
Dense Incremental MetricSemantic Mapping for MultiAgent Systems via Sparse Gaussian Process Regression
We develop an online probabilistic metricsemantic mapping approach for ...
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Composable Learning with Sparse Kernel Representations
We present a reinforcement learning algorithm for learning sparse nonpa...
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Sparse Representations of Positive Functions via Projected PseudoMirror Descent
We consider the problem of expected risk minimization when the populatio...
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A Markov Decision Process Approach to Active Meta Learning
In supervised learning, we fit a single statistical model to a given dat...
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Variational Policy Gradient Method for Reinforcement Learning with General Utilities
In recent years, reinforcement learning (RL) systems with general goals ...
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Balancing Rates and Variance via Adaptive BatchSize for Stochastic Optimization Problems
Stochastic gradient descent is a canonical tool for addressing stochasti...
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Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck
Gaussian processes provide a framework for nonlinear nonparametric Bayes...
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Policy Gradient using Weak Derivatives for Reinforcement Learning
This paper considers policy search in continuous stateaction reinforcem...
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Distributed Beamforming for Agents with Localization Errors
We consider a scenario in which a group of agents aim to collectively tr...
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Efficient Gaussian Process Bandits by Believing only Informative Actions
Bayesian optimization is a framework for global search via maximum a pos...
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Asynchronous and Parallel Distributed Pose Graph Optimization
We present Asynchronous Stochastic Parallel Pose Graph Optimization (ASA...
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Cautious Reinforcement Learning via Distributional Risk in the Dual Domain
We study the estimation of risksensitive policies in reinforcement lear...
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On the Sample Complexity of ActorCritic Method for Reinforcement Learning with Function Approximation
Reinforcement learning, mathematically described by Markov Decision Prob...
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Optimally Compressed Nonparametric Online Learning
Batch training of machine learning models based on neural networks is no...
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Approximate Shannon Sampling in Importance Sampling: Nearly Consistent Finite Particle Estimates
In Bayesian inference, we seek to compute information about random varia...
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Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony
An open challenge in supervised learning is conceptual drift: a data poi...
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Adaptive Kernel Learning in Heterogeneous Networks
We consider the framework of learning over decentralized networks, where...
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Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies
Policy gradient (PG) methods are a widely used reinforcement learning me...
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Nonparametric Stochastic Compositional Gradient Descent for QLearning in Continuous Markov Decision Problems
We consider Markov Decision Problems defined over continuous state and a...
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Decentralized Online Learning with Kernels
We consider multiagent stochastic optimization problems over reproducin...
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A Class of Parallel Doubly Stochastic Algorithms for LargeScale Learning
We consider learning problems over training sets in which both, the numb...
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Decentralized Dynamic Discriminative Dictionary Learning
We consider discriminative dictionary learning in a distributed online s...
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Alec Koppel
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http://koppel.netlify.app/