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Found 15 papers, 7 papers with code
GrowSpace: Learning How to Shape Plants
We argue that plant responses to an environmental stimulus are a good example of a real-world problem that can be approached within a reinforcement learning (RL)framework.
Sequential Automated Machine Learning: Bandits-driven Exploration using a Collaborative Filtering Representation
The goal of Automated Machine Learning (AutoML) is to make Machine Learning (ML) tools more accessible.
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments
We therefore use our case study of the ubiquitous two-arm binary reward setting to empirically investigate the impact of using Thompson Sampling instead of uniform random assignment.
Comparison of pharmacist evaluation of medication orders with predictions of a machine learning model
The objective of this work was to assess the clinical performance of an unsupervised machine learning model aimed at identifying unusual medication orders and pharmacological profiles.
Deep interpretability for GWAS
Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases.
A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well
In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings.
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
We propose $\tt RandUCB$, a bandit strategy that builds on theoretically derived confidence intervals similar to upper confidence bound (UCB) algorithms, but akin to Thompson sampling (TS), it uses randomization to trade off exploration and exploitation.
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, where the agent can easily become trapped into suboptimal solutions.
Leveraging exploration in off-policy algorithms via normalizing flows
The ability to discover approximately optimal policies in domains with sparse rewards is crucial to applying reinforcement learning (RL) in many real-world scenarios.
Temporal Regularization for Markov Decision Process
Several applications of Reinforcement Learning suffer from instability due to high variance.
Temporal Regularization in Markov Decision Process
Several applications of Reinforcement Learning suffer from instability due to high variance.
On-line Adaptative Curriculum Learning for GANs
We argue that less expressive discriminators are smoother and have a general coarse grained view of the modes map, which enforces the generator to cover a wide portion of the data distribution support.
Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy
More specifically, we are proposing a system based on a deep neural network that can provide a quantitative quality measure of a STED image of neuronal structures given as input.
Streaming kernel regression with provably adaptive mean, variance, and regularization
The variance of the noise is not assumed to be known.
Estimating Quality in Multi-Objective Bandits Optimization
The question is: how good do estimations of these objectives have to be in order for the solution maximizing the preference function to remain unchanged?
