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01/31/2023
Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees
Robustness and safety are critical for the trustworthy deployment of dee...
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01/31/2023
Unconstrained Dynamic Regret via Sparse Coding
Motivated by time series forecasting, we study Online Linear Optimizatio...
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11/29/2022
Closing the gap between SVRG and TD-SVRG with Gradient Splitting
Temporal difference (TD) learning is a simple algorithm for policy evalu...
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10/15/2022
Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers
We develop a Distributionally Robust Optimization (DRO) formulation for ...
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06/28/2022
Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse
Real-world sequential decision making requires data-driven algorithms th...
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05/13/2022
Optimal Parameter-free Online Learning with Switching Cost
Parameter-freeness in online learning refers to the adaptivity of an alg...
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03/11/2022
Learning from humans: combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging task
We develop a method to learn bio-inspired foraging policies using human ...
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11/05/2021
Predicting Antimicrobial Resistance in the Intensive Care Unit
Antimicrobial resistance (AMR) is a risk for patients and a burden for t...
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10/29/2021
Generalized Proximal Policy Optimization with Sample Reuse
In real-world decision making tasks, it is critical for data-driven rein...
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08/20/2021
Distributionally Robust Learning
This monograph develops a comprehensive statistical learning framework t...
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06/09/2021
Communication-efficient SGD: From Local SGD to One-Shot Averaging
We consider speeding up stochastic gradient descent (SGD) by parallelizi...
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03/22/2021
Online Baum-Welch algorithm for Hierarchical Imitation Learning
The options framework for hierarchical reinforcement learning has increa...
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02/02/2021
Strongly Adaptive OCO with Memory
Recent progress in online control has popularized online learning with m...
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12/19/2020
Uncertainty-Aware Policy Optimization: A Robust, Adaptive Trust Region Approach
In order for reinforcement learning techniques to be useful in real-worl...
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07/09/2020
Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case
Usage of automated controllers which make decisions on an environment ar...
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06/10/2020
Robust Grouped Variable Selection Using Distributionally Robust Optimization
We propose a Distributionally Robust Optimization (DRO) formulation with...
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06/10/2020
Robustified Multivariate Regression and Classification Using Distributionally Robust Optimization under the Wasserstein Metric
We develop Distributionally Robust Optimization (DRO) formulations for M...
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06/03/2020
Local SGD With a Communication Overhead Depending Only on the Number of Workers
We consider speeding up stochastic gradient descent (SGD) by parallelizi...
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06/28/2019
Asymptotic Network Independence in Distributed Optimization for Machine Learning
We provide a discussion of several recent results which have overcome a ...
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06/06/2019
A Non-Asymptotic Analysis of Network Independence for Distributed Stochastic Gradient Descent
This paper is concerned with minimizing the average of n cost functions ...
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03/21/2019
Convergence of Parameter Estimates for Regularized Mixed Linear Regression Models
We consider Mixed Linear Regression (MLR), where training data have bee...
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03/21/2019
Prescriptive Cluster-Dependent Support Vector Machines with an Application to Reducing Hospital Readmissions
We augment linear Support Vector Machine (SVM) classifiers by adding thr...
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10/24/2018
Clinical Concept Extraction with Contextual Word Embedding
Automatic extraction of clinical concepts is an essential step for turni...
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01/03/2018
Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach
Urban living in modern large cities has significant adverse effects on h...
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06/07/2017
Outlier Detection Using Distributionally Robust Optimization under the Wasserstein Metric
We present a Distributionally Robust Optimization (DRO) approach to outl...
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02/27/2017
Statistical Anomaly Detection via Composite Hypothesis Testing for Markov Models
Under Markovian assumptions, we leverage a Central Limit Theorem (CLT) f...
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01/21/2017
Learning Policies for Markov Decision Processes from Data
We consider the problem of learning a policy for a Markov decision proce...
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09/19/2013