7 accepted papers
Maximum selection under probabilistic queries \emph{(probabilistic maximization)} is a fundamental algorithmic problem arising in numerous theoretical and practical contexts. We derive the first query-optimal sequential algorithm for probabilistic-maximization. Departing from previous assumptions, t…
N-gram models remain a fundamental component of language modeling. In data-scarce regimes, they are a strong alternative to neural models. Even when not used as-is, recent work shows they can regularize neural models. Despite this success, the effectiveness of one of the best N-gram smoothing method…
We present a comprehensive understanding of three important problems in PAC preference learning: maximum selection (maxing), ranking, and estimating
We consider $(\epsilon,\delta)$-PAC maximum-selection and ranking using pairwise comparisons for general probabilistic models whose comparison probabilities satisfy strong stochastic transitivity and stochastic triangle inequality. Modifying the popular knockout tournament, we propose a simple maxim…
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. With just one simple natural assumption: strong stochast…
Categorical models are a natural fit for many problems. When learning the distribution of categories from samples, high-dimensionality may dilute the data. Minimax optimality is too pessimistic to remedy this issue. A serendipitously discovered estimator, absolute discounting, corrects empirical fre…
Utilizing the structure of a probabilistic model can significantly increase its learning speed. Motivated by several recent applications, in particular bigram models in language processing, we consider learning low-rank conditional probability matrices under expected KL-risk. This choice makes smoot…