
ESENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning
We introduce ESENAS, a simple neural architecture search (NAS) algorith...
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Fairness with Continuous Optimal Transport
Whilst optimal transport (OT) is increasingly being recognized as a powe...
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Regret Bound Balancing and Elimination for Model Selection in Bandits and RL
We propose a simple model selection approach for algorithms in stochasti...
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Online Model Selection for Reinforcement Learning with Function Approximation
Deep reinforcement learning has achieved impressive successes yet often ...
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Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian
Over the last decade, a single algorithm has changed many facets of our ...
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Accelerated Message Passing for EntropyRegularized MAP Inference
Maximum a posteriori (MAP) inference in discretevalued Markov random fi...
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On Optimism in ModelBased Reinforcement Learning
The principle of optimism in the face of uncertainty is prevalent throug...
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Stochastic Bandits with Linear Constraints
We study a constrained contextual linear bandit setting, where the goal ...
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Regret Balancing for Bandit and RL Model Selection
We consider model selection in stochastic bandit and reinforcement learn...
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Learning the Truth From Only One Side of the Story
Learning under onesided feedback (i.e., where examples arrive in an onl...
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Stochastic Flows and Geometric Optimization on the Orthogonal Group
We present a new class of stochastic, geometricallydriven optimization ...
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Robustness Guarantees for Mode Estimation with an Application to Bandits
Mode estimation is a classical problem in statistics with a wide range o...
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Model Selection in Contextual Stochastic Bandit Problems
We study model selection in stochastic bandit problems. Our approach rel...
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On Thompson Sampling with Langevin Algorithms
Thompson sampling is a methodology for multiarmed bandit problems that ...
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Ready Policy One: World Building Through Active Learning
ModelBased Reinforcement Learning (MBRL) offers a promising direction f...
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Effective Diversity in PopulationBased Reinforcement Learning
Maintaining a population of solutions has been shown to increase explora...
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ESMAML: Simple HessianFree Meta Learning
We introduce ESMAML, a new framework for solving the model agnostic met...
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Wasserstein Fair Classification
We propose an approach to fair classification that enforces independence...
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Reinforcement Learning with Chromatic Networks
We present a new algorithm for finding compact neural networks encoding ...
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Approximate SheraliAdams Relaxations for MAP Inference via Entropy Regularization
Maximum a posteriori (MAP) inference is a fundamental computational para...
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Wasserstein Reinforcement Learning
We propose behaviordriven optimization via Wasserstein distances (WDs) ...
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Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes
We propose a new class of structured methods for Monte Carlo (MC) sampli...
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Adaptive SampleEfficient Blackbox Optimization via ESactive Subspaces
We present a new algorithm ASEBO for conducting optimization of highdim...
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When random search is not enough: SampleEfficient and NoiseRobust Blackbox Optimization of RL Policies
Interest in derivativefree optimization (DFO) and "evolutionary strateg...
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GenOja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation
In this paper, we study the problems of principal Generalized Eigenvecto...
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Online learning with kernel losses
We present a generalization of the adversarial linear bandits framework,...
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A note on reinforcement learning with Wasserstein distance regularisation, with applications to multipolicy learning
In this note we describe an application of Wasserstein distance to Reinf...
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Aldo Pacchiano
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