
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
Policy optimization, which learns the policy of interest by maximizing t...
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SampleEfficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting
Lowcomplexity models such as linear function representation play a pivo...
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Understanding the Effect of Bias in Deep Anomaly Detection
Anomaly detection presents a unique challenge in machine learning, due t...
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Generating Continuous Motion and Force Plans in RealTime for Legged Mobile Manipulation
Manipulators can be added to legged robots, allowing them to interact wi...
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Towards an Interpretable Datadriven Trigger System for Highthroughput Physics Facilities
Dataintensive science is increasingly reliant on realtime processing c...
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Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small EigenGaps
Eigenvector perturbation analysis plays a vital role in various statisti...
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Softmax Policy Gradient Methods Can Take Exponential Time to Converge
The softmax policy gradient (PG) method, which performs gradient ascent ...
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Is QLearning Minimax Optimal? A Tight Sample Complexity Analysis
Qlearning, which seeks to learn the optimal Qfunction of a Markov deci...
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Spectral Methods for Data Science: A Statistical Perspective
Spectral methods have emerged as a simple yet surprisingly effective app...
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PreferenceBased Batch and Sequential Teaching
Algorithmic machine teaching studies the interaction between a teacher a...
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Learning Time Varying Risk Preferences from Investment Portfolios using Inverse Optimization with Applications on Mutual Funds
The fundamental principle in Modern Portfolio Theory (MPT) is based on t...
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Learning Mixtures of LowRank Models
We study the problem of learning mixtures of lowrank models, i.e. recon...
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Using Ensemble Classifiers to Detect Incipient Anomalies
Incipient anomalies present milder symptoms compared to severe ones, and...
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Convex and Nonconvex Optimization Are Both MinimaxOptimal for Noisy Blind Deconvolution
We investigate the effectiveness of convex relaxation and nonconvex opti...
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Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization
Natural policy gradient (NPG) methods are among the most widely used pol...
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Exploiting Uncertainties from Ensemble Learners to Improve DecisionMaking in Healthcare AI
Ensemble learning is widely applied in Machine Learning (ML) to improve ...
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Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of IntermediateSeverity Faults?
IS faults present milder symptoms compared to severe faults, and are mor...
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Averagecase Complexity of Teaching Convex Polytopes via Halfspace Queries
We examine the task of locating a target region among those induced by i...
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Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality
We study the distribution and uncertainty of nonconvex optimization for ...
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Sample Complexity of Asynchronous QLearning: Sharper Analysis and Variance Reduction
Asynchronous Qlearning aims to learn the optimal actionvalue function ...
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Breaking the Sample Size Barrier in ModelBased Reinforcement Learning with a Generative Model
We investigate the sample efficiency of reinforcement learning in a γdi...
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Understanding the Power and Limitations of Teaching with Imperfect Knowledge
Machine teaching studies the interaction between a teacher and a student...
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An Online Learning Framework for EnergyEfficient Navigation of Electric Vehicles
Energyefficient navigation constitutes an important challenge in electr...
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A Financial Service Chatbot based on Deep Bidirectional Transformers
We develop a chatbot using Deep Bidirectional Transformer models (BERT) ...
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Adaptive Teaching of Temporal Logic Formulas to Learners with Preferences
Machine teaching is an algorithmic framework for teaching a target hypot...
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Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data
This paper delivers improved theoretical guarantees for the convex progr...
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Inference for linear forms of eigenvectors under minimal eigenvalue separation: Asymmetry and heteroscedasticity
A fundamental task that spans numerous applications is inference and unc...
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Nonconvex LowRank Symmetric Tensor Completion from Noisy Data
We study a noisy symmetric tensor completion problem of broad practical ...
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Landmark Ordinal Embedding
In this paper, we aim to learn a lowdimensional Euclidean representatio...
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PreferenceBased Batch and Sequential Teaching: Towards a Unified View of Models
Algorithmic machine teaching studies the interaction between a teacher a...
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Subspace Estimation from Unbalanced and Incomplete Data Matrices: ℓ_2,∞ Statistical Guarantees
This paper is concerned with estimating the column space of an unknown l...
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RDMA vs. RPC for Implementing Distributed Data Structures
Distributed data structures are key to implementing scalable application...
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Nailed It: Autonomous Roofing with a NailgunEquipped Octocopter
This paper presents the first demonstration of autonomous roofing with a...
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CommunicationEfficient Distributed Optimization in Networks with Gradient Tracking
There is a growing interest in largescale machine learning and optimiza...
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Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing OutofDistribution Faults
The Monte Carlo dropout method has proved to be a scalable and easytou...
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An EncoderDecoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
We present a novel unsupervised deep learning approach that utilizes the...
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Inference and Uncertainty Quantification for Noisy Matrix Completion
Noisy matrix completion aims at estimating a lowrank matrix given only ...
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Understanding the Effectiveness of Ultrasonic Microphone Jammer
Recent works have explained the principle of using ultrasonic transmissi...
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Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
In many highthroughput experimental design settings, such as those comm...
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AEDNet: An Abnormal Event Detection Network
It is challenging to detect the anomaly in crowded scenes for quite a lo...
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Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
This paper studies noisy lowrank matrix completion: given partial and c...
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A OneClass Support Vector Machine Calibration Method for Time Series Change Point Detection
It is important to identify the change point of a system's health status...
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Trip Prediction by Leveraging Trip Histories from Neighboring Users
We propose a novel approach for trip prediction by analyzing user's trip...
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Asymmetry Helps: Eigenvalue and Eigenvector Analyses of Asymmetrically Perturbed LowRank Matrices
This paper is concerned with a curious phenomenon in spectral estimation...
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Optimizing Photonic Nanostructures via Multifidelity Gaussian Processes
We apply numerical methods in combination with finitedifferencetimedo...
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A General Framework for Multifidelity Bayesian Optimization with Gaussian Processes
How can we efficiently gather information to optimize an unknown functio...
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Pixel Level Data Augmentation for Semantic Image Segmentation using Generative Adversarial Networks
Semantic segmentation is one of the basic topics in computer vision, it ...
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Adversarial WiFi Sensing using a Single Smartphone
Wireless devices are everywhere, at home, at the office, and on the stre...
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Adversarial WiFi Sensing
Wireless devices are everywhere, at home, at the office, and on the stre...
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Nonconvex Optimization Meets LowRank Matrix Factorization: An Overview
Substantial progress has been made recently on developing provably accur...
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Yuxin Chen
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Assistant professor in the Department of Electrical Engineering and an associated faculty member in the Department of Computer Science at Princeton University