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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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Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning
Q-learning, which seeks to learn the optimal Q-function 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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Preference-Based 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 Low-Rank Models
We study the problem of learning mixtures of low-rank 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 Minimax-Optimal 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 Decision-Making 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 Intermediate-Severity Faults?
IS faults present milder symptoms compared to severe faults, and are mor...
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Average-case 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 Q-Learning: Sharper Analysis and Variance Reduction
Asynchronous Q-learning aims to learn the optimal action-value function ...
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Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
We investigate the sample efficiency of reinforcement learning in a γ-di...
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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 Energy-Efficient Navigation of Electric Vehicles
Energy-efficient 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 Low-Rank 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 low-dimensional Euclidean representatio...
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Preference-Based 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 Nailgun-Equipped Octocopter
This paper presents the first demonstration of autonomous roofing with a...
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Communication-Efficient Distributed Optimization in Networks with Gradient Tracking
There is a growing interest in large-scale machine learning and optimiza...
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Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults
The Monte Carlo dropout method has proved to be a scalable and easy-to-u...
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An Encoder-Decoder 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 low-rank 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 high-throughput experimental design settings, such as those comm...
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AED-Net: 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 low-rank matrix completion: given partial and c...
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A One-Class 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 Low-Rank Matrices
This paper is concerned with a curious phenomenon in spectral estimation...
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Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
We apply numerical methods in combination with finite-difference-time-do...
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A General Framework for Multi-fidelity 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 Low-Rank Matrix Factorization: An Overview
Substantial progress has been made recently on developing provably accur...
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Seeding Deep Learning using Wireless Localization
Deep learning is often constrained by the lack of large, diverse labeled...
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Teaching Multiple Concepts to Forgetful Learners
How can we help a forgetful learner learn multiple concepts within a lim...
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Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval
This paper considers the problem of solving systems of quadratic equatio...
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Teaching Categories to Human Learners with Visual Explanations
We study the problem of computer-assisted teaching with explanations. Co...
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Nonconvex Matrix Factorization from Rank-One Measurements
We consider the problem of recovering low-rank matrices from random rank...
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Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
In real-world applications of education and human teaching, an effective...
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