Timezone: »
We study matrix completion problem with side information. Side information has been considered in several matrix completion applications, and is generally shown to be useful empirically. Recently, Xu et al. studied the effect of side information for matrix completion under a theoretical viewpoint, showing that sample complexity can be significantly reduced given completely clean features. However, since in reality most given features are noisy or even weakly informative, how to develop a general model to handle general feature set, and how much the noisy features can help matrix recovery in theory, is still an important issue to investigate. In this paper, we propose a novel model that balances between features and observations simultaneously, enabling us to leverage feature information yet to be robust to feature noise. Moreover, we study the effectof general features in theory, and show that by using our model, the sample complexity can still be lower than matrix completion as long as features are sufficiently informative. This result provides a theoretical insight of usefulness for general side information. Finally, we consider synthetic data and two real applications - relationship prediction and semi-supervised clustering, showing that our model outperforms other methods for matrix completion with features both in theory and practice.
Author Information
Kai-Yang Chiang (UT Austin)
Cho-Jui Hsieh (UC Davis)
Inderjit Dhillon (University of Texas at Austin)
More from the Same Authors
-
2022 : Differentially Private Federated Learning with Normalized Updates »
Rudrajit Das · Abolfazl Hashemi · Sujay Sanghavi · Inderjit Dhillon -
2023 Poster: Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization »
Jui-Nan Yen · Sai Surya Duvvuri · Inderjit Dhillon · Cho-Jui Hsieh -
2023 Poster: A Computationally Efficient Sparsified Online Newton Method »
Fnu Devvrit · Sai Surya Duvvuri · Rohan Anil · Vineet Gupta · Cho-Jui Hsieh · Inderjit Dhillon -
2022 Poster: S3GC: Scalable Self-Supervised Graph Clustering »
Fnu Devvrit · Aditya Sinha · Inderjit Dhillon · Prateek Jain -
2022 Poster: ELIAS: End-to-End Learning to Index and Search in Large Output Spaces »
Nilesh Gupta · Patrick Chen · Hsiang-Fu Yu · Cho-Jui Hsieh · Inderjit Dhillon -
2021 Poster: Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification »
Jiong Zhang · Wei-Cheng Chang · Hsiang-Fu Yu · Inderjit Dhillon -
2021 Poster: Label Disentanglement in Partition-based Extreme Multilabel Classification »
Xuanqing Liu · Wei-Cheng Chang · Hsiang-Fu Yu · Cho-Jui Hsieh · Inderjit Dhillon -
2021 Poster: DRONE: Data-aware Low-rank Compression for Large NLP Models »
Patrick Chen · Hsiang-Fu Yu · Inderjit Dhillon · Cho-Jui Hsieh -
2019 : Lunch Break and Posters »
Xingyou Song · Elad Hoffer · Wei-Cheng Chang · Jeremy Cohen · Jyoti Islam · Yaniv Blumenfeld · Andreas Madsen · Jonathan Frankle · Sebastian Goldt · Satrajit Chatterjee · Abhishek Panigrahi · Alex Renda · Brian Bartoldson · Israel Birhane · Aristide Baratin · Niladri Chatterji · Roman Novak · Jessica Forde · YiDing Jiang · Yilun Du · Linara Adilova · Michael Kamp · Berry Weinstein · Itay Hubara · Tal Ben-Nun · Torsten Hoefler · Daniel Soudry · Hsiang-Fu Yu · Kai Zhong · Yiming Yang · Inderjit Dhillon · Jaime Carbonell · Yanqing Zhang · Dar Gilboa · Johannes Brandstetter · Alexander R Johansen · Gintare Karolina Dziugaite · Raghav Somani · Ari Morcos · Freddie Kalaitzis · Hanie Sedghi · Lechao Xiao · John Zech · Muqiao Yang · Simran Kaur · Qianli Ma · Yao-Hung Hubert Tsai · Ruslan Salakhutdinov · Sho Yaida · Zachary Lipton · Daniel Roy · Michael Carbin · Florent Krzakala · Lenka Zdeborová · Guy Gur-Ari · Ethan Dyer · Dilip Krishnan · Hossein Mobahi · Samy Bengio · Behnam Neyshabur · Praneeth Netrapalli · Kris Sankaran · Julien Cornebise · Yoshua Bengio · Vincent Michalski · Samira Ebrahimi Kahou · Md Rifat Arefin · Jiri Hron · Jaehoon Lee · Jascha Sohl-Dickstein · Samuel Schoenholz · David Schwab · Dongyu Li · Sang Choe · Henning Petzka · Ashish Verma · Zhichao Lin · Cristian Sminchisescu -
2019 Poster: Provable Non-linear Inductive Matrix Completion »
Kai Zhong · Zhao Song · Prateek Jain · Inderjit Dhillon -
2019 Poster: Inverting Deep Generative models, One layer at a time »
Qi Lei · Ajil Jalal · Inderjit Dhillon · Alex Dimakis -
2019 Poster: Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting »
Rajat Sen · Hsiang-Fu Yu · Inderjit Dhillon -
2019 Poster: AutoAssist: A Framework to Accelerate Training of Deep Neural Networks »
Jiong Zhang · Hsiang-Fu Yu · Inderjit Dhillon -
2019 Poster: Primal-Dual Block Generalized Frank-Wolfe »
Qi Lei · JIACHENG ZHUO · Constantine Caramanis · Inderjit Dhillon · Alex Dimakis -
2017 Poster: A Greedy Approach for Budgeted Maximum Inner Product Search »
Hsiang-Fu Yu · Cho-Jui Hsieh · Qi Lei · Inderjit Dhillon -
2016 Poster: Asynchronous Parallel Greedy Coordinate Descent »
Yang You · Xiangru Lian · Ji Liu · Hsiang-Fu Yu · Inderjit Dhillon · James Demmel · Cho-Jui Hsieh -
2016 Poster: Coordinate-wise Power Method »
Qi Lei · Kai Zhong · Inderjit Dhillon -
2016 Poster: Structured Sparse Regression via Greedy Hard Thresholding »
Prateek Jain · Nikhil Rao · Inderjit Dhillon -
2016 Poster: Mixed Linear Regression with Multiple Components »
Kai Zhong · Prateek Jain · Inderjit Dhillon -
2016 Poster: Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction »
Hsiang-Fu Yu · Nikhil Rao · Inderjit Dhillon -
2016 Poster: Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain »
Ian En-Hsu Yen · Xiangru Huang · Kai Zhong · Ruohan Zhang · Pradeep Ravikumar · Inderjit Dhillon -
2016 Poster: A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order »
Xiangru Lian · Huan Zhang · Cho-Jui Hsieh · Yijun Huang · Ji Liu -
2015 Workshop: Multiresolution methods for large-scale learning »
Inderjit Dhillon · Risi Kondor · Rob Nowak · Michael O'Neil · Nedelina Teneva -
2015 Poster: Matrix Completion with Noisy Side Information »
Kai-Yang Chiang · Cho-Jui Hsieh · Inderjit Dhillon -
2015 Poster: Collaborative Filtering with Graph Information: Consistency and Scalable Methods »
Nikhil Rao · Hsiang-Fu Yu · Pradeep Ravikumar · Inderjit Dhillon -
2015 Spotlight: Collaborative Filtering with Graph Information: Consistency and Scalable Methods »
Nikhil Rao · Hsiang-Fu Yu · Pradeep Ravikumar · Inderjit Dhillon -
2015 Poster: Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent »
Ian En-Hsu Yen · Kai Zhong · Cho-Jui Hsieh · Pradeep Ravikumar · Inderjit Dhillon -
2015 Poster: Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial »
David I Inouye · Pradeep Ravikumar · Inderjit Dhillon -
2015 Poster: Consistent Multilabel Classification »
Oluwasanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon -
2014 Poster: QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models »
