Timezone: »
Subspace learning seeks a low dimensional representation of data that enables accurate reconstruction. However, in many applications, data is obtained from multiple sources rather than a single source (e.g. an object might be viewed by cameras at different angles, or a document might consist of text and images). The conditional independence of separate sources imposes constraints on their shared latent representation, which, if respected, can improve the quality of the learned low dimensional representation. In this paper, we present a convex formulation of multi-view subspace learning that enforces conditional independence while reducing dimensionality. For this formulation, we develop an efficient algorithm that recovers an optimal data reconstruction by exploiting an implicit convex regularizer, then recovers the corresponding latent representation and reconstruction model, jointly and optimally. Experiments illustrate that the proposed method produces high quality results.
Author Information
Martha White (University of Alberta)
Yao-Liang Yu (University of Waterloo)
Xinhua Zhang (University of Illinois at Chicago (UIC))
Dale Schuurmans (Google Brain & University of Alberta)
More from the Same Authors
-
2022 : Poisoning Generative Models to Promote Catastrophic Forgetting »
Siteng Kang · Xinhua Zhang -
2022 : Continual Poisoning of Generative Models to Promote Catastrophic Forgetting »
Siteng Kang · Xinhua Zhang -
2023 Poster: General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence »
Lingwei Zhu · Zheng Chen · Matthew Schlegel · Martha White -
2022 Workshop: Deep Reinforcement Learning Workshop »
Karol Hausman · Qi Zhang · Matthew Taylor · Martha White · Suraj Nair · Manan Tomar · Risto Vuorio · Ted Xiao · Zeyu Zheng · Manan Tomar -
2022 Poster: Moment Distributionally Robust Tree Structured Prediction »
Yeshu Li · Danyal Saeed · Xinhua Zhang · Brian Ziebart · Kevin Gimpel -
2022 Poster: Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats »
Hongwei Jin · Zishun Yu · Xinhua Zhang -
2021 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · Chelsea Finn · David Silver · Matthew Taylor · Martha White · Srijita Das · Yuqing Du · Andrew Patterson · Manan Tomar · Olivia Watkins -
2021 Poster: Distributionally Robust Imitation Learning »
Mohammad Ali Bashiri · Brian Ziebart · Xinhua Zhang -
2021 Poster: Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation »
Mao Li · Kaiqi Jiang · Xinhua Zhang -
2020 Poster: Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks »
Hongwei Jin · Zhan Shi · Venkata Jaya Shankar Ashish Peruri · Xinhua Zhang -
2020 Spotlight: Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks »
Hongwei Jin · Zhan Shi · Venkata Jaya Shankar Ashish Peruri · Xinhua Zhang -
2020 Poster: An implicit function learning approach for parametric modal regression »
Yangchen Pan · Ehsan Imani · Amir-massoud Farahmand · Martha White -
2020 Poster: Towards Safe Policy Improvement for Non-Stationary MDPs »
Yash Chandak · Scott Jordan · Georgios Theocharous · Martha White · Philip Thomas -
2020 Poster: Proximal Mapping for Deep Regularization »
Mao Li · Yingyi Ma · Xinhua Zhang -
2020 Spotlight: Towards Safe Policy Improvement for Non-Stationary MDPs »
Yash Chandak · Scott Jordan · Georgios Theocharous · Martha White · Philip Thomas -
2020 Spotlight: Proximal Mapping for Deep Regularization »
Mao Li · Yingyi Ma · Xinhua Zhang -
2020 Session: Orals & Spotlights Track 14: Reinforcement Learning »
Deepak Pathak · Martha White -
2019 : Closing Remarks »
Bo Dai · Niao He · Nicolas Le Roux · Lihong Li · Dale Schuurmans · Martha White -
2019 : Poster and Coffee Break 2 »
