
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy
Even the most carefully curated economic data sets have variables that a...
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A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees
Debiased machine learning is a meta algorithm based on bias correction a...
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Selfinterpretable Convolutional Neural Networks for Text Classification
Deep learning models for natural language processing (NLP) are inherentl...
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Some multivariate goodness of fit tests based on data depth
Using the fact that some depth functions characterize certain family of ...
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Robustness Tests of NLP Machine Learning Models: Search and Semantically Replace
This paper proposes a strategy to assess the robustness of different mac...
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Debiased Kernel Methods
I propose a practical procedure based on bias correction and sample spli...
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StragglerResilient Distributed Machine Learning with Dynamic Backup Workers
With the increasing demand for largescale training of machine learning ...
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Some parametric tests based on sample spacings
Assume that we have a random sample from an absolutely continuous distri...
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LowPower Status Updates via SleepWake Scheduling
We consider the problem of optimizing the freshness of status updates th...
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Learning Augmented Index Policy for Optimal Service Placement at the Network Edge
We consider the problem of service placement at the network edge, in whi...
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Adversarial Estimation of Riesz Representers
We provide an adversarial approach to estimating Riesz representers of l...
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Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments
Negative control is a strategy for learning the causal relationship betw...
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Learning Hidden Markov Models from Aggregate Observations
In this paper, we propose an algorithm for estimating the parameters of ...
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Reward Biased Maximum Likelihood Estimation for Reinforcement Learning
The principle of RewardBiased Maximum Likelihood Estimate Based Adaptiv...
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Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification
The deep neural networks (DNNs) have achieved great success in learning ...
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Filtering for Aggregate Hidden Markov Models with Continuous Observations
We consider a class of filtering problems for large populations where ea...
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Accelerating computational modeling and design of highentropy alloys
With huge design spaces for unique chemical and mechanical properties, w...
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MultiArmed Bandits with Dependent Arms
We study a variant of the classical multiarmed bandit problem (MABP) wh...
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Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and OffPolicy Planning
We propose a novel framework for nonparametric policy evaluation in sta...
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Model Robustness with Text Classification: Semanticpreserving adversarial attacks
We propose algorithms to create adversarial attacks to assess model robu...
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A Partially Observable MDP Approach for Sequential Testing for Infectious Diseases such as COVID19
The outbreak of the novel coronavirus (COVID19) is unfolding as a major...
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Incremental inference of collective graphical models
We consider incremental inference problems from aggregate data for colle...
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Multimarginal optimal transport and probabilistic graphical models
We study multimarginal optimal transport problems from a probabilistic ...
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Contextual Bandits with SideObservations
We investigate contextual bandits in the presence of sideobservations a...
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Improving Robustness via Risk Averse Distributional Reinforcement Learning
One major obstacle that precludes the success of reinforcement learning ...
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Inference with Aggregate Data: An Optimal Transport Approach
We consider inference problems over probabilistic graphical models with ...
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Learning in Networked Control Systems
We design adaptive controller (learning rule) for a networked control sy...
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Learning in Markov Decision Processes under Constraints
We consider reinforcement learning (RL) in Markov Decision Processes (MD...
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Samplebased Distributional Policy Gradient
Distributional reinforcement learning (DRL) is a recent reinforcement le...
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Optimal AgeEnergy Tradeoff via SleepWake Scheduling
The problem of controlling and analyzing information updates has receive...
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Debiased Machine Learning for Compliers
Instrumental variable identification is a concept in causal statistics f...
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Optimal Information Updating based on Value of Information
We address the problem of how to optimally schedule data packets over an...
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Kernel Instrumental Variable Regression
Instrumental variable regression is a strategy for learning causal relat...
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Optimal Decentralized Dynamic Policies for Video Streaming over Wireless Channels
The problem addressed is that of optimally controlling, in a decentraliz...
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3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solidsolution alloys
The need for advanced materials has led to the development of complex, m...
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Physicsaware Deep Generative Models for Creating Synthetic Microstructures
A key problem in computational material science deals with understanding...
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Learning L2 Continuous Regression Functionals via Regularized Riesz Representers
Many objects of interest can be expressed as an L2 continuous functional...
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Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks
We consider a wireless broadcast network with a base station sending tim...
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Throughput Optimal Decentralized Scheduling of MultiHop Networks with EndtoEnd Deadline Constraints: II Wireless Networks with Interference
Consider a multihop wireless network serving multiple flows in which wir...
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Kiefer Wolfowitz Algorithm is Asymptotically Optimal for a Class of NonStationary Bandit Problems
We consider the problem of designing an allocation rule or an "online le...
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Rahul Singh
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