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Found 24 papers, 10 papers with code
WAPTS: A Weighted Allocation Probability Adjusted Thompson Sampling Algorithm for High-Dimensional and Sparse Experiment Settings
Aiming for more effective experiment design, such as in video content advertising where different content options compete for user engagement, these scenarios can be modeled as multi-arm bandit problems.
On shallow planning under partial observability
Formulating a real-world problem under the Reinforcement Learning framework involves non-trivial design choices, such as selecting a discount factor for the learning objective (discounted cumulative rewards), which articulates the planning horizon of the agent.
Neural Active Learning Meets the Partial Monitoring Framework
We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the costly acquisition of information (labelled observations) and the cost of prediction errors.
Randomized Confidence Bounds for Stochastic Partial Monitoring
To advocate for the adoption of the PM framework, we design a use case on the real-world problem of monitoring the error rate of any deployed classification system.
Association Rules Mining with Auto-Encoders
Classical association rule mining algorithms have several limitations, especially with regards to their high execution times and number of rules produced.
Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients
The focus of this study is on predicting the pain and tiredness level of a patient post their diagnosis.
Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy
Polypharmacy, most often defined as the simultaneous consumption of five or more drugs at once, is a prevalent phenomenon in the older population.
Cambrian Explosion Algorithm for Multi-Objective Association Rules Mining
Association rule mining is one of the most studied research fields of data mining, with applications ranging from grocery basket problems to highly explainable classification systems.
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization
TS-PostDiff takes a Bayesian approach to mixing TS and Uniform Random (UR): the probability a participant is assigned using UR allocation is the posterior probability that the difference between two arms is 'small' (below a certain threshold), allowing for more UR exploration when there is little or no reward to be gained.
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?