Cho-Jui Hsieh · Inderjit Dhillon · Pradeep Ravikumar · Stephen Becker · Peder A Olsen -
2014 Poster: Consistent Binary Classification with Generalized Performance Metrics »
Sanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon -
2014 Poster: Fast Prediction for Large-Scale Kernel Machines »
Cho-Jui Hsieh · Si Si · Inderjit Dhillon -
2014 Poster: Multi-Scale Spectral Decomposition of Massive Graphs »
Si Si · Donghyuk Shin · Inderjit Dhillon · Beresford N Parlett -
2014 Poster: Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space »
Ian En-Hsu Yen · Ting-Wei Lin · Shou-De Lin · Pradeep Ravikumar · Inderjit Dhillon -
2014 Spotlight: Consistent Binary Classification with Generalized Performance Metrics »
Sanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon -
2014 Poster: Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators »
Kai Zhong · Ian En-Hsu Yen · Inderjit Dhillon · Pradeep Ravikumar -
2014 Poster: Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs »
David I Inouye · Pradeep Ravikumar · Inderjit Dhillon -
2014 Poster: Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings »
Ian En-Hsu Yen · Cho-Jui Hsieh · Pradeep Ravikumar · Inderjit Dhillon -
2013 Poster: BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables »
Cho-Jui Hsieh · Matyas A Sustik · Inderjit Dhillon · Pradeep Ravikumar · Russell Poldrack -
2013 Oral: BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables »
Cho-Jui Hsieh · Matyas A Sustik · Inderjit Dhillon · Pradeep Ravikumar · Russell Poldrack -
2013 Poster: Large Scale Distributed Sparse Precision Estimation »
Huahua Wang · Arindam Banerjee · Cho-Jui Hsieh · Pradeep Ravikumar · Inderjit Dhillon -
2013 Poster: Learning with Noisy Labels »
Nagarajan Natarajan · Inderjit Dhillon · Pradeep Ravikumar · Ambuj Tewari -
2012 Poster: A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation »
Cho-Jui Hsieh · Inderjit Dhillon · Pradeep Ravikumar · Arindam Banerjee -
2011 Poster: Greedy Algorithms for Structurally Constrained High Dimensional Problems »
Ambuj Tewari · Pradeep Ravikumar · Inderjit Dhillon -
2011 Poster: Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation »
Cho-Jui Hsieh · Matyas A Sustik · Inderjit Dhillon · Pradeep Ravikumar -
2011 Poster: Nearest Neighbor based Greedy Coordinate Descent »
Inderjit Dhillon · Pradeep Ravikumar · Ambuj Tewari -
2011 Poster: Orthogonal Matching Pursuit with Replacement »
Prateek Jain · Ambuj Tewari · Inderjit Dhillon -
2010 Spotlight: Guaranteed Rank Minimization via Singular Value Projection »
Prateek Jain · Raghu Meka · Inderjit Dhillon -
2010 Poster: Guaranteed Rank Minimization via Singular Value Projection »
Prateek Jain · Raghu Meka · Inderjit Dhillon -
2010 Spotlight: Inductive Regularized Learning of Kernel Functions »
Prateek Jain · Brian Kulis · Inderjit Dhillon -
2010 Poster: Inductive Regularized Learning of Kernel Functions »
Prateek Jain · Brian Kulis · Inderjit Dhillon -
2009 Poster: Matrix Completion from Power-Law Distributed Samples »
Raghu Meka · Prateek Jain · Inderjit Dhillon -
2008 Poster: Online Metric Learning and Fast Similarity Search »
Prateek Jain · Brian Kulis · Inderjit Dhillon · Kristen Grauman -
2008 Oral: Online Metric Learning and Fast Similarity Search »
Prateek Jain · Brian Kulis · Inderjit Dhillon · Kristen Grauman -
2006 Poster: Differential Entropic Clustering of Multivariate Gaussians »
Jason V Davis · Inderjit Dhillon