
Near Optimal Provable Uniform Convergence in OffPolicy Evaluation for Reinforcement Learning
The OffPolicy Evaluation aims at estimating the performance of target p...
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Bullseye Polytope: A Scalable CleanLabel Poisoning Attack with Improved Transferability
A recent source of concern for the security of neural networks is the em...
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Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
Adversarial learning has demonstrated good performance in the unsupervis...
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Asymptotically Efficient OffPolicy Evaluation for Tabular Reinforcement Learning
We consider the problem of offpolicy evaluation for reinforcement learn...
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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...
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Doubly Robust Crowdsourcing
Largescale labeled datasets are the indispensable fuel that ignites the...
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Optimal OffPolicy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
Motivated by the many realworld applications of reinforcement learning ...
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Online Forecasting of TotalVariationbounded Sequences
We consider the problem of online forecasting of sequences of length n w...
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Provably Efficient QLearning with Low Switching Cost
We take initial steps in studying PACMDP algorithms with limited adapti...
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A HigherOrder KolmogorovSmirnov Test
We present an extension of the KolmogorovSmirnov (KS) twosample test, ...
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ImitationRegularized Offline Learning
We study the problem of offline learning in automated decision systems u...
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ProxQuant: Quantized Neural Networks via Proximal Operators
To make deep neural networks feasible in resourceconstrained environmen...
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Subsampled Rényi Differential Privacy and Analytical Moments Accountant
We study the problem of subsampling in differential privacy (DP), a ques...
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Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
The Gaussian mechanism is an essential building block used in multitude ...
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Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain
We revisit the problem of linear regression under a differential privacy...
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signSGD: compressed optimisation for nonconvex problems
Training large neural networks requires distributing learning across mul...
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Detecting and Correcting for Label Shift with Black Box Predictors
Faced with distribution shift between training and test set, we wish to ...
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Nonstationary Stochastic Optimization with Local Spatial and Temporal Changes
We consider a nonstationary sequential stochastic optimization problem,...
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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...
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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...
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A Theoretical Analysis of Noisy Sparse Subspace Clustering on DimensionalityReduced Data
Subspace clustering is the problem of partitioning unlabeled data points...
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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...
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OnAverage KLPrivacy and its equivalence to Generalization for MaxEntropy Mechanisms
We define OnAverage KLPrivacy and present its properties and connectio...
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A Minimax Theory for Adaptive Data Analysis
In adaptive data analysis, the user makes a sequence of queries on the d...
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Fast Differentially Private Matrix Factorization
Differentially private collaborative filtering is a challenging task, bo...
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Graph Connectivity in Noisy Sparse Subspace Clustering
Subspace clustering is the problem of clustering data points into a unio...
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Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
We consider the problem of Bayesian learning on sensitive datasets and p...
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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 ...
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Trend Filtering on Graphs
We introduce a family of adaptive estimators on graphs, based on penaliz...
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Parallel and Distributed BlockCoordinate FrankWolfe Algorithms
We develop parallel and distributed FrankWolfe algorithms; the former o...
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The Falling Factorial Basis and Its Statistical Applications
We study a novel splinelike basis, which we name the "falling factorial...
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Practical Matrix Completion and Corruption Recovery using Proximal Alternating Robust Subspace Minimization
Lowrank matrix completion is a problem of immense practical importance....
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Noisy Sparse Subspace Clustering
This paper considers the problem of subspace clustering under noise. Spe...
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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.