no code implementations • 2 Jun 2023 • Alessio Mazzetto, Reza Esfandiarpoor, Eli Upfal, Stephen H. Bach
In particular, at each step, our algorithm guarantees an estimation of the current accuracies of the weak supervision sources over a window of past observations that minimizes a trade-off between the error due to the variance of the estimation and the error due to the drift.
no code implementations • 5 Feb 2023 • Alessio Mazzetto, Eli Upfal
We study nonparametric density estimation in non-stationary drift settings.
1 code implementation • 25 May 2022 • Alessio Mazzetto, Cristina Menghini, Andrew Yuan, Eli Upfal, Stephen H. Bach
We develop the first non-trivial lower bound on the worst-case error of the best map from attributes to classes for this setting, even with perfect attribute detectors.
no code implementations • NeurIPS 2021 • Shahrzad Haddadan, Yue Zhuang, Cyrus Cousins, Eli Upfal
While the cooling schedule in these algorithms is adaptive, the mean estimation computations use MCMC as a black-box to draw approximate samples.
no code implementations • 4 Oct 2019 • Lorenzo De Stefani, Eli Upfal
While standard statistical inference techniques and machine learning generalization bounds assume that tests are run on data selected independently of the hypotheses, practical data analysis and machine learning are usually iterative and adaptive processes where the same holdout data is often used for testing a sequence of hypotheses (or models), which may each depend on the outcome of the previous tests on the same data.
no code implementations • 18 Dec 2018 • Clayton Sanford, Cyrus Cousins, Eli Upfal
We frame the problem of selecting an optimal audio encoding scheme as a supervised learning task.
no code implementations • 24 Aug 2018 • Yeounoh Chung, Peter J. Haas, Eli Upfal, Tim Kraska
Over the past decades, researchers and ML practitioners have come up with better and better ways to build, understand and improve the quality of ML models, but mostly under the key assumption that the training data is distributed identically to the testing data.
no code implementations • 8 Jul 2018 • Kim Albertsson, Piero Altoe, Dustin Anderson, John Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Bjorn Burkle, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Yi-fan Chen, Taylor Childers, Yann Coadou, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Andrea De Simone, Javier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ulrich Heintz, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark Neubauer, Harvey Newman, Sydney Otten, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Wei Sun, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Justin Vasel, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Kun Yang, Omar Zapata
In this document we discuss promising future research and development areas for machine learning in particle physics.
BIG-bench Machine Learning Vocal Bursts Intensity Prediction
1 code implementation • 18 May 2016 • Matteo Ceccarello, Andrea Pietracaprina, Geppino Pucci, Eli Upfal
Given a dataset of points in a metric space and an integer $k$, a diversity maximization problem requires determining a subset of $k$ points maximizing some diversity objective measure, e. g., the minimum or the average distance between two points in the subset.
Distributed, Parallel, and Cluster Computing
1 code implementation • 24 Feb 2016 • Lorenzo De Stefani, Alessandro Epasto, Matteo Riondato, Eli Upfal
We present TRI\`EST, a suite of one-pass streaming algorithms to compute unbiased, low-variance, high-quality approximations of the global and local (i. e., incident to each vertex) number of triangles in a fully-dynamic graph represented as an adversarial stream of edge insertions and deletions.
Data Structures and Algorithms Databases G.2.2; H.2.8
no code implementations • 8 Sep 2015 • Ahmad Mahmoody, Evgenios M. Kornaropoulos, Eli Upfal
We formulate and study a fundamental search and detection problem, Schedule Optimization, motivated by a variety of real-world applications, ranging from monitoring content changes on the web, social networks, and user activities to detecting failure on large systems with many individual machines.
no code implementations • 4 Dec 2013 • Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal
In this work we study a very general setting for the multi-armed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric.
no code implementations • NeurIPS 2008 • Deepayan Chakrabarti, Ravi Kumar, Filip Radlinski, Eli Upfal
In our model, arms have (stochastic) lifetime after which they expire.
2 code implementations • 29 Sep 2008 • Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal
In this work we study a very general setting for the multi-armed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric.