Fetching the paper…
Reading the bibliography…
Recently, bi-level optimization (BLO) has taken center stage in some very exciting developments in the area of signal processing (SP) and machine learning (ML).
The theory of the market economy
Heinrich Von Stackelberg, · 1952
Earlier work this paper cites.
“Mathematical programs with optimization problems in the constraints,”
Jerome Bracken and James T. McGill, · 1973
Earlier work this paper cites.
“Defense applications of mathematical programs with optimization problems in the constraints,”
Jerome Bracken and James T McGill, · 1974
Earlier work this paper cites.
“Production and marketing decisions with multiple objectives in a competitive environment,”
Jerome Bracken and JT McGill, · 1978
Earlier work this paper cites.
Matrix Analysis
R. A. Horn and C. R. Johnson, · 1990
Earlier work this paper cites.
“A space-time correlation model for multielement antenna systems in mobile fading channels,”
Ali Abdi and Mostafa Kaveh, · 2002
Earlier work this paper cites.
The implicit function theorem: history, theory, and applications
Steven George Krantz and Harold R Parks, · 2002
Earlier work this paper cites.
“Lower bound on training-based channel estimation error for frequency-selective block-fading rayleigh mimo channels,”
Osvaldo Simeone and Umberto Spagnolini, · 2004
Earlier work this paper cites.
“Channel estimation for mimo-ofdm systems by modal analysis/filtering,”
Marcello Cicerone, Osvaldo Simeone, and Umberto Spagnolini, · 2006
Earlier work this paper cites.
“An overview of bilevel optimization,”
Benoît Colson, Patrice Marcotte, and Gilles Savard, · 2007
Earlier work this paper cites.
“Reverse-mode ad in a functional framework: Lambda the ultimate backpropagator,”
Barak A Pearlmutter and Jeffrey Mark Siskind, · 2008
Earlier work this paper cites.
“Conjugate gradient method,”
John L Nazareth, · 2009
Earlier work this paper cites.
“Learning multiple layers of features from tiny images,”
Alex Krizhevsky, Geoffrey Hinton, et al., · 2009
Earlier work this paper cites.
“Imagenet: A large-scale hierarchical image database,”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, · 2009
Earlier work this paper cites.
“A bi-level formulation for the combined dynamic equilibrium based traffic signal control,”
Satish Ukkusuri, Kien Doan, and HM Abdul Aziz, · 2013
Earlier work this paper cites.
“Stochastic first-and zeroth-order methods for nonconvex stochastic programming,”
S. Ghadimi and G. Lan, · 2013
Earlier work this paper cites.
“Saga: A fast incremental gradient method with support for non-strongly convex composite objectives,”
Aaron Defazio, Francis Bach, and Simon Lacoste-Julien, · 2014
Earlier work this paper cites.
“Explaining and harnessing adversarial examples,”
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy, · 2014
Earlier work this paper cites.
“Gradient-based hyperparameter optimization through reversible learning,”
Dougal Maclaurin, David Duvenaud, and Ryan Adams, · 2015
Earlier work this paper cites.
“Human-level concept learning through probabilistic program induction,”
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum, · 2015
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally, · 2015
Earlier work this paper cites.
“Learning both weights and connections for efficient neural network,”
Song Han, Jeff Pool, John Tran, and William Dally, · 2015
Earlier work this paper cites.
“Learning to learn by gradient descent by gradient descent,”
Marcin Andrychowicz, Misha Denil, et al., · 2016
Earlier work this paper cites.
“Optimization as a model for few-shot learning,”
Sachin Ravi and Hugo Larochelle, · 2016
Earlier work this paper cites.
Stephen Gould, Basura Fernando, Anoop Cherian, Peter Anderson, Rodrigo Santa Cruz, and Edison Guo, · 2016
Earlier work this paper cites.
“Introduction to online convex optimization,”
Elad Hazan et al., · 2016
Earlier work this paper cites.
“Scalable gradient-based tuning of continuous regularization hyperparameters,”
Jelena Luketina, Mathias Berglund, Klaus Greff, and Tapani Raiko, · 2016
Earlier work this paper cites.
“Matching networks for one shot learning,”
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al., · 2016
Earlier work this paper cites.
“The limitations of deep learning in adversarial settings,”
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami, · 2016
Earlier work this paper cites.
“Identity mappings in deep residual networks,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“A review on bilevel optimization: from classical to evolutionary approaches and applications,”
Ankur Sinha, Pekka Malo, and Kalyanmoy Deb, · 2017
Earlier work this paper cites.
“Towards poisoning of deep learning algorithms with back-gradient optimization,”
Luis Muñoz-González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil C Lupu, and Fabio Roli, · 2017
Earlier work this paper cites.
“Model-agnostic meta-learning for fast adaptation of deep networks,”
Chelsea Finn, Pieter Abbeel, and Sergey Levine, · 2017
Earlier work this paper cites.
“Forward and reverse gradient-based hyperparameter optimization,”
Luca Franceschi, Michele Donini, Paolo Frasconi, and Massimiliano Pontil, · 2017
Earlier work this paper cites.
“Trends and applications in stackelberg security games,”
Debarun Kar, Thanh H Nguyen, Fei Fang, Matthew Brown, Arunesh Sinha, Milind Tambe, and Albert Xin Jiang, · 2017
Earlier work this paper cites.
“Towards evaluating the robustness of neural networks,”
Nicholas Carlini and David Wagner, · 2017
Earlier work this paper cites.
“Towards deep learning models resistant to adversarial attacks,”
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu, · 2017
Earlier work this paper cites.
“Learning to optimize: Training deep neural networks for interference management,”
Haoran Sun, Xiangyi Chen, Qingjiang Shi, Mingyi Hong, Xiao Fu, and Nicholas D Sidiropoulos, · 2018
Earlier work this paper cites.
“A very brief introduction to machine learning with applications to communication systems,”
Osvaldo Simeone, · 2018
Earlier work this paper cites.
“Adversarial attacks on neural networks for graph data,”
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann, · 2018
Earlier work this paper cites.
“On first-order meta-learning algorithms,”
Alex Nichol, Joshua Achiam, and John Schulman, · 2018
Earlier work this paper cites.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros, · 2018
Earlier work this paper cites.
“Bilevel programming for hyperparameter optimization and meta-learning,”
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil, · 2018
Earlier work this paper cites.
“Darts: Differentiable architecture search,”
Hanxiao Liu, Karen Simonyan, and Yiming Yang, · 2018
Earlier work this paper cites.
“Transfer learning with neural automl,”
Catherine Wong, Neil Houlsby, Yifeng Lu, and Andrea Gesmundo, · 2018
Earlier work this paper cites.
“Progressive neural architecture search,”
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy, · 2018
Earlier work this paper cites.
“Approximation methods for bilevel programming,”
Saeed Ghadimi and Mengdi Wang, · 2018
Earlier work this paper cites.
“signsgd: Compressed optimisation for non-convex problems,”
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Animashree Anandkumar, · 2018
Earlier work this paper cites.
“signsgd via zeroth-order oracle,”
Sijia Liu · 2018
Cited alongside, same era.
“Spider: Near-optimal non-convex optimization via stochastic path-integrated differential estimator,”
Cong Fang, Chris Junchi Li, Zhouchen Lin, and Tong Zhang, · 2018
Cited alongside, same era.
“Data poisoning attacks on multi-task relationship learning,”
Mengchen Zhao, Bo An, Yaodong Yu, Sulin Liu, and Sinno Pan, · 2018
Cited alongside, same era.
“The lottery ticket hypothesis: Finding sparse, trainable neural networks,”
Jonathan Frankle and Michael Carbin, · 2018
Cited alongside, same era.
“Meta-learning with implicit gradients,”
Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine, · 2019
Cited alongside, same era.
“Dataset condensation with gradient matching.,”
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen, · 2021
Later among the works it cites.
“Rethinking bi-level optimization in neural architecture search: A gibbs sampling perspective,”
Chao Xue, Xiaoxing Wang, Junchi Yan, Yonggang Hu, Xiaokang Yang, and Kewei Sun, · 2021
Later among the works it cites.
“Meta-learning to improve pre-training,”
Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, and David K Duvenaud, · 2021
Later among the works it cites.
Risheng Liu, Jiaxin Gao, Jin Zhang, Deyu Meng, and Zhouchen Lin, · 2021
Later among the works it cites.
“Sign-MAML: Efficient model-agnostic meta-learning by signSGD,”
Chen Fan, Parikshit Ram, and Sijia Liu · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Harkirat Singh Behl, Atılım Güneş Baydin, and Philip HS Torr, · 2019
Cited alongside, same era.
