
Onebit feedback is sufficient for upper confidence bound policies
We consider a variant of the traditional multiarmed bandit problem in w...
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Stochastic Linear Bandits with Protected Subspace
We study a variant of the stochastic linear bandit problem wherein we op...
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Why Does MAML Outperform ERM? An Optimization Perspective
ModelAgnostic MetaLearning (MAML) has demonstrated widespread success ...
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Hellinger KLUCB based Bandit Algorithms for Markovian and i.i.d. Settings
In the regretbased formulation of multiarmed bandit (MAB) problems, ex...
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Robust MultiAgent MultiArmed Bandits
There has been recent interest in collaborative multiagent bandits, whe...
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MultiAgent LowDimensional Linear Bandits
We study a multiagent stochastic linear bandit with side information, p...
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Contextual Blocking Bandits
We study a novel variant of the multiarmed bandit problem, where at eac...
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Warm Starting Bandits with Side Information from Confounded Data
We study a variant of the multiarmed bandit problem where side informat...
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DistributionAgnostic ModelAgnostic MetaLearning
The ModelAgnostic MetaLearning (MAML) algorithm <cit.> has been celebr...
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FiniteSample Analysis of Stochastic Approximation Using Smooth Convex Envelopes
Stochastic Approximation (SA) is a popular approach for solving fixed po...
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The Gossiping InsertEliminate Algorithm for MultiAgent Bandits
We consider a decentralized multiagent Multi Armed Bandit (MAB) setup c...
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Optimistic Optimization for Statistical Model Checking with Regret Bounds
We explore application of multiarmed bandit algorithms to statistical m...
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Social Learning in Multi Agent Multi Armed Bandits
In this paper, we introduce a distributed version of the classical stoch...
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Blocking Bandits
We consider a novel stochastic multiarmed bandit setting, where playing...
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Mix and Match: An Optimistic TreeSearch Approach for Learning Models from Mixture Distributions
We consider a covariate shift problem where one has access to several m...
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Noisy Blackbox Optimization with MultiFidelity Queries: A Tree Search Approach
We study the problem of blackbox optimization of a noisy function in th...
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Entropic Latent Variable Discovery
We consider the problem of discovering the simplest latent variable that...
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Importance weighted generative networks
Deep generative networks can simulate from a complex target distribution...
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The Shape of Alerts: Detecting Malware Using Distributed Detectors by Robustly Amplifying Transient Correlations
We introduce a new malware detector  ShapeGD  that aggregates permac...
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Contextual Bandits with Stochastic Experts
We consider the problem of contextual bandits with stochastic experts, w...
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On Learning the cμ Rule in Single and Parallel Server Networks
We consider learningbased variants of the c μ rule for scheduling in si...
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On Learning the cμ Rule: Single and Multiserver Settings
We consider learningbased variants of the c μ rule  a classic and wel...
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Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks
Emerging 5G systems will need to efficiently support both broadband traf...
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ModelPowered Conditional Independence Test
We consider the problem of nonparametric Conditional Independence testi...
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Identifying Best Interventions through Online Importance Sampling
Motivated by applications in computational advertising and systems biolo...
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The Search Problem in Mixture Models
We consider the task of learning the parameters of a single component o...
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Contextual Bandits with Latent Confounders: An NMF Approach
Motivated by online recommendation and advertising systems, we consider ...
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Greedy Learning of Markov Network Structure
We propose a new yet natural algorithm for learning the graph structure ...
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Sanjay Shakkottai
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