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02/26/2022
Model-free Reinforcement Learning for Content Caching at the Wireless Edge via Restless Bandits
An explosive growth in the number of on-demand content requests has impo...
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01/25/2022
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems
We consider the problem of controlling a stochastic linear system with q...
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01/13/2022
Generalized Kernel Ridge Regression for Long Term Causal Inference: Treatment Effects, Dose Responses, and Counterfactual Distributions
I propose kernel ridge regression estimators for long term causal infere...
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12/28/2021
A Finite Sample Theorem for Longitudinal Causal Inference with Machine Learning: Long Term, Dynamic, and Mediated Effects
I construct and justify confidence intervals for longitudinal causal par...
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11/09/2021
Generalized Kernel Ridge Regression for Causal Inference with Missing-at-Random Sample Selection
I propose kernel ridge regression estimators for nonparametric dose resp...
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11/06/2021
Kernel Methods for Multistage Causal Inference: Mediation Analysis and Dynamic Treatment Effects
We propose kernel ridge regression estimators for mediation analysis and...
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10/28/2021
Pruning Attention Heads of Transformer Models Using A* Search: A Novel Approach to Compress Big NLP Architectures
Recent years have seen a growing adoption of Transformer models such as ...
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10/20/2021
U-statistic based on overlapping sample spacings
For testing goodness of fit, we consider a class of U-statistics of over...
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09/20/2021
Reinforcement Learning for Finite-Horizon Restless Multi-Armed Multi-Action Bandits
We study a finite-horizon restless multi-armed bandit problem with multi...
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07/24/2021
Inference of collective Gaussian hidden Markov models
We consider inference problems for a class of continuous state collectiv...
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07/06/2021
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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05/31/2021
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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05/18/2021
Self-interpretable Convolutional Neural Networks for Text Classification
Deep learning models for natural language processing (NLP) are inherentl...
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05/08/2021
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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04/20/2021
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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02/22/2021
Debiased Kernel Methods
I propose a practical procedure based on bias correction and sample spli...
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02/11/2021
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers
With the increasing demand for large-scale training of machine learning ...
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02/11/2021
Some parametric tests based on sample spacings
Assume that we have a random sample from an absolutely continuous distri...
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02/04/2021
Low-Power Status Updates via Sleep-Wake Scheduling
We consider the problem of optimizing the freshness of status updates th...
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01/10/2021
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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12/30/2020
Adversarial Estimation of Riesz Representers
We provide an adversarial approach to estimating Riesz representers of l...
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12/18/2020
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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11/23/2020
Learning Hidden Markov Models from Aggregate Observations
In this paper, we propose an algorithm for estimating the parameters of ...
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11/16/2020
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning
The principle of Reward-Biased Maximum Likelihood Estimate Based Adaptiv...
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11/08/2020
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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11/04/2020
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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10/22/2020
Accelerating computational modeling and design of high-entropy alloys
With huge design spaces for unique chemical and mechanical properties, w...
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10/13/2020
Multi-Armed Bandits with Dependent Arms
We study a variant of the classical multi-armed bandit problem (MABP) wh...
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10/10/2020
Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and Off-Policy Planning
We propose a novel framework for non-parametric policy evaluation in sta...
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08/12/2020
Model Robustness with Text Classification: Semantic-preserving adversarial attacks
We propose algorithms to create adversarial attacks to assess model robu...
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07/25/2020
A Partially Observable MDP Approach for Sequential Testing for Infectious Diseases such as COVID-19
The outbreak of the novel coronavirus (COVID-19) is unfolding as a major...
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06/26/2020
Incremental inference of collective graphical models
We consider incremental inference problems from aggregate data for colle...
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06/25/2020
Multi-marginal optimal transport and probabilistic graphical models
We study multi-marginal optimal transport problems from a probabilistic ...
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06/06/2020
Contextual Bandits with Side-Observations
We investigate contextual bandits in the presence of side-observations a...
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05/01/2020
Improving Robustness via Risk Averse Distributional Reinforcement Learning
One major obstacle that precludes the success of reinforcement learning ...
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03/31/2020
Inference with Aggregate Data: An Optimal Transport Approach
We consider inference problems over probabilistic graphical models with ...
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03/21/2020
Learning in Networked Control Systems
We design adaptive controller (learning rule) for a networked control sy...
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02/27/2020
Learning in Markov Decision Processes under Constraints
We consider reinforcement learning (RL) in Markov Decision Processes (MD...
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01/08/2020
Sample-based Distributional Policy Gradient
Distributional reinforcement learning (DRL) is a recent reinforcement le...
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10/01/2019
Optimal Age-Energy Trade-off via Sleep-Wake Scheduling
The problem of controlling and analyzing information updates has receive...
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09/10/2019
De-biased Machine Learning for Compliers
Instrumental variable identification is a concept in causal statistics f...
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08/03/2019
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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06/01/2019
Kernel Instrumental Variable Regression
Instrumental variable regression is a strategy for learning causal relat...
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02/20/2019
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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11/23/2018
3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys
The need for advanced materials has led to the development of complex, m...
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11/21/2018
Physics-aware Deep Generative Models for Creating Synthetic Microstructures
A key problem in computational material science deals with understanding...
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09/14/2018
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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01/05/2018
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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09/06/2017
Throughput Optimal Decentralized Scheduling of Multi-Hop Networks with End-to-End Deadline Constraints: II Wireless Networks with Interference
Consider a multihop wireless network serving multiple flows in which wir...
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02/26/2017