“Invariant risk minimization,”
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz, · 2019
Cited alongside, same era.
“Pc-darts: Partial channel connections for memory-efficient architecture search,”
Yuhui Xu, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Guo-Jun Qi, Qi Tian, and Hongkai Xiong, · 2019
Cited alongside, same era.
“Neural architecture search: A survey,”
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter, · 2019
Cited alongside, same era.
“Truncated back-propagation for bilevel optimization,”
Amirreza Shaban, Ching-An Cheng, Nathan Hatch, and Byron Boots, · 2019
Cited alongside, same era.
“Penalty method for inversion-free deep bilevel optimization,”
Akshay Mehra and Jihun Hamm, · 2019
Cited alongside, same era.
“Momentum-based variance reduction in non-convex sgd,”
Ashok Cutkosky and Francesco Orabona, · 2019
Cited alongside, same era.
“A value-function-based interior-point method for non-convex bi-level optimization,”
Risheng Liu, Xuan Liu, Xiaoming Yuan, Shangzhi Zeng, and Jin Zhang, · 2021
Later among the works it cites.
“Value-function-based sequential minimization for bi-level optimization,”
Risheng Liu, Xuan Liu, Shangzhi Zeng, Jin Zhang, and Yixuan Zhang, · 2021
Later among the works it cites.
“Bilevel optimization: Convergence analysis and enhanced design,”
Kaiyi Ji, Junjie Yang, and Yingbin Liang, · 2021
Later among the works it cites.
“A near-optimal algorithm for stochastic bilevel optimization via double-momentum,”
Prashant Khanduri, Siliang Zeng, Mingyi Hong, Hoi-To Wai, Zhaoran Wang, and Zhuoran Yang, · 2021
Later among the works it cites.
“Tighter analysis of alternating stochastic gradient method for stochastic nested problems,”
Tianyi Chen, Yuejiao Sun, and Wotao Yin, · 2021
Later among the works it cites.
“Provably faster algorithms for bilevel optimization,”
Junjie Yang, Kaiyi Ji, and Yingbin Liang, · 2021
Later among the works it cites.
“A momentum-assisted single-timescale stochastic approximation algorithm for bilevel optimization,” 2021
Prashant Khanduri, Siliang Zeng, Mingyi Hong, Hoi-To Wai, Zhaoran Wang, and Zhuoran Yang, · 2021
Later among the works it cites.
“Projection-free algorithm for stochastic bi-level optimization,”
Zeeshan Akhtar, Amrit Singh Bedi, Srujan Teja Thomdapu, and Ketan Rajawat, · 2021
Later among the works it cites.
“Randomized stochastic variance-reduced methods for multi-task stochastic bilevel optimization,”
Zhishuai Guo, Quanqi Hu, Lijun Zhang, and Tianbao Yang, · 2021
Later among the works it cites.
“Sanity checks for lottery tickets: Does your winning ticket really win the jackpot?,”
Xiaolong Ma, Geng Yuan, Xuan Shen, Tianlong Chen, Xuxi Chen, Xiaohan Chen, Ning Liu, Minghai Qin, Sijia Liu, Zhangyang Wang, et al., · 2021
Later among the works it cites.
“Does invariant risk minimization capture invariance?,”
Pritish Kamath, Akilesh Tangella, Danica Sutherland, and Nathan Srebro, · 2021
Later among the works it cites.
“On a computationally ill-behaved bilevel problem with a continuous and nonconvex lower level,”
Yasmine Beck, Martin Schmidt, Johannes Thürauf, and Daniel Bienstock, · 2022
Later among the works it cites.
“Learning to continuously optimize wireless resource in a dynamic environment: A bilevel optimization perspective,”
Haoran Sun, Wenqiang Pu, Xiao Fu, Tsung-Hui Chang, and Mingyi Hong, · 2022
Later among the works it cites.
“Predicting flat-fading channels via meta-learned closed-form linear filters and equilibrium propagation,”
Sangwoo Park and Osvaldo Simeone, · 2022
Later among the works it cites.
“Bilevel methods for image reconstruction,”
Caroline Crockett, Jeffrey A Fessler, et al., · 2022
Later among the works it cites.
“Revisiting and advancing fast adversarial training through the lens of bi-level optimization,”
Yihua Zhang, Guanhua Zhang, Prashant Khanduri, Mingyi Hong, Shiyu Chang, and Sijia Liu, · 2022
Later among the works it cites.
