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Submodular functions, crucial for various applications, often lack practical learning methods for their acquisition.
A law of comparative judgment
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Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry · 1952
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Henry Scheffe · 1952
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Stimulus and response generalization: A stochastic model relating generalization to distance in psychological space
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Individual choice behavior: A theoretical analysis
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The analysis of variance and pairwise scaling
Gordon G Bechtel · 1967
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A covariance analysis of multiple paired comparisons
Gordon G Bechtel · 1971
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Combinatorial optimization: networks and matroids
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An analysis of approximations for maximizing submodular set functions—i
George L Nemhauser, Laurence A Wolsey, and Marshall L Fisher · 1978
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Matroid matching and some applications
László Lovász · 1980
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Decomposition of submodular functions
William H Cunningham · 1983
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Optimal attack and reinforcement of a network
William H Cunningham · 1985
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Random sampling with a reservoir
Jeffrey S. Vitter · 1985
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Psychometric theory third edition. mcgraw-hili
JC Nunnally and IH Bernstein · 1994
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Efficient method for paired comparison
D Amnon Silverstein and Joyce E Farrell · 2001
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Combinatorial optimization: polyhedra and efficiency
Alexander Schrijver · 2003
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Submodular functions and optimization
Satoru Fujishige · 2005
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Accelerated greedy algorithms for maximizing submodular set functions
Michel Minoux · 2005
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Learning structured prediction models: A large margin approach
Ben Taskar, Vassil Chatalbashev, Daphne Koller, and Carlos Guestrin · 2005
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Cost-effective outbreak detection in networks
Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne VanBriesen, and Natalie Glance · 2007
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Approximating submodular functions everywhere
Michel X Goemans, Nicholas JA Harvey, Satoru Iwata, and Vahab Mirrokni · 2009
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Non-monotone submodular maximization under matroid and knapsack constraints
Jon Lee, Vahab S Mirrokni, Viswanath Nagarajan, and Maxim Sviridenko · 2009
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How to select a good training-data subset for transcription: Submodular active selection for sequences
H. Lin and J. Bilmes · 2009
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Distance metric learning for large margin nearest neighbor classification
Kilian Q Weinberger and Lawrence K Saul · 2009
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Structured sparsity-inducing norms through submodular functions
F. Bach · 2010
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Adaptive submodularity: A new approach to active learning and stochastic optimization
Daniel Golovin and Andreas Krause · 2010
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An application of the submodular principal partition to training data subset selection
H. Lin and J. Bilmes · 2010
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Efficient minimization of decomposable submodular functions
Peter Stobbe and Andreas Krause · 2010
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Learning valuation functions
Maria Florina Balcan, Florin Constantin, Satoru Iwata, and Lei Wang · 2011
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Maximizing a monotone submodular function subject to a matroid constraint
Gruia Calinescu, Chandra Chekuri, Martin Pal, and Jan Vondrák · 2011
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Online submodular set cover, ranking, and repeated active learning
Andrew Guillory and Jeff A Bilmes · 2011
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Submodularity beyond submodular energies: coupling edges in graph cuts
Stefanie Jegelka and Jeff Bilmes · 2011
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A class of submodular functions for document summarization
Hui Lin and Jeff Bilmes · 2011
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Matroid Theory: Second Edition
J.G. Oxley · 2011
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Large-margin learning of submodular summarization methods
R. Sipos, P. Shivaswamy, and T. Joachims · 2011
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael U Gutmann and Aapo Hyvärinen · 2012
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Determinantal point processes for machine learning
Alex Kulesza, Ben Taskar, et al · 2012
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Learning mixtures of submodular shells with application to document summarization
Hui Lin and Jeff A Bilmes · 2012
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Large-margin learning of submodular summarization models
Ruben Sipos, Pannaga Shivaswamy, and Thorsten Joachims · 2012
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Fast semidifferential-based submodular function optimization
Rishabh Iyer, Stefanie Jegelka, and Jeff Bilmes · 2013
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Submodular optimization with submodular cover and submodular knapsack constraints
Rishabh K Iyer and Jeff A Bilmes · 2013
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Submodular feature selection for high-dimensional acoustic score spaces
Yuzong Liu, Kai Wei, Katrin Kirchhoff, Yisong Song, and Jeff Bilmes · 2013
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Design of experiments in nonlinear models
Luc Pronzato and Andrej Pázman · 2013
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The psycho-biology of language: An introduction to dynamic philology
George Kingsley Zipf · 2013
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Deep set prediction networks
Yan Zhang, Jonathon Hare, and Adam Prugel-Bennett · 2019
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Deep submodular network: An application to multi-document summarization
Alireza Ghadimi and Hamid Beigy · 2020
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Set functions for time series
