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07/21/2023
Model-based Offline Reinforcement Learning with Count-based Conservatism
In this paper, we propose a model-based offline reinforcement learning m...
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05/31/2023
Combinatorial Neural Bandits
We consider a contextual combinatorial bandit problem where in each roun...
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12/27/2022
Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation
We study model-based reinforcement learning (RL) for episodic Markov dec...
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06/11/2022
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits
We propose a novel algorithm for linear contextual bandits with O(√(dT l...
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05/23/2022
Personalized Federated Learning with Server-Side Information
Personalized Federated Learning (FL) is an emerging research field in FL...
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03/25/2021
Multinomial Logit Contextual Bandits: Provable Optimality and Practicality
We consider a sequential assortment selection problem where the user cho...
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07/16/2020
Sparsity-Agnostic Lasso Bandit
We consider a stochastic contextual bandit problem where the dimension d...
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06/07/2020
Thompson Sampling for Multinomial Logit Contextual Bandits
We consider a dynamic assortment selection problem where the goal is to ...
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04/22/2020
Sequential Anomaly Detection using Inverse Reinforcement Learning
One of the most interesting application scenarios in anomaly detection i...
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05/30/2019
Counting and Segmenting Sorghum Heads
Phenotyping is the process of measuring an organism's observable traits....
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03/15/2019
Crowd Counting with Decomposed Uncertainty
Research in neural networks in the field of computer vision has achieved...
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12/21/2018
Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs
Graph pattern matching algorithms to handle million-scale dynamic graphs...
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08/31/2018
Directed Exploration in PAC Model-Free Reinforcement Learning
We study an exploration method for model-free RL that generalizes the co...
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07/19/2017