“Learning to optimize: A primer and a benchmark,”
Tianlong Chen, Xiaohan Chen, Wuyang Chen, Zhangyang Wang, Howard Heaton, Jialin Liu, and Wotao Yin, · 2022
Later among the works it cites.
“Bayesian invariant risk minimization,”
Yong Lin, Hanze Dong, Hao Wang, and Tong Zhang, · 2022
Later among the works it cites.
“Sparse invariant risk minimization,”
Xiao Zhou, Yong Lin, Weizhong Zhang, and Tong Zhang, · 2022
Later among the works it cites.
“Dataset distillation by matching training trajectories,”
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu, · 2022
Later among the works it cites.
“Advancing model pruning via bi-level optimization,”
Yihua Zhang, Yuguang Yao, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, and Sijia Liu, · 2022
Later among the works it cites.
“Learning sample reweighting for accuracy and adversarial robustness,”
Chester Holtz, Tsui-Wei Weng, and Gal Mishne, · 2022
Later among the works it cites.
“Gradient-based bi-level optimization for deep learning: A survey,”
Can Chen, Xi Chen, Chen Ma, Zixuan Liu, and Xue Liu, · 2022
Later among the works it cites.
“Differentiable bilevel programming for stackelberg congestion games,”
Jiayang Li, Jing Yu, Qianni Wang, Boyi Liu, Zhaoran Wang, and Yu Marco Nie, · 2022
Later among the works it cites.
“A constrained optimization approach to bilevel optimization with multiple inner minima,”
Daouda Sow, Kaiyi Ji, Ziwei Guan, and Yingbin Liang, · 2022
Later among the works it cites.
“Bome! bilevel optimization made easy: A simple first-order approach,”
Bo Liu, Mao Ye, Stephen Wright, Peter Stone, and Qiang Liu, · 2022
Later among the works it cites.
Quan Xiao, Han Shen, Wotao Yin, and Tianyi Chen, · 2022
Later among the works it cites.
“A single-timescale method for stochastic bilevel optimization,”
Tianyi Chen, Yuejiao Sun, Quan Xiao, and Wotao Yin, · 2022
Later among the works it cites.
“Amortized implicit differentiation for stochastic bilevel optimization,”
Michael Arbel and Julien Mairal, · 2022
Later among the works it cites.
Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, and Thomas Moreau, · 2022
Later among the works it cites.
“Efficient robust training via backward smoothing,”
Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu, and Jingjing Liu, · 2022
Later among the works it cites.
“Pareto invariant risk minimization,”
Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Kaili Ma, Yonggang Zhang, Han Yang, Bo Han, and James Cheng, · 2022
Later among the works it cites.
“A survey on bilevel optimization under uncertainty,”
Yasmine Beck, Ivana Ljubić, and Martin Schmidt, · 2023
Closest in time.
“Adversarial training should be cast as a non-zero-sum game,”
Alexander Robey, Fabian Latorre, George J Pappas, Hamed Hassani, and Volkan Cevher, · 2023
Closest in time.
“Linearly constrained bilevel optimization: A smoothed implicit gradient approach,”
Prashant Khanduri · 2023
Closest in time.
“On penalty-based bilevel gradient descent method,”
Han Shen and Tianyi Chen, · 2023
Closest in time.
“A generalized alternating method for bilevel learning under the polyak-łojasiewicz condition,”
Quan Xiao, Songtao Lu, and Tianyi Chen, · 2023
Closest in time.
“A fully first-order method for stochastic bilevel optimization,”
Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, and Robert Nowak, · 2023
Closest in time.
“Robustness-preserving lifelong learning via dataset condensation,”
Jinghan Jia, Yihua Zhang, Dogyoon Song, Sijia Liu, and Alfred Hero, · 2023
Closest in time.
“What is missing in IRM training and evaluation? challenges and solutions,”
Yihua Zhang, Pranay Sharma, Parikshit Ram, Mingyi Hong, Kush Varshney, and Sijia Liu, · 2023
Closest in time.
“Minimax problems with coupled linear constraints: computational complexity, duality and solution methods,”
I. Tsaknakis, M. Hong, and S Zhang, · 2023
Closest in time.
“Understanding and improving visual prompting: A label-mapping perspective,”
Aochuan Chen, Yuguang Yao, Pin-Yu Chen, Yihua Zhang, and Sijia Liu, · 2023
Closest in time.