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Found 12 papers, 6 papers with code
Taming False Positives in Out-of-Distribution Detection with Human Feedback
Empirical evaluation of our system on synthetic and benchmark OOD datasets shows that our method can maintain FPR at most $5\%$ while maximizing TPR.
Pearls from Pebbles: Improved Confidence Functions for Auto-labeling
We develop a tractable version of the framework to obtain \texttt{Colander} (Confidence functions for Efficient and Reliable Auto-labeling), a new post-hoc method specifically designed to maximize performance in TBAL systems.
Metric Learning from Limited Pairwise Preference Comparisons
We study whether the metric can still be recovered, even though it is known that learning individual ideal items is now no longer possible.
Limitations of Face Image Generation
In particular, their ability to synthesize and modify human faces has spurred research into using generated face images in both training data augmentation and model performance assessments.
Promises and Pitfalls of Threshold-based Auto-labeling
Given the long shelf-life and diverse usage of the resulting datasets, understanding when the data obtained by such auto-labeling systems can be relied on is crucial.
One for All: Simultaneous Metric and Preference Learning over Multiple Users
This paper investigates simultaneous preference and metric learning from a crowd of respondents.
Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics
This paper investigates the theoretical and empirical performance of Fisher-Pitman-type permutation tests for assessing the equality of unknown Poisson mixture distributions.
Estimating the number and effect sizes of non-null hypotheses
We study the problem of estimating the distribution of effect sizes (the mean of the test statistic under the alternate hypothesis) in a multiple testing setting.
Optimal Estimation of Change in a Population of Parameters
Provided these paired observations, $\{(X_i, Y_i) \}_{i=1}^N$, our goal is to accurately estimate the \emph{distribution of the change in parameters}, $\delta_i := q_i - p_i$, over the population and properties of interest like the \emph{$\ell_1$-magnitude of the change} with sparse observations ($t\ll N$).
Maximum Likelihood Estimation for Learning Populations of Parameters
Precisely, for sufficiently large $N$, the MLE achieves the information theoretic optimal error bound of $\mathcal{O}(\frac{1}{t})$ for $t < c\log{N}$, with regards to the earth mover's distance (between the estimated and true distributions).
Crowdsourced Clustering: Querying Edges vs Triangles
When a generative model for the data is available (and we consider a few of these) we determine the cost of a query by its entropy; when such models do not exist we use the average response time per query of the workers as a surrogate for the cost.
Graph Clustering With Missing Data: Convex Algorithms and Analysis
We consider the problem of finding clusters in an unweighted graph, when the graph is partially observed.