Karol Hausman · Kefan Dong · Ken Goldberg · Lihong Li · Lin Yang · Lingxiao Wang · Lior Shani · Liwei Wang · Loren Amdahl-Culleton · Lucas Cassano · Marc Dymetman · Marc Bellemare · Marcin Tomczak · Margarita Castro · Marius Kloft · Marius-Constantin Dinu · Markus Holzleitner · Martha White · Mengdi Wang · Michael Jordan · Mihailo Jovanovic · Ming Yu · Minshuo Chen · Moonkyung Ryu · Muhammad Zaheer · Naman Agarwal · Nan Jiang · Niao He · Nikolaus Yasui · Nikos Karampatziakis · Nino Vieillard · Ofir Nachum · Olivier Pietquin · Ozan Sener · Pan Xu · Parameswaran Kamalaruban · Paul Mineiro · Paul Rolland · Philip Amortila · Pierre-Luc Bacon · Prakash Panangaden · Qi Cai · Qiang Liu · Quanquan Gu · Raihan Seraj · Richard Sutton · Rick Valenzano · Robert Dadashi · Rodrigo Toro Icarte · Roshan Shariff · Roy Fox · Ruosong Wang · Saeed Ghadimi · Samuel Sokota · Sean Sinclair · Sepp Hochreiter · Sergey Levine · Sergio Valcarcel Macua · Sham Kakade · Shangtong Zhang · Sheila McIlraith · Shie Mannor · Shimon Whiteson · Shuai Li · Shuang Qiu · Wai Lok Li · Siddhartha Banerjee · Sitao Luan · Tamer Basar · Thinh Doan · Tianhe Yu · Tianyi Liu · Tom Zahavy · Toryn Klassen · Tuo Zhao · Vicenç Gómez · Vincent Liu · Volkan Cevher · Wesley Suttle · Xiao-Wen Chang · Xiaohan Wei · Xiaotong Liu · Xingguo Li · Xinyi Chen · Xingyou Song · Yao Liu · YiDing Jiang · Yihao Feng · Yilun Du · Yinlam Chow · Yinyu Ye · Yishay Mansour · · Yonathan Efroni · Yongxin Chen · Yuanhao Wang · Bo Dai · Chen-Yu Wei · Harsh Shrivastava · Hongyang Zhang · Qinqing Zheng · SIDDHARTHA SATPATHI · Xueqing Liu · Andreu Vall -
2019 Workshop: The Optimization Foundations of Reinforcement Learning »
Bo Dai · Niao He · Nicolas Le Roux · Lihong Li · Dale Schuurmans · Martha White -
2019 : Opening Remarks »
Bo Dai · Niao He · Nicolas Le Roux · Lihong Li · Dale Schuurmans · Martha White -
2019 Poster: Surrogate Objectives for Batch Policy Optimization in One-step Decision Making »
Minmin Chen · Ramki Gummadi · Chris Harris · Dale Schuurmans -
2019 Poster: Learning Macroscopic Brain Connectomes via Group-Sparse Factorization »
Farzane Aminmansour · Andrew Patterson · Lei Le · Yisu Peng · Daniel Mitchell · Franco Pestilli · Cesar F Caiafa · Russell Greiner · Martha White -
2019 Poster: Importance Resampling for Off-policy Prediction »
Matthew Schlegel · Wesley Chung · Daniel Graves · Jian Qian · Martha White -
2019 Poster: Meta-Learning Representations for Continual Learning »
Khurram Javed · Martha White -
2018 : Invited Speaker #6 Martha White »
Martha White -
2018 Poster: Supervised autoencoders: Improving generalization performance with unsupervised regularizers »
Lei Le · Andrew Patterson · Martha White -
2018 Poster: Distributionally Robust Graphical Models »
Rizal Fathony · Ashkan Rezaei · Mohammad Ali Bashiri · Xinhua Zhang · Brian Ziebart -
2018 Poster: Context-dependent upper-confidence bounds for directed exploration »
Raksha Kumaraswamy · Matthew Schlegel · Adam White · Martha White -
2018 Poster: An Off-policy Policy Gradient Theorem Using Emphatic Weightings »
Ehsan Imani · Eric Graves · Martha White -
2017 Poster: Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search »
Mohammad Ali Bashiri · Xinhua Zhang -
2017 Poster: Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction »
Zhan Shi · Xinhua Zhang · Yaoliang Yu -
2017 Spotlight: Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction »
Zhan Shi · Xinhua Zhang · Yaoliang Yu -
2016 Poster: Convex Two-Layer Modeling with Latent Structure »
Vignesh Ganapathiraman · Xinhua Zhang · Yaoliang Yu · Junfeng Wen -
2016 Poster: Deep Learning Games »
Dale Schuurmans · Martin A Zinkevich -
2016 Poster: Reward Augmented Maximum Likelihood for Neural Structured Prediction »
Mohammad Norouzi · Samy Bengio · zhifeng Chen · Navdeep Jaitly · Mike Schuster · Yonghui Wu · Dale Schuurmans -
2016 Poster: Estimating the class prior and posterior from noisy positives and unlabeled data »
Shantanu Jain · Martha White · Predrag Radivojac -
