1 code implementation • 25 Apr 2024 • Harit Vishwakarma, Heguang Lin, Ramya Korlakai Vinayak
Empirical evaluation of our system on synthetic and benchmark OOD datasets shows that our method can maintain FPR at most $5\%$ while maximizing TPR.
no code implementations • 24 Apr 2024 • Harit Vishwakarma, Reid, Chen, Sui Jiet Tay, Satya Sai Srinath Namburi, Frederic Sala, Ramya Korlakai Vinayak
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.
1 code implementation • 28 Mar 2024 • Zhi Wang, Geelon So, Ramya Korlakai Vinayak
We study whether the metric can still be recovered, even though it is known that learning individual ideal items is now no longer possible.
1 code implementation • 13 Sep 2023 • Harrison Rosenberg, Shimaa Ahmed, Guruprasad V Ramesh, Ramya Korlakai Vinayak, Kassem Fawaz
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.
2 code implementations • NeurIPS 2023 • Harit Vishwakarma, Heguang Lin, Frederic Sala, Ramya Korlakai Vinayak
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.
2 code implementations • 7 Jul 2022 • Gregory Canal, Blake Mason, Ramya Korlakai Vinayak, Robert Nowak
This paper investigates simultaneous preference and metric learning from a crowd of respondents.
no code implementations • 6 Jun 2021 • Zhen Miao, Weihao Kong, Ramya Korlakai Vinayak, Wei Sun, Fang Han
This paper investigates the theoretical and empirical performance of Fisher-Pitman-type permutation tests for assessing the equality of unknown Poisson mixture distributions.
1 code implementation • ICML 2020 • Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson
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.
no code implementations • 28 Nov 2019 • Ramya Korlakai Vinayak, Weihao Kong, Sham M. Kakade
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 ($t\ll N$).
no code implementations • 12 Feb 2019 • Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham M. Kakade
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).
no code implementations • NeurIPS 2016 • Ramya Korlakai Vinayak, Babak Hassibi
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.
no code implementations • NeurIPS 2014 • Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi
We consider the problem of finding clusters in an unweighted graph, when the graph is partially observed.