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Found 6 papers, 1 papers with code
Unified Risk Analysis for Weakly Supervised Learning
The analysis component of the framework, viewed as a decontamination process, provides a systematic method of conducting risk rewrite.
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models
Although the uniform prior is shown to be optimal, we highlight the inherent limitation of its optimality, which is limited to specific parameterizations and emphasizes the significance of the invariance property of priors.
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits
In addition to the empirical performance, TS has been shown to achieve asymptotic problem-dependent lower bounds in several models.
Hyper-parameter Tuning under a Budget Constraint
We study a budgeted hyper-parameter tuning problem, where we optimize the tuning result under a hard resource constraint.
Federated Multi-Task Learning
Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices.
An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits
We present an algorithm that achieves almost optimal pseudo-regret bounds against adversarial and stochastic bandits.
