Proposes a model-free, weighted risk-sensitive RL algorithm balancing reward and error state probability, validated on continuous and discrete control tasks.

P. Geibel, F. Wysotzki

Proposes Gossip learning with linear models using random walks and model averaging, enabling privacy-preserving, low-communication distributed classification.

Róbert Ormándi, István Hegedüs, Márk Jelasity

Proposes a unified neural network framework leveraging large-scale unlabeled data for multiple NLP tasks, achieving state-of-the-art results without task-specific engineering.

Ronan Collobert, Jason Weston, Leon Bottou et al.

Bayesian optimization efficiently maximizes expensive black-box functions using Gaussian processes and acquisition functions, reducing sample complexity.

Eric Brochu, Vlad M. Cora, Nando de Freitas

Introduces LinUCB, a linear contextual Bandit algorithm, achieving 12.5% click rate lift on Yahoo! dataset, suitable for large-scale personalized news recommendation.

Lihong Li, Wei Chu, John Langford et al.

Exp4.P algorithm achieves supervised learning-like guarantees in contextual bandit problems, significantly reducing regret.

Alina Beygelzimer, John Langford, Lihong Li et al.

Proposes a spectral matrix completion algorithm achieving O(rn) sample efficiency with provable error bounds.

Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh

Linearly Parameterized Bandits use exploration-exploitation strategy to achieve Θ(r√T) cumulative regret and Bayes risk.

Paat Rusmevichientong, John N. Tsitsiklis

Proposes a kernel-based two-sample test framework (MMD) for efficient distribution comparison with strong theoretical guarantees.

Arthur Gretton, Karsten Borgwardt, Malte J. Rasch et al.

Conformal prediction provides confidence sets with guaranteed coverage probability in online settings, applicable to models like SVM and ridge regression.

Glenn Shafer, Vladimir Vovk