README.md

metadata

license: mit
task_categories:
  - text-classification
language:
  - en
tags:
  - research
  - machine-learning
  - reinforcement-learning
  - bandits
  - arxiv:2604.19672
  - arxiv:2604.19695
  - arxiv:2604.19698
  - arxiv:2604.20074
  - arxiv:2604.20077
  - arxiv:2604.20078
  - arxiv:2604.21432
  - arxiv:2604.21462
  - arxiv:2604.21956
  - arxiv:2604.22385
  - arxiv:2604.22386
  - arxiv:2604.24537
  - arxiv:2604.24545
  - arxiv:2604.24555
  - arxiv:2604.25269
  - arxiv:2604.25271
  - arxiv:2604.25272
  - arxiv:2604.26550
  - arxiv:2604.26654
  - arxiv:2604.26818
  - arxiv:2604.27562
  - arxiv:2604.27563
  - arxiv:2604.27564
pretty_name: Michal Valko Research Papers

Michal Valko Research Papers

Selected research papers by Michal Valko on bandits, reinforcement learning, and online learning.

Papers

• Bayesian policy gradient and actor-critic algorithms
• Online semi-supervised perception: Real-time learning without explicit feedback
• Learning from a single labeled face and a stream of unlabeled data
• Semi-supervised learning with max-margin graph cuts
• Evolutionary feature selection for spiking neural network pattern classifiers
• Large-scale semi-supervised learning with online spectral graph sparsification
• Online learning with Erdős-Rényi side-observation graphs
• Online combinatorial optimization with stochastic decision sets and adversarial losses
• Spectral bandits
• Efficient learning by implicit exploration in bandit problems with side observations
• Extreme bandits
• Stochastic simultaneous optimistic optimization
• Conditional anomaly detection using soft harmonic functions: An application to clinical alerting
• Pliable rejection sampling
• Pack only the essentials: Adaptive dictionary learning for kernel ridge regression
• Conditional anomaly detection with soft harmonic functions
• A single algorithm for both restless and rested rotting bandits
• Maximum entropy semi-supervised inverse reinforcement learning
• Analysis of Nyström method with sequential ridge leverage scores
• Improved large-scale graph learning through ridge spectral sparsification
• Planning in entropy-regularized Markov decision processes and games
• On two ways to use determinantal point processes for Monte Carlo integration
• Budgeted online influence maximization