
A HigherOrder KolmogorovSmirnov Test
We present an extension of the KolmogorovSmirnov (KS) twosample test, ...
03/24/2019 ∙ by Veeranjaneyulu Sadhanala, et al. ∙ 16 ∙ shareread it

Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
The Gaussian mechanism is an essential building block used in multitude ...
05/16/2018 ∙ by Borja Balle, et al. ∙ 12 ∙ shareread it

ImitationRegularized Offline Learning
We study the problem of offline learning in automated decision systems u...
01/15/2019 ∙ by Yifei Ma, et al. ∙ 12 ∙ shareread it

Online Forecasting of TotalVariationbounded Sequences
We consider the problem of online forecasting of sequences of length n w...
06/08/2019 ∙ by Dheeraj Baby, et al. ∙ 5 ∙ shareread it

Provably Efficient QLearning with Low Switching Cost
We take initial steps in studying PACMDP algorithms with limited adapti...
05/30/2019 ∙ by Yu Bai, et al. ∙ 3 ∙ shareread it

Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
Time series forecasting is an important problem across many domains, inc...
06/29/2019 ∙ by Shiyang Li, et al. ∙ 3 ∙ shareread it

Subsampled Rényi Differential Privacy and Analytical Moments Accountant
We study the problem of subsampling in differential privacy (DP), a ques...
07/31/2018 ∙ by YuXiang Wang, et al. ∙ 2 ∙ shareread it

ProxQuant: Quantized Neural Networks via Proximal Operators
To make deep neural networks feasible in resourceconstrained environmen...
10/01/2018 ∙ by Yu Bai, et al. ∙ 2 ∙ shareread it

Nonstationary Stochastic Optimization with Local Spatial and Temporal Changes
We consider a nonstationary sequential stochastic optimization problem,...
08/09/2017 ∙ by Xi Chen, et al. ∙ 0 ∙ shareread it

Perinstance Differential Privacy and the Adaptivity of Posterior Sampling in Linear and Ridge regression
Differential privacy (DP), ever since its advent, has been a controversi...
07/24/2017 ∙ by YuXiang Wang, et al. ∙ 0 ∙ shareread it

Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata
We combine finegrained spatially referenced census data with the vote o...
11/11/2016 ∙ by Seth Flaxman, et al. ∙ 0 ∙ shareread it

A Theoretical Analysis of Noisy Sparse Subspace Clustering on DimensionalityReduced Data
Subspace clustering is the problem of partitioning unlabeled data points...
10/24/2016 ∙ by Yining Wang, et al. ∙ 0 ∙ shareread it

Fast Differentially Private Matrix Factorization
Differentially private collaborative filtering is a challenging task, bo...
05/06/2015 ∙ by Ziqi Liu, et al. ∙ 0 ∙ shareread it

Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers
We consider the problem of estimating a function defined over n location...
05/26/2016 ∙ by Veeranjaneyulu Sadhanala, et al. ∙ 0 ∙ shareread it

OnAverage KLPrivacy and its equivalence to Generalization for MaxEntropy Mechanisms
We define OnAverage KLPrivacy and present its properties and connectio...
05/08/2016 ∙ by YuXiang Wang, et al. ∙ 0 ∙ shareread it

A Minimax Theory for Adaptive Data Analysis
In adaptive data analysis, the user makes a sequence of queries on the d...
02/13/2016 ∙ by YuXiang Wang, et al. ∙ 0 ∙ shareread it

Graph Connectivity in Noisy Sparse Subspace Clustering
Subspace clustering is the problem of clustering data points into a unio...
04/04/2015 ∙ by Yining Wang, et al. ∙ 0 ∙ shareread it

Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
We consider the problem of Bayesian learning on sensitive datasets and p...
02/26/2015 ∙ by YuXiang Wang, et al. ∙ 0 ∙ shareread it

Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle
While machine learning has proven to be a powerful datadriven solution ...
02/23/2015 ∙ by YuXiang Wang, et al. ∙ 0 ∙ shareread it

Trend Filtering on Graphs
We introduce a family of adaptive estimators on graphs, based on penaliz...
10/28/2014 ∙ by YuXiang Wang, et al. ∙ 0 ∙ shareread it

Parallel and Distributed BlockCoordinate FrankWolfe Algorithms
We develop parallel and distributed FrankWolfe algorithms; the former o...
09/22/2014 ∙ by YuXiang Wang, et al. ∙ 0 ∙ shareread it

The Falling Factorial Basis and Its Statistical Applications
We study a novel splinelike basis, which we name the "falling factorial...
05/03/2014 ∙ by YuXiang Wang, et al. ∙ 0 ∙ shareread it

Noisy Sparse Subspace Clustering
This paper considers the problem of subspace clustering under noise. Spe...
09/05/2013 ∙ by YuXiang Wang, et al. ∙ 0 ∙ shareread it

Practical Matrix Completion and Corruption Recovery using Proximal Alternating Robust Subspace Minimization
Lowrank matrix completion is a problem of immense practical importance....
09/06/2013 ∙ by YuXiang Wang, et al. ∙ 0 ∙ shareread it

Detecting and Correcting for Label Shift with Black Box Predictors
Faced with distribution shift between training and test set, we wish to ...
02/12/2018 ∙ by Zachary C Lipton, et al. ∙ 0 ∙ shareread it

signSGD: compressed optimisation for nonconvex problems
Training large neural networks requires distributing learning across mul...
02/13/2018 ∙ by Jeremy Bernstein, et al. ∙ 0 ∙ shareread it

Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain
We revisit the problem of linear regression under a differential privacy...
03/07/2018 ∙ by YuXiang Wang, et al. ∙ 0 ∙ shareread it

Optimal OffPolicy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
Motivated by the many realworld applications of reinforcement learning ...
06/08/2019 ∙ by Tengyang Xie, et al. ∙ 0 ∙ shareread it

Doubly Robust Crowdsourcing
Largescale labeled datasets are the indispensable fuel that ignites the...
06/08/2019 ∙ by Chong Liu, et al. ∙ 0 ∙ shareread it
YuXiang Wang
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Scientist, Amazon AI, AWS, Applied Scientist, Amazon AI, Machine Learning Department in Carnegie Mellon University, PhD Student at Carnegie Mellon University.