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Learning Mixtures of Linear Regressions with Nearly Optimal Complexity

22 February 2018

Papers citing "Learning Mixtures of Linear Regressions with Nearly Optimal Complexity"

50 / 58 papers shown

Title
Analyzing the Effect of Embedding Norms and Singular Values to Oversmoothing in Graph Neural Networks Dimitrios Kelesis Dimitris Fotakis Georgios Paliouras 68 0 0 07 Oct 2025
Learning and Generalization with Mixture DataInternational Symposium on Information Theory (ISIT), 2025 Harsh Vardhan A. Ghosh A. Mazumdar FedML 214 1 0 29 Apr 2025
Multi-convex Programming for Discrete Latent Factor Models Prototyping Hao Zhu Shengchao Yan Jasper Hoffmann Joschka Boedecker AI4CE 168 1 0 02 Apr 2025
Finite Sample Analysis of Tensor Decomposition for Learning Mixtures of Linear SystemsConference on Learning for Dynamics & Control (L4DC), 2024 Maryann Rui M. Dahleh 298 2 0 13 Dec 2024
Agnostic Learning of Mixed Linear Regressions with EM and AM Algorithms A. Ghosh Arya Mazumdar FedML 219 0 0 03 Jun 2024
Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds David Jin Sushrut Karmalkar Harry Zhang Luca Carlone 3DPC 219 13 0 16 Feb 2024
Provably learning a multi-head attention layer Sitan Chen Yuanzhi Li MLT 254 17 0 06 Feb 2024
Global Convergence of Online Identification for Mixed Linear Regression Yujing Liu Zhixin Liu Lei Guo 167 2 0 30 Nov 2023
Transformers can optimally learn regression mixture modelsInternational Conference on Learning Representations (ICLR), 2023 Reese Pathak Rajat Sen Weihao Kong Abhimanyu Das 163 15 0 14 Nov 2023
SQ Lower Bounds for Learning Mixtures of Linear ClassifiersNeural Information Processing Systems (NeurIPS), 2023 Ilias Diakonikolas D. Kane Yuxin Sun 229 3 0 18 Oct 2023
Estimation of Models with Limited Data by Leveraging Shared StructureIEEE Conference on Decision and Control (CDC), 2023 Maryann Rui Thibaut Horel M. Dahleh 151 1 0 04 Oct 2023
Non-Clashing Teaching Maps for Balls in GraphsAnnual Conference Computational Learning Theory (COLT), 2023 Jérémie Chalopin V. Chepoi Fionn Mc Inerney Sébastien Ratel 168 10 0 06 Sep 2023
Linear Regression using Heterogeneous Data BatchesNeural Information Processing Systems (NeurIPS), 2023 Ayush Jain Rajat Sen Weihao Kong Abhimanyu Das A. Orlitsky 138 3 0 05 Sep 2023
EM for Mixture of Linear Regression with Clustered DataInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023 Amirhossein Reisizadeh Khashayar Gatmiry Asuman Ozdaglar FedML 111 1 0 22 Aug 2023
Clustered Linear Contextual Bandits with Knapsacks Yichuan Deng M. Mamakos Zhao Song 135 0 0 21 Aug 2023
Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical SystemsInternational Conference on Machine Learning (ICML), 2023 Ainesh Bakshi Allen Liu Ankur Moitra Morris Yau 205 10 0 13 Jul 2023
On the robust learning mixtures of linear regressions Ying-Min Huang Liang Chen OOD 126 0 0 23 May 2023
A Parameterized Theory of PAC LearningAAAI Conference on Artificial Intelligence (AAAI), 2023 Cornelius Brand R. Ganian Kirill Simonov 83 7 0 27 Apr 2023
Mixed Regression via Approximate Message PassingJournal of machine learning research (JMLR), 2023 Nelvin Tan R. Venkataramanan 282 7 0 05 Apr 2023
Statistical-Computational Tradeoffs in Mixed Sparse Linear RegressionAnnual Conference Computational Learning Theory (COLT), 2023 Gabriel Arpino R. Venkataramanan 186 6 0 03 Mar 2023
Sharp analysis of EM for learning mixtures of pairwise differencesAnnual Conference Computational Learning Theory (COLT), 2023 A. Dhawan Cheng Mao A. Pananjady 205 1 0 20 Feb 2023
Imbalanced Mixed Linear RegressionNeural Information Processing Systems (NeurIPS), 2023 Pini Zilber B. Nadler 142 5 0 29 Jan 2023
Efficient List-Decodable Regression using BatchesInternational Conference on Machine Learning (ICML), 2022 Abhimanyu Das Ayush Jain Weihao Kong Rajat Sen 154 5 0 23 Nov 2022
Global Convergence of Federated Learning for Mixed RegressionIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2022 Lili Su Jiaming Xu Pengkun Yang FedML 155 9 0 15 Jun 2022
On Learning Mixture of Linear Regressions in the Non-Realizable SettingInternational Conference on Machine Learning (ICML), 2022 Avishek Ghosh A. Mazumdar S. Pal Rajat Sen 198 11 0 26 May 2022
