplings for unbiased Monte Carlo estim…
Telescoping Density-Ratio Estimation
Benjamin Rhodes, Kai Xu, Michael U. Gutmann
Density-ratio estimation via classification is a cornerstone of unsupervised learning. It has provided the foundation for state-of-the-art methods in representation learning and ge…
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
Mohamed Tarek, Kai Xu, Martin Trapp +2
We present the preliminary high-level design and features of DynamicPPL.jl, a modular library providing a lightning-fast infrastructure for probabilistic programming. Besides a com…
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Cole L. Hurwitz, Kai Xu, Akash Srivastava +2
Determining the positions of neurons in an extracellular recording is useful for investigating functional properties of the underlying neural circuitry. In this work, we present a…
Generative Ratio Matching Networks
Akash Srivastava, Kai Xu, Michael U. Gutmann +1
Deep generative models can learn to generate realistic-looking images, but many of the most effective methods are adversarial and involve a saddlepoint optimization, which requires…
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge
Kai Xu, Dae Hoon Park, Chang Yi +1
Interpreting black box classifiers, such as deep networks, allows an analyst to validate a classifier before it is deployed in a high-stakes setting. A natural idea is to visualize…