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1306.3729
Cited By
Spectral Experts for Estimating Mixtures of Linear Regressions
International Conference on Machine Learning (ICML), 2013
17 June 2013
Arun Tejasvi Chaganty
Abigail Z. Jacobs
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Papers citing
"Spectral Experts for Estimating Mixtures of Linear Regressions"
50 / 72 papers shown
Learning and Generalization with Mixture Data
International Symposium on Information Theory (ISIT), 2025
Harsh Vardhan
A. Ghosh
A. Mazumdar
FedML
345
2
0
29 Apr 2025
Agnostic Learning of Mixed Linear Regressions with EM and AM Algorithms
A. Ghosh
Arya Mazumdar
FedML
319
1
0
03 Jun 2024
Global Convergence of Online Identification for Mixed Linear Regression
Yujing Liu
Zhixin Liu
Lei Guo
289
2
0
30 Nov 2023
Linear Regression using Heterogeneous Data Batches
Neural Information Processing Systems (NeurIPS), 2023
Ayush Jain
Rajat Sen
Weihao Kong
Abhimanyu Das
A. Orlitsky
199
3
0
05 Sep 2023
On the robust learning mixtures of linear regressions
Ying-Min Huang
Liang Chen
OOD
177
0
0
23 May 2023
Mixed Regression via Approximate Message Passing
Journal of machine learning research (JMLR), 2023
Nelvin Tan
R. Venkataramanan
346
7
0
05 Apr 2023
Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression
Annual Conference Computational Learning Theory (COLT), 2023
Gabriel Arpino
R. Venkataramanan
295
7
0
03 Mar 2023
Sharp analysis of EM for learning mixtures of pairwise differences
Annual Conference Computational Learning Theory (COLT), 2023
A. Dhawan
Cheng Mao
A. Pananjady
315
1
0
20 Feb 2023
mixdistreg: An R Package for Fitting Mixture of Experts Distributional Regression with Adaptive First-order Methods
David Rügamer
228
2
0
04 Feb 2023
Imbalanced Mixed Linear Regression
Neural Information Processing Systems (NeurIPS), 2023
Pini Zilber
B. Nadler
193
5
0
29 Jan 2023
Efficient List-Decodable Regression using Batches
International Conference on Machine Learning (ICML), 2022
Abhimanyu Das
Ayush Jain
Weihao Kong
Rajat Sen
200
5
0
23 Nov 2022
Towards Understanding Mixture of Experts in Deep Learning
Zixiang Chen
Yihe Deng
Yue-bo Wu
Quanquan Gu
Yuan-Fang Li
MLT
MoE
262
87
0
04 Aug 2022
Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation
International Conference on Machine Learning (ICML), 2022
Adarsh Barik
Jean Honorio
208
8
0
02 Jun 2022
On Learning Mixture of Linear Regressions in the Non-Realizable Setting
International Conference on Machine Learning (ICML), 2022
Avishek Ghosh
A. Mazumdar
S. Pal
Rajat Sen
271
13
0
26 May 2022
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures
Annual Conference Computational Learning Theory (COLT), 2022
Bryon Aragam
W. Tai
525
3
0
28 Mar 2022
Support Recovery in Mixture Models with Sparse Parameters
IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2022
A. Mazumdar
S. Pal
279
1
0
24 Feb 2022
A Wasserstein Minimax Framework for Mixed Linear Regression
International Conference on Machine Learning (ICML), 2021
Theo Diamandis
Yonina C. Eldar
Alireza Fallah
Farzan Farnia
Asuman Ozdaglar
276
7
0
14 Jun 2021
Support Recovery of Sparse Signals from a Mixture of Linear Measurements
Neural Information Processing Systems (NeurIPS), 2021
V. Gandikota
A. Mazumdar
S. Pal
279
11
0
10 Jun 2021
Efficient Algorithms for Estimating the Parameters of Mixed Linear Regression Models
Babak Barazandeh
Ali Ghafelebashi
Meisam Razaviyayn
Ram Sriharsha
179
3
0
12 May 2021
Estimation, Confidence Intervals, and Large-Scale Hypotheses Testing for High-Dimensional Mixed Linear Regression
Linjun Zhang
Rong Ma
T. Tony Cai
Hongzhe Li
236
13
0
06 Nov 2020
Recovery of sparse linear classifiers from mixture of responses
V. Gandikota
A. Mazumdar
S. Pal
392
11
0
22 Oct 2020
Neural Mixture Distributional Regression
David Rügamer
Florian Pfisterer
J. Herbinger
BDL
223
6
0
14 Oct 2020
Learning Mixtures of Low-Rank Models
IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2020
Yanxi Chen
Cong Ma
H. Vincent Poor
Yuxin Chen
368
16
0
23 Sep 2020
Recovery of Sparse Signals from a Mixture of Linear Samples
A. Mazumdar
S. Pal
FedML
237
12
0
29 Jun 2020
Robust Meta-learning for Mixed Linear Regression with Small Batches
Weihao Kong
Raghav Somani
Sham Kakade
Sewoong Oh
OOD
275
38
0
17 Jun 2020
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Jeongyeol Kwon
Nhat Ho
Constantine Caramanis
303
40
0
04 Jun 2020
Reducibility and Statistical-Computational Gaps from Secret Leakage
Matthew Brennan
Guy Bresler
424
102
0
16 May 2020
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression
Avishek Ghosh
Kannan Ramchandran
318
20
0
23 Apr 2020
Meta-learning for mixed linear regression
International Conference on Machine Learning (ICML), 2020
Weihao Kong
Raghav Somani
Zhao Song
Sham Kakade
Sewoong Oh
201
70
0
20 Feb 2020
Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments
Symposium on the Theory of Computing (STOC), 2019
Sitan Chen
Haibin Zhang
Zhao Song
247
43
0
16 Dec 2019
Sample Complexity of Learning Mixtures of Sparse Linear Regressions
A. Krishnamurthy
A. Mazumdar
A. Mcgregor
S. Pal
186
21
0
30 Oct 2019
Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation
Avishek Ghosh
A. Pananjady
Adityanand Guntuboyina
Kannan Ramchandran
257
28
0
21 Jun 2019
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities
Neural Information Processing Systems (NeurIPS), 2019
Wei Qian
Yuqian Zhang
Yudong Chen
191
10
0
16 Jun 2019
EM Converges for a Mixture of Many Linear Regressions
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
Jeongyeol Kwon
Constantine Caramanis
202
42
0
28 May 2019
Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach
Xin Zhang
Jia Liu
Zhengyuan Zhu
FedML
212
6
0
28 May 2019
Convergence of Parameter Estimates for Regularized Mixed Linear Regression Models
IEEE Conference on Decision and Control (CDC), 2019
Taiyao Wang
I. Paschalidis
975
7
0
21 Mar 2019
Iterative Least Trimmed Squares for Mixed Linear Regression
Neural Information Processing Systems (NeurIPS), 2019
Yanyao Shen
Sujay Sanghavi
343
27
0
10 Feb 2019
Hierarchical Methods of Moments
M. Ruffini
Guillaume Rabusseau
Borja Balle
190
3
0
17 Oct 2018
Global Convergence of EM Algorithm for Mixtures of Two Component Linear Regression
Jeongyeol Kwon
Wei Qian
Constantine Caramanis
Yudong Chen
Damek Davis
443
60
0
12 Oct 2018
Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval
Jianqing Fan
Han Liu
Zhaoran Wang
Zhuoran Yang
269
22
0
21 Aug 2018
Tensor Methods for Additive Index Models under Discordance and Heterogeneity
Krishnakumar Balasubramanian
Jianqing Fan
Zhuoran Yang
180
13
0
17 Jul 2018
Learning Mixtures of Linear Regressions with Nearly Optimal Complexity
Yuanzhi Li
Yingyu Liang
388
79
0
22 Feb 2018
Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms
Ashok Vardhan Makkuva
Sewoong Oh
Sreeram Kannan
Pramod Viswanath
MoE
255
21
0
21 Feb 2018
Spectral Learning of Binomial HMMs for DNA Methylation Data
Chicheng Zhang
E. Mukamel
Kamalika Chaudhuri
242
0
0
07 Feb 2018
Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entries
Dong Xia
M. Yuan
Cun-Hui Zhang
317
71
0
14 Nov 2017
SpectralLeader: Online Spectral Learning for Single Topic Models
Tong Yu
Branislav Kveton
Zheng Wen
Hung Bui
Ole J. Mengshoel
BDL
232
0
0
21 Sep 2017
Learning Combinations of Sigmoids Through Gradient Estimation
Stratis Ioannidis
Andrea Montanari
252
2
0
22 Aug 2017
The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising
Dong Xia
Fan Zhou
242
0
0
05 Jul 2017
Estimating the Coefficients of a Mixture of Two Linear Regressions by Expectation Maximization
Jason M. Klusowski
Dana Yang
W. Brinda
201
45
0
26 Apr 2017
Sequential Local Learning for Latent Graphical Models
Sejun Park
Eunho Yang
Jinwoo Shin
BDL
198
0
0
12 Mar 2017
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