Education
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
Equipped with this algorithm, we demonstrate reincarnating RL's gains
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers
Quantile (and, more generally, KL) regret bounds, such as those achieved by NormalHedge (Chaudhuri, Freund, and Hsu 2009) and its variants, relax the goal of competing against the best individual expert to only competing against a majority of experts on adversarial data.
Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations
Each attribute combination, called an'arm', is associated with a Beta density from which the service's accuracy is sampled. We expect the GP to smooth the parameters of the Beta density over related arms to mitigate sparsity.
Private learning implies quantum stability
Motivated by learning theory, Aaronson et al. introduced many (weaker) learning models: the
Policy Learning Using Weak Supervision
Most existing works require agents to receive high-quality supervision signals, e.g., reward or
FasterRisk: Fast and Accurate Interpretable Risk Scores
Recent work used mathematical programming, which is computationally slow.
