154 citations · 339 across the 18 of their papers we have counts for
19 papers
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits
Yusha Liu, Aarti Singh
In continuum-armed bandit problems where the underlying function resides in a reproducing kernel Hilbert space (RKHS), namely, the kernelised bandit problems, an important open pro…
Smooth Bandit Optimization: Generalization to Hölder Space
Yusha Liu, Yining Wang, Aarti Singh
We consider bandit optimization of a smooth reward function, where the goal is cumulative regret minimization. This problem has been studied for -Hölder continuous (including Li…
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions
Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah +1
A number of applications require two-sample testing on ranked preference data. For instance, in crowdsourcing, there is a long-standing question of whether pairwise comparison data…
Efficient Load Sampling for Worst-Case Structural Analysis Under Force Location Uncertainty
Yining Wang, Erva Ulu, Aarti Singh +1
An important task in structural design is to quantify the structural performance of an object under the external forces it may experience during its use. The problem proves to be c…
Robust Nonparametric Regression under Huber's -contamination Model
Simon S. Du, Yining Wang, Sivaraman Balakrishnan +2
We consider the non-parametric regression problem under Huber's -contamination model, in which an fraction of observations are subject to arbitrary adversarial noise. We fir…
How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network?
Simon S. Du, Yining Wang, Xiyu Zhai +3
It is widely believed that the practical success of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) owes to the fact that CNNs and RNNs use a more compact…
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates
Yining Wang, Sivaraman Balakrishnan, Aarti Singh
We consider the problem of global optimization of an unknown non-convex smooth function with zeroth-order feedback. In this setup, an algorithm is allowed to adaptively query the u…
Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach
Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh +1
The experimental design problem concerns the selection of k points from a potentially large design pool of p-dimensional vectors, so as to maximize the statistical efficiency regre…
Stochastic Zeroth-order Optimization in High Dimensions
Yining Wang, Simon Du, Sivaraman Balakrishnan +1
We consider the problem of optimizing a high-dimensional convex function using stochastic zeroth-order queries. Under sparsity assumptions on the gradients or function values, we p…
Computationally Efficient Robust Estimation of Sparse Functionals
Simon S. Du, Sivaraman Balakrishnan, Aarti Singh
Many conventional statistical procedures are extremely sensitive to seemingly minor deviations from modeling assumptions. This problem is exacerbated in modern high-dimensional set…
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems
Simon S. Du, Yining Wang, Aarti Singh
We show that given an estimate that is close to a general high-rank positive semi-definite (PSD) matrix in spectral norm (i.e., ), the…
Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates
Yining Wang, Jialei Wang, Sivaraman Balakrishnan +1
Although a majority of the theoretical literature in high-dimensional statistics has focused on settings which involve fully-observed data, settings with missing values and corrupt…
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data
Yining Wang, Yu-Xiang Wang, Aarti Singh
Subspace clustering is the problem of partitioning unlabeled data points into a number of clusters so that data points within one cluster lie approximately on a low-dimensional lin…
Data Poisoning Attacks on Factorization-Based Collaborative Filtering
Bo Li, Yining Wang, Aarti Singh +1
Recommendation and collaborative filtering systems are important in modern information and e-commerce applications. As these systems are becoming increasingly popular in the indust…
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models
Yining Wang, Adams Wei Yu, Aarti Singh
We derive computationally tractable methods to select a small subset of experiment settings from a large pool of given design points. The primary focus is on linear regression mode…
Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data
Yining Wang, Aarti Singh
We consider the problem of matrix column subset selection, which selects a subset of columns from an input matrix such that the input can be well approximated by the span of the se…
Graph Connectivity in Noisy Sparse Subspace Clustering
Yining Wang, Yu-Xiang Wang, Aarti Singh
Subspace clustering is the problem of clustering data points into a union of low-dimensional linear/affine subspaces. It is the mathematical abstraction of many important problems…
Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition
Yining Wang, Aarti Singh
We present a simple noise-robust margin-based active learning algorithm to find homogeneous (passing the origin) linear separators and analyze its error convergence when labels are…
Efficient Active Algorithms for Hierarchical Clustering
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu +1
Advances in sensing technologies and the growth of the internet have resulted in an explosion in the size of modern datasets, while storage and processing power continue to lag beh…