
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits
The model selection problem in the pure exploration linear bandit settin...
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Pure Exploration in Kernel and Neural Bandits
We study pure exploration in bandits, where the dimension of the feature...
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Nearest Neighbor Search Under Uncertainty
Nearest Neighbor Search (NNS) is a central task in knowledge representat...
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Pareto Optimal Model Selection in Linear Bandits
We study a model selection problem in the linear bandit setting, where t...
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Generalized Chernoff Sampling for Active Learning and Structured Bandit Algorithms
Active learning and structured stochastic bandit problems are intimately...
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Robust Outlier Arm Identification
We study the problem of Robust Outlier Arm Identification (ROAI), where ...
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On Regret with Multiple Best Arms
We study regret minimization problem with the existence of multiple best...
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Finding All εGood Arms in Stochastic Bandits
The pureexploration problem in stochastic multiarmed bandits aims to f...
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Tighter Confidence Intervals for Rating Systems
Rating systems are ubiquitous, with applications ranging from product re...
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Should Adversarial Attacks Use Pixel pNorm?
Adversarial attacks aim to confound machine learning systems, while rema...
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MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
This paper studies the problem of adaptively sampling from K distributio...
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Learning Nearest Neighbor Graphs from Noisy Distance Samples
We consider the problem of learning the nearest neighbor graph of a data...
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Linear Bandits with Feature Feedback
This paper explores a new form of the linear bandit problem in which the...
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Bilinear Bandits with Lowrank Structure
We introduce the bilinear bandit problem with lowrank structure where a...
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Scalable Sparse Subspace Clustering via Ordered Weighted ℓ_1 Regression
The main contribution of the paper is a new approach to subspace cluster...
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Teacher Improves Learning by Selecting a Training Subset
We call a learner superteachable if a teacher can trim down an iid trai...
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Adaptive Sampling for Coarse Ranking
We consider the problem of active coarse ranking, where the goal is to s...
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Learning LowDimensional Metrics
This paper investigates the theoretical foundations of metric learning, ...
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Scalable Generalized Linear Bandits: Online Computation and Hashing
Generalized Linear Bandits (GLBs), a natural extension of the stochastic...
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GraphBased Active Learning: A New Look at Expected Error Minimization
In graphbased active learning, algorithms based on expected error minim...
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Finite Sample Prediction and Recovery Bounds for Ordinal Embedding
The goal of ordinal embedding is to represent items as points in a lowd...
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Active Algorithms For Preference Learning Problems with Multiple Populations
In this paper we model the problem of learning preferences of a populati...
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On Learning High Dimensional Structured Single Index Models
Single Index Models (SIMs) are simple yet flexible semiparametric model...
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Learning Single Index Models in High Dimensions
Single Index Models (SIMs) are simple yet flexible semiparametric model...
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S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification
This paper investigates the problem of active learning for binary label ...
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Sparse Dueling Bandits
The dueling bandit problem is a variation of the classical multiarmed b...
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Data Requirement for Phylogenetic Inference from Multiple Loci: A New Distance Method
We consider the problem of estimating the evolutionary history of a set ...
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Classification with Sparse Overlapping Groups
Classification with a sparsity constraint on the solution plays a centra...
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lil' UCB : An Optimal Exploration Algorithm for MultiArmed Bandits
The paper proposes a novel upper confidence bound (UCB) procedure for id...
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Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis
Multitask learning can be effective when features useful in one task are...
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On Finding the Largest Mean Among Many
Sampling from distributions to find the one with the largest mean arises...
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A Junction Tree Framework for Undirected Graphical Model Selection
An undirected graphical model is a joint probability distribution define...
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Signal Recovery in Unions of Subspaces with Applications to Compressive Imaging
In applications ranging from communications to genetics, signals can be ...
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Robust SpatioTemporal Signal Recovery from Noisy Counts in Social Media
Many realworld phenomena can be represented by a spatiotemporal signal...
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HighRank Matrix Completion and Subspace Clustering with Missing Data
This paper considers the problem of completing a matrix with many missin...
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MinimaxOptimal Bounds for Detectors Based on Estimated Prior Probabilities
In many signal detection and classification problems, we have knowledge ...
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Tight Measurement Bounds for Exact Recovery of Structured Sparse Signals
Standard compressive sensing results state that to exactly recover an s ...
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Causal Network Inference via Group Sparse Regularization
This paper addresses the problem of inferring sparse causal networks mod...
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Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities
Hierarchical clustering based on pairwise similarities is a common tool ...
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Distilled Sensing: Adaptive Sampling for Sparse Detection and Estimation
Adaptive sampling results in dramatic improvements in the recovery of sp...
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Robert Nowak
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McFarlandBascom Professor in Engineering at University of WisconsinMadison