254 citations · 882 across the 66 of their papers we have counts for
8 papers · 1 filter
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Yichong Xu, Sivaraman Balakrishnan, Aarti Singh +1
In supervised learning, we typically leverage a fully labeled dataset to design methods for function estimation or prediction. In many practical situations, we are able to obtain a…
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…
Local White Matter Architecture Defines Functional Brain Dynamics
Yo Joong Choe, Sivaraman Balakrishnan, Aarti Singh +2
Large bundles of myelinated axons, called white matter, anatomically connect disparate brain regions together and compose the structural core of the human connectome. We recently p…
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…
Robust Multivariate Nonparametric Tests via Projection-Averaging
Ilmun Kim, Sivaraman Balakrishnan, Larry Wasserman
In this work, we generalize the Cramér-von Mises statistic via projection-averaging to obtain a robust test for the multivariate two-sample problem. The proposed test is consistent…
Robust Estimation via Robust Gradient Estimation
Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan +1
We provide a new computationally-efficient class of estimators for risk minimization. We show that these estimators are robust for general statistical models: in the classical Hube…
Sharp instruments for classifying compliers and generalizing causal effects
Edward H. Kennedy, Sivaraman Balakrishnan, Max G'Sell
It is well-known that, without restricting treatment effect heterogeneity, instrumental variable (IV) methods only identify "local" effects among compliers, i.e., those subjects wh…