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Multi-objective optimization (MOO) problems are prevalent in machine learning.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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Multiobjective genetic algorithms with application to control engineering problems
Carlos Manuel Mira da Fonseca · 1995
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Normal-boundary intersection: A new method for generating the pareto surface in nonlinear multicriteria optimization problems
Indraneel Das and John E Dennis · 1998
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach
Eckart Zitzler and Lothar Thiele · 1999
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Steepest descent methods for multicriteria optimization
Jörg Fliege and Benar Fux Svaiter · 2000
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The sarcos dataset, 2000
Sethu Vijayakumar · 2000
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Comparison of multiobjective evolutionary algorithms: Empirical results
Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele · 2000
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A fast and elitist multiobjective genetic algorithm: NSGA-II
Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and T. Meyarivan · 2002
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Convex optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe · 2004
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Multicriteria optimization , volume 491
Matthias Ehrgott · 2005
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Moea/d: A multiobjective evolutionary algorithm based on decomposition
Qingfu Zhang and Hui Li · 2007
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
I-Cheng Yeh and Che-hui Lien · 2009
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Multiple-gradient descent algorithm (mgda) for multiobjective optimization
Jean-Antoine Désidéri · 2012
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Fairness-aware classifier with prejudice remover regularizer
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2012
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints
Kalyanmoy Deb and Himanshu Jain · 2013
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Nonuniform covering method as applied to multicriteria optimization problems with guaranteed accuracy
Yu G Evtushenko and Mikhail Anatol’evich Posypkin · 2013
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A data-driven approach to predict the success of bank telemarketing
Sérgio Moro, Paulo Cortez, and Paulo Rita · 2014
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Policy gradient approaches for multi-objective sequential decision making
Simone Parisi, Matteo Pirotta, Nicola Smacchia, Luca Bascetta, and Marcello Restelli · 2014
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Multi-objective reinforcement learning using sets of pareto dominating policies
Kristof Van Moffaert and Ann Nowé · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Multi-objective reinforcement learning with continuous pareto frontier approximation
Matteo Pirotta, Simone Parisi, and Marcello Restelli · 2015
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Predictive entropy search for multi-objective bayesian optimization
Daniel Hernández-Lobato, Jose Hernandez-Lobato, Amar Shah, and Ryan Adams · 2016
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Multi-objective reinforcement learning through continuous pareto manifold approximation
Simone Parisi, Matteo Pirotta, and Marcello Restelli · 2016
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Enet: A deep neural network architecture for real-time semantic segmentation
Adam Paszke, Abhishek Chaurasia, Sangpil Kim, and E. Culurciello · 2016
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Inverse reinforcement learning through policy gradient minimization
Matteo Pirotta and Marcello Restelli · 2016
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Pareto frontier learning with expensive correlated objectives
Amar Shah and Zoubin Ghahramani · 2016
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Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
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Monocular depth estimation: A survey
Amlaan Bhoi · 2019
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You only train once: Loss-conditional training of deep networks
Alexey Dosovitskiy and Josip Djolonga · 2019
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Hypernetwork functional image representation
Sylwester Klocek, Lukasz Maziarka, Maciej Wolczyk, J. Tabor, J. Nowak, and M. Smieja · 2019
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Pareto multi-task learning
Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qing-Fu Zhang, and Sam Kwong · 2019
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End-to-end multi-task learning with attention
Shikun Liu, Edward Johns, and Andrew J Davison · 2019
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Yahav Bechavod and Katrina Ligett · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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David Ha, Andrew M. Dai, and Quoc V. Le · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
X. Huang and Serge J. Belongie · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Film: Visual reasoning with a general conditioning layer
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An overview of multi-task learning in deep neural networks, 2017
Sebastian Ruder · 2017
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Nsga-net: neural architecture search using multi-objective genetic algorithm
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Self-tuning networks: Bilevel optimization of hyperparameters using structured best-response functions
Matthew Mackay, Paul Vicol, Jonathan Lorraine, David Duvenaud, and Roger Grosse · 2019
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and A. Galstyan · 2019
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Hyper-graph-network decoders for block codes
Eliya Nachmani and L. Wolf · 2019
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Which tasks should be learned together in multi-task learning?
Trevor Standley, Amir R Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese · 2019
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A generalized algorithm for multi-objective reinforcement learning and policy adaptation
Runzhe Yang, Xingyuan Sun, and Karthik Narasimhan · 2019
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Fairness constraints: A flexible approach for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P Gummadi · 2019
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Controllable pareto multi-task learning
X. Lin, Zhiyuan Yang, Q. Zhang, and S. Kwong · 2020
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Accuracy and fairness trade-offs in machine learning: A stochastic multi-objective approach
Suyun Liu and Luis Nunes Vicente · 2020
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Efficient continuous pareto exploration in multi-task learning
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Multi-task learning with user preferences: Gradient descent with controlled ascent in pareto optimization
D. Mahapatra and V. Rajan · 2020
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Image segmentation using deep learning: A survey
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