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Found 7 papers, 1 papers with code
Optimal Sequential Maximization: One Interview is Enough!
To derive these results we consider a probabilistic setting where several candidates for a position are asked multiple questions with the goal of finding who has the highest probability of answering interview questions correctly.
AdaCliP: Adaptive Clipping for Private SGD
Motivated by this, differentially private stochastic gradient descent (SGD) algorithms for training machine learning models have been proposed.
On Learning Markov Chains
We consider two problems related to the min-max risk (expected loss) of estimating an unknown $k$-state Markov chain from its $n$ sequential samples: predicting the conditional distribution of the next sample with respect to the KL-divergence, and estimating the transition matrix with respect to a natural loss induced by KL or a more general $f$-divergence measure.
The Limits of Maxing, Ranking, and Preference Learning
We present a comprehensive understanding of three important problems in PAC preference learning: maximum selection (maxing), ranking, and estimating all pairwise preference probabilities, in the adaptive setting.
The power of absolute discounting: all-dimensional distribution estimation
Minimax optimality is too pessimistic to remedy this issue.
Maxing and Ranking with Few Assumptions
PAC maximum selection (maxing) and ranking of $n$ elements via random pairwise comparisons have diverse applications and have been studied under many models and assumptions.
Maximum Selection and Ranking under Noisy Comparisons
We consider $(\epsilon,\delta)$-PAC maximum-selection and ranking for general probabilistic models whose comparisons probabilities satisfy strong stochastic transitivity and stochastic triangle inequality.