Max Horn, Michael Moor, Christian Bock, Bastian Rieck, and Karsten Borgwardt · 2020
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Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 2020
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A Practical Online Framework for Extracting Running Video Summaries under a Fixed Memory Budget
Chandrashekhar Lavania, Kai Wei, Rishabh Iyer, and Jeff Bilmes · 2020
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A trainable optimal transport embedding for feature aggregation and its relationship to attention
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Submodular maximization with cardinality constraints
Niv Buchbinder, Moran Feldman, Joseph Naor, and Roy Schwartz · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Learning mixtures of submodular functions for image collection summarization
Sebastian Tschiatschek, Rishabh K Iyer, Haochen Wei, and Jeff A Bilmes · 2014
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Unsupervised submodular subset selection for speech data
Kai Wei, Yuzong Liu, Katrin Kirchhoff, and Jeff Bilmes · 2014
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Video summarization by learning submodular mixtures of objectives
Michael Gygli, Helmut Grabner, and Luc Van Gool · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Grégoire Mialon, Dexiong Chen, Alexandre d’Aspremont, and Julien Mairal · 2020
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Coresets for data-efficient training of machine learning models
Baharan Mirzasoleiman, Jeff Bilmes, and Jure Leskovec · 2020
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Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2020
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Submodularity in action: From machine learning to signal processing applications
Ehsan Tohidi, Rouhollah Amiri, Mario Coutino, David Gesbert, Geert Leus, and Amin Karbasi · 2020
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A survey of inverse reinforcement learning: Challenges, methods and progress
Saurabh Arora and Prashant Doshi · 2021
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Adaptivity in adaptive submodularity
Hossein Esfandiari, Amin Karbasi, and Vahab Mirrokni · 2021
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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2021
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A loss curvature perspective on training instability in deep learning
Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George Dahl, Zachary Nado, and Orhan Firat · 2021
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Regularized submodular maximization at scale
Ehsan Kazemi, Shervin Minaee, Moran Feldman, and Amin Karbasi · 2021
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Grad-match: Gradient matching based data subset selection for efficient deep model training
Krishnateja Killamsetty, Sivasubramanian Durga, Ganesh Ramakrishnan, Abir De, and Rishabh Iyer · 2021
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Glister: Generalization based data subset selection for efficient and robust learning
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, and Rishabh Iyer · 2021
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Similar: Submodular information measures based active learning in realistic scenarios, 2021
Suraj Kothawade, Nathan Beck, Krishnateja Killamsetty, and Rishabh Iyer · 2021
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Learning transferable visual models from natural language supervision, 2021
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Near-optimal multi-perturbation experimental design for causal structure learning
Scott Sussex, Caroline Uhler, and Andreas Krause · 2021
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A survey of inverse reinforcement learning
Stephen Adams, Tyler Cody, and Peter A Beling · 2022
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Submodularity in machine learning and artificial intelligence, 2022
Jeff Bilmes · 2022
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Neural estimation of submodular functions with applications to differentiable subset selection
Abir De and Soumen Chakrabarti · 2022
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The power of subsampling in submodular maximization
Christopher Harshaw, Ehsan Kazemi, Moran Feldman, and Amin Karbasi · 2022
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Differentiable expectation-maximization for set representation learning
Minyoung Kim · 2022
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Measuring personality when stakes are high: Are graded paired comparisons a more reliable alternative to traditional forced-choice methods?
Harriet Lingel, Paul Bürkner, Klaus Melchers, and Niklas Schulte · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Efficient training of low-curvature neural networks
Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, and François Fleuret · 2022
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A cookbook of self-supervised learning
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Deep reinforcement learning from human preferences, 2023
Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2023
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Unihpe: Towards unified human pose estimation via contrastive learning
Zhongyu Jiang, Wenhao Chai, Lei Li, Zhuoran Zhou, Cheng-Yen Yang, and Jenq-Neng Hwang · 2023
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Submodular reinforcement learning
Manish Prajapat, Mojmír Mutnỳ, Melanie N Zeilinger, and Andreas Krause · 2023
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Streaming active learning with deep neural networks, 2023
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, and Jordan T. Ash · 2023
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Is rlhf more difficult than standard rl? a theoretical perspective
Yuanhao Wang, Qinghua Liu, and Chi Jin · 2023
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Provable offline reinforcement learning with human feedback
Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D Lee, and Wen Sun · 2023
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Deep class-incremental learning: A survey, 2023
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Principled reinforcement learning with human feedback from pairwise or k k -wise comparisons
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An experimental design framework for label-efficient supervised finetuning of large language models, 2024
Gantavya Bhatt, Yifang Chen, Arnav M. Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeffrey Bilmes, Simon S. Du, Kevin Jamieson, Jordan T. Ash, and Robert D. Nowak · 2024
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Repeated random sampling for minimizing the time-to-accuracy of learning
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