
MetaReinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks
In this paper, the problem of enhancing the quality of virtual reality (...
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Adversarial Combinatorial Bandits with General Nonlinear Reward Functions
In this paper we study the adversarial combinatorial bandit with a known...
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Smooth Bandit Optimization: Generalization to Hölder Space
We consider bandit optimization of a smooth reward function, where the g...
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PrivacyPreserving Dynamic Personalized Pricing with Demand Learning
The prevalence of ecommerce has made detailed customers' personal infor...
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Nearly Bounded Regret of Resolving Heuristics in Pricebased Revenue Management
Pricebased revenue management is a class of important questions in oper...
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Nearly DimensionIndependent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection
We consider the stochastic contextual bandit problem under the high dime...
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Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing
Datadriven sequential decision has found a wide range of applications i...
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Optimism in Reinforcement Learning with Generalized Linear Function Approximation
We design a new provably efficient algorithm for episodic reinforcement ...
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Robust Dynamic Assortment Optimization in the Presence of Outlier Customers
We consider the dynamic assortment optimization problem under the multin...
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Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVs
In this paper, the problem of optimizing the deployment of unmanned aeri...
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√(n)Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank
In this paper, we consider the problem of online learning of Markov deci...
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NCLS: Neural CrossLingual Summarization
Crosslingual summarization (CLS) is the task to produce a summary in on...
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Tight Regret Bounds for Infinitearmed Linear Contextual Bandits
Linear contextual bandit is a class of sequential decision making proble...
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Nearly MinimaxOptimal Regret for Linearly Parameterized Bandits
We study the linear contextual bandit problem with finite action sets. W...
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On Asymptotically Tight Tail Bounds for Sums of Geometric and Exponential Random Variables
In this note we prove bounds on the upper and lower probability tails of...
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Dynamic Assortment Optimization with Changing Contextual Information
In this paper, we study the dynamic assortment optimization problem unde...
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Efficient Load Sampling for WorstCase Structural Analysis Under Force Location Uncertainty
An important task in structural design is to quantify the structural per...
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Dynamic Assortment Selection under the Nested Logit Models
We study a stylized dynamic assortment planning problem during a selling...
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Robust Nonparametric Regression under Huber's εcontamination Model
We consider the nonparametric regression problem under Huber's ϵcontam...
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Phrase Table as Recommendation Memory for Neural Machine Translation
Neural Machine Translation (NMT) has drawn much attention due to its pro...
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How Many Samples are Needed to Learn a Convolutional Neural Network?
A widespread folklore for explaining the success of convolutional neural...
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NearOptimal Policies for Dynamic Multinomial Logit Assortment Selection Models
In this paper we consider the dynamic assortment selection problem under...
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Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates
We consider the problem of global optimization of an unknown nonconvex ...
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NearLinear Time Local Polynomial Nonparametric Estimation
Local polynomial regression (Fan & Gijbels, 1996) is an important class ...
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Direct Learning to Rank and Rerank
Learningtorank techniques have proven to be extremely useful for prior...
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NearOptimal Discrete Optimization for Experimental Design: A Regret Minimization Approach
The experimental design problem concerns the selection of k points from ...
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Word, Subword or Character? An Empirical Study of Granularity in ChineseEnglish NMT
Neural machine translation (NMT), a new approach to machine translation,...
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Towards Neural Machine Translation with Partially Aligned Corpora
While neural machine translation (NMT) has become the new paradigm, the ...
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Convergence Rates of Latent Topic Models Under Relaxed Identifiability Conditions
In this paper we study the frequentist convergence rate for the Latent D...
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Stochastic Zerothorder Optimization in High Dimensions
We consider the problem of optimizing a highdimensional convex function...
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A Note on Tight Lower Bound for MNLBandit Assortment Selection Models
In this note we prove a tight lower bound for the MNLbandit assortment ...
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Nonstationary Stochastic Optimization with Local Spatial and Temporal Changes
We consider a nonstationary sequential stochastic optimization problem,...
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Sequence Modeling via Segmentations
Segmental structure is a common pattern in many types of sequences such ...
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On the Power of Truncated SVD for General Highrank Matrix Estimation Problems
We show that given an estimate A that is close to a general highrank po...
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Rate Optimal Estimation and Confidence Intervals for Highdimensional Regression with Missing Covariates
Although a majority of the theoretical literature in highdimensional st...
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A Theoretical Analysis of Noisy Sparse Subspace Clustering on DimensionalityReduced Data
Subspace clustering is the problem of partitioning unlabeled data points...
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Online and DifferentiallyPrivate Tensor Decomposition
In this paper, we resolve many of the key algorithmic questions regardin...
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An Improved GapDependency Analysis of the Noisy Power Method
We consider the noisy power method algorithm, which has wide application...
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Spectral Learning for Supervised Topic Models
Supervised topic models simultaneously model the latent topic structure ...
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On Computationally Tractable Selection of Experiments in MeasurementConstrained Regression Models
We derive computationally tractable methods to select a small subset of ...
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Fast and Guaranteed Tensor Decomposition via Sketching
Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in sta...
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Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data
We consider the problem of matrix column subset selection, which selects...
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Graph Connectivity in Noisy Sparse Subspace Clustering
Subspace clustering is the problem of clustering data points into a unio...
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Noiseadaptive Marginbased Active Learning and Lower Bounds under Tsybakov Noise Condition
We present a simple noiserobust marginbased active learning algorithm ...
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A Theoretical Analysis of NDCG Type Ranking Measures
A central problem in ranking is to design a ranking measure for evaluati...
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Yining Wang
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PhD student, Machine Learning Department at Carnegie Mellon University