2015 Poster: Embedding Inference for Structured Multilabel Prediction »
Farzaneh Mirzazadeh · Siamak Ravanbakhsh · Nan Ding · Dale Schuurmans -
2014 Workshop: Representation and Learning Methods for Complex Outputs »
Richard Zemel · Dale Schuurmans · Kilian Q Weinberger · Yuhong Guo · Jia Deng · Francesco Dinuzzo · Hal Daumé III · Honglak Lee · Noah A Smith · Richard Sutton · Jiaqian YU · Vitaly Kuznetsov · Luke Vilnis · Hanchen Xiong · Calvin Murdock · Thomas Unterthiner · Jean-Francis Roy · Martin Renqiang Min · Hichem SAHBI · Fabio Massimo Zanzotto -
2014 Poster: Convex Deep Learning via Normalized Kernels »
Özlem Aslan · Xinhua Zhang · Dale Schuurmans -
2014 Poster: Robust Bayesian Max-Margin Clustering »
Changyou Chen · Jun Zhu · Xinhua Zhang -
2013 Workshop: Output Representation Learning »
Yuhong Guo · Dale Schuurmans · Richard Zemel · Samy Bengio · Yoshua Bengio · Li Deng · Dan Roth · Kilian Q Weinberger · Jason Weston · Kihyuk Sohn · Florent Perronnin · Gabriel Synnaeve · Pablo R Strasser · julien audiffren · Carlo Ciliberto · Dan Goldwasser -
2013 Poster: Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space »
Xinhua Zhang · Wee Sun Lee · Yee Whye Teh -
2013 Spotlight: Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space »
Xinhua Zhang · Wee Sun Lee · Yee Whye Teh -
2013 Poster: On Decomposing the Proximal Map »
Yao-Liang Yu -
2013 Poster: Convex Two-Layer Modeling »
Özlem Aslan · Hao Cheng · Xinhua Zhang · Dale Schuurmans -
2013 Spotlight: Convex Two-Layer Modeling »
Özlem Aslan · Hao Cheng · Xinhua Zhang · Dale Schuurmans -
2013 Oral: On Decomposing the Proximal Map »
Yao-Liang Yu -
2013 Poster: Polar Operators for Structured Sparse Estimation »
Xinhua Zhang · Yao-Liang Yu · Dale Schuurmans -
2013 Poster: Better Approximation and Faster Algorithm Using the Proximal Average »
Yao-Liang Yu -
2012 Poster: Accelerated Training for Matrix-norm Regularization: A Boosting Approach »
Xinhua Zhang · Yao-Liang Yu · Dale Schuurmans -
2012 Poster: A Polynomial-time Form of Robust Regression »
Yao-Liang Yu · Özlem Aslan · Dale Schuurmans -
2010 Poster: Lower Bounds on Rate of Convergence of Cutting Plane Methods »
Xinhua Zhang · Ankan Saha · S.V.N. Vishwanathan -
2010 Poster: Relaxed Clipping: A Global Training Method for Robust Regression and Classification »
Yao-Liang Yu · Min Yang · Linli Xu · Martha White · Dale Schuurmans -
2010 Poster: Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains »
Martha White · Adam M White -
2009 Poster: Convex Relaxation of Mixture Regression with Efficient Algorithms »
Novi Quadrianto · Tiberio Caetano · John Lim · Dale Schuurmans -
2009 Poster: A General Projection Property for Distribution Families »
Yao-Liang Yu · Yuxi Li · Dale Schuurmans · Csaba Szepesvari -
2008 Poster: Kernel Measures of Independence for non-iid Data »
Xinhua Zhang · Le Song · Arthur Gretton · Alexander Smola -
2008 Spotlight: Kernel Measures of Independence for non-iid Data »
Xinhua Zhang · Le Song · Arthur Gretton · Alexander Smola -
2007 Spotlight: Stable Dual Dynamic Programming »
Tao Wang · Daniel Lizotte · Michael Bowling · Dale Schuurmans -
2007 Poster: Stable Dual Dynamic Programming »
Tao Wang · Daniel Lizotte · Michael Bowling · Dale Schuurmans -
2007 Session: Spotlights »
Dale Schuurmans -
2007 Poster: Convex Relaxations of EM »
Yuhong Guo · Dale Schuurmans -
2007 Poster: Discriminative Batch Mode Active Learning »
Yuhong Guo · Dale Schuurmans -
2006 Poster: Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms »
Xinhua Zhang · Wee Sun Lee -
2006 Poster: Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields »
Chi-Hoon Lee · Shaojun Wang · Feng Jiao · Dale Schuurmans · Russell Greiner -
2006 Poster: implicit Online Learning with Kernels »
Li Cheng · Vishwanathan S V N · Dale Schuurmans · Shaojun Wang · Terry Caelli