What Makes A Good Fisherman? Linear Regression under Self-Selection BiasSymposium on the Theory of Computing (STOC), 2022 Yeshwanth Cherapanamjeri C. Daskalakis Andrew Ilyas Manolis Zampetakis 230 10 0 06 May 2022
Bayesian Active Learning for Discrete Latent Variable ModelsNeural Computation (Neural Comput.), 2022 Aditi Jha Zoe C. Ashwood Jonathan W. Pillow 181 11 0 27 Feb 2022
Support Recovery in Mixture Models with Sparse ParametersIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2022 A. Mazumdar S. Pal 207 1 0 24 Feb 2022
On the identifiability of mixtures of ranking models Xiaomin Zhang Xucheng Zhang Po-Ling Loh Yingyu Liang 239 6 0 31 Jan 2022
Learning Mixtures of Linear Dynamical SystemsInternational Conference on Machine Learning (ICML), 2022 Yanxi Chen H. Vincent Poor 261 20 0 26 Jan 2022
Towards Sample-efficient Overparameterized Meta-learningNeural Information Processing Systems (NeurIPS), 2022 Yue Sun Adhyyan Narang Halil Ibrahim Gulluk Samet Oymak Maryam Fazel BDL 144 25 0 16 Jan 2022
Uniform Consistency in Nonparametric Mixture ModelsAnnals of Statistics (Ann. Stat.), 2021 Bryon Aragam Ruiyi Yang 285 6 0 31 Aug 2021
Statistical Query Lower Bounds for List-Decodable Linear RegressionNeural Information Processing Systems (NeurIPS), 2021 Ilias Diakonikolas D. Kane Ankit Pensia Thanasis Pittas Alistair Stewart 142 24 0 17 Jun 2021
Multiple Support Recovery Using Very Few Measurements Per SampleIEEE Transactions on Signal Processing (IEEE TSP), 2021 Lekshmi Ramesh C. Murthy Himanshu Tyagi 141 5 0 20 May 2021
Sample Efficient Subspace-based Representations for Nonlinear Meta-LearningIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2021 Halil Ibrahim Gulluk Yue Sun Samet Oymak Maryam Fazel 175 2 0 14 Feb 2021
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable ModelsIEEE Annual Symposium on Foundations of Computer Science (FOCS), 2020 Ilias Diakonikolas D. Kane 176 33 0 14 Dec 2020
On InstaHide, Phase Retrieval, and Sparse Matrix FactorizationInternational Conference on Learning Representations (ICLR), 2020 Sitan Chen Xiaoxiao Li Zhao Song Danyang Zhuo 198 13 0 23 Nov 2020
Estimation, Confidence Intervals, and Large-Scale Hypotheses Testing for High-Dimensional Mixed Linear Regression Linjun Zhang Rong Ma T. Tony Cai Hongzhe Li 134 13 0 06 Nov 2020
Neural Mixture Distributional Regression David Rügamer Florian Pfisterer J. Herbinger BDL 148 6 0 14 Oct 2020
Learning Mixtures of Low-Rank ModelsIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2020 Yanxi Chen Cong Ma H. Vincent Poor Yuxin Chen 219 15 0 23 Sep 2020
Splintering with distributions: A stochastic decoy scheme for private computation Praneeth Vepakomma Julia Balla Ramesh Raskar FedML 351 2 0 06 Jul 2020
Robust Meta-learning for Mixed Linear Regression with Small Batches Weihao Kong Raghav Somani Sham Kakade Sewoong Oh OOD 193 37 0 17 Jun 2020
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear RegressionInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020 Jeongyeol Kwon Nhat Ho Constantine Caramanis 224 41 0 04 Jun 2020
Reducibility and Statistical-Computational Gaps from Secret Leakage Matthew Brennan Guy Bresler 326 100 0 16 May 2020
Outlier-Robust Clustering of Non-Spherical Mixtures Ainesh Bakshi Pravesh Kothari 338 34 0 06 May 2020
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression Avishek Ghosh Kannan Ramchandran 227 19 0 23 Apr 2020
Making Method of Moments Great Again? -- How can GANs learn distributions Yuanzhi Li Zehao Dou GAN 224 5 0 09 Mar 2020
Meta-learning for mixed linear regressionInternational Conference on Machine Learning (ICML), 2020 Weihao Kong Raghav Somani Zhao Song Sham Kakade Sewoong Oh 154 71 0 20 Feb 2020
Learning Mixtures of Linear Regressions in Subexponential Time via Fourier MomentsSymposium on the Theory of Computing (STOC), 2019 Sitan Chen Haibin Zhang Zhao Song 158 43 0 16 Dec 2019
Identifying Linear Models in Multi-Resolution Population Data using Minimum Description Length Principle to Predict Household IncomeACM Transactions on Knowledge Discovery from Data (TKDD), 2019 Chainarong Amornbunchornvej Navaporn Surasvadi Anon Plangprasopchok S. Thajchayapong 142 9 0 10 Jul 2019