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Arora, Raman

Assistant Professor
Computer Science
https://www.cs.jhu.edu/~raman/

Malone 331
(410) 516-1327
rarora8@jhu.edu

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About

Education
  • Ph.D. 2009, Univ of Wisconsin Madison
  • Master of Science 2005, Univ of Wisconsin Madison
  • Bachelor of Engineering 2001, Netaji Subhas Institute of Technology, University, Delhi, India
Research Areas
  • Application
  • BIG data
  • Computational approaches
  • DATA
  • Data science
  • MACHINE learning
  • Multiview learning
  • Representation learning techniques
  • Spectral learning
  • Stochastic approximation algorithms
  • Subspace learning
  • THEORY
  • Unlabeled data
  • Unsupervised representation learning technique
  • Useful representations
Awards
  • 2017:  Faculty fellowship to ISRAEL
  • 2015:  NSF BIGDATA Award
  • 2014:  Senior Member
Presentations
  • "Foundations of representation learning", Faculty fellowship to ISRAEL (FF2ISRAEL).  Israel.  May 25, 2018
  • "Foundations of representation learning", Deep Learning for Computer Vision.  Germany.  September 25, 2017
  • "Representation learning for language processing", JSALT summer school.  Pittsburgh Pennsylvania, United States of America (the).  June 21, 2017
  • "Deep multiview representation learning", Tutorial at IJCNN.  Anchorage Alaska, United States of America (the).  May 14, 2017
  • "Stochastic approximation for Canonical Correlation Analysis", NSF BIGDATA PI Meeting.  Arlington Virginia, United States of America (the).  April 15, 2017
  • "Algorithmic biases in deep learning", Signal and Information Processing (SIP) Seminar.  New Brunswick, NJ.  March 16, 2017
  • "Stochastic approximation for representation learning", Center for Imaging Science (CIS) Seminar.  Baltimore, MD.  March 14, 2017
  • "Stochastic approximation for representation learning", NYU ML Seminar.  New York, NY.  March 7, 2017
  • "Scalable unsupervised learning for BIGDATA", BIGDATA seminar @ Indus Insights.  New Delhi, India.  December 29, 2016
  • "New results in the theory of deep learning", NSIT Dept Colloqium.  New Delhi, India.  December 28, 2016
  • "Representation learning for language and speech processing", IBM Research Seminar.  New Delhi, India.  December 20, 2016
  • "Stochastic approximation for canonical correlation analysis", IIIT CS Seminar.  New Delhi, India.  December 16, 2016
  • "Disease Trajectory Maps", Advances in Neural Information Processing Systems.  Barcelona, Spain.  December 8, 2016
  • "Robust Principal Component Analysis", USNA data seminar.  Annapolis, MD.  October 28, 2016
  • "Unsupervised learning: the new frontier", Bowie State CS Seminar.  Bowie, MD.  September 26, 2016
  • "Advances in unsupervised learning and data analysis", Paypal Data Seminar.  Lutherville-Timonium, MD.  August 19, 2016
  • "Scalable algorithms for unsupervised learning", DARPA xAI Proposers Meeting.  Arlington, VA.  August 11, 2016
  • "Stochastic optimization for multiview representation learning using partial least squares", International Conference on Machine Learning.  New York, NY.  June 21, 2016
  • "Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning", International Conference on Machine Learning.  New York, NY.  June 21, 2016
  • "Representation Learning", Fred Jelinek Summer School.  Baltimore, MD.  June 17, 2016
  • "Stochastic optimization for multiview learning", IEEE BIGDATA summer school.  Pittsburgh, PA.  June 17, 2016
  • "Embedding Lexical Features via Low-Rank Tensors", Annual Conference NAACL HLT.  San Diego, CA.  June 12, 2016
  • "Embedding Lexical Features via Low-Rank Tensors", Association for Computational Linguistics (ACL).  San Diego, CA.  June 12, 2016
  • "Stochastic optimization for multiview learning", CMU ML Seminar.  Pittsburgh, PA.  May 17, 2016
  • "Stochastic optimization for multiview learning", NSF BIGDATA PI Meeting.  Washington DC.  April 20, 2016
  • "Machine learning with humans in the loop", DARPA HIVE Program Planning.  JHU.  March 8, 2016
  • "Multiview LSA: Representation Learning via Generalized CCA", Annual Conference NAACL HLT.  Denver, CO.  June 1, 2015
  • "Accelerated mini-batch randomized block coordinate descent method", Advances in Neural Information Processing Systems.  Montreal, Canada.  December 11, 2014
  • "Reconstruction of articulatory measurements with smoothed low-rank matrix completion", IEEE Workshop on Spoken Language Technologies (SLT).  South Lake Tahoe, CA.  December 8, 2014
  • "Multi-view learning of representations for speech and language", IEEE Workshop on Spoken Language Technologies (SLT).  South Lake Tahoe, CA.  December 7, 2014
  • "Learning Representations from Big Data", MD Molecular Marshall.  Baltimore, MD.  August 1, 2014
  • "Multi-view learning with supervision for transformed bottleneck features", International conference on acoustics, speech and signal processing.  Florence, Italy.  May 7, 2014
  • "Learning Representations from Big Data", Lieber seminar.  Lieber institute of brain development.  March 27, 2014

Publications

Journal Articles
  • Specia L, Barrault L, Caglayan O, Duarte A, Elliott D, Gella S, Holzenberger N, Lala C, Lee SJ, Libovicky J, Madhyastha P, Metze F, Mulligan K, Ostapenko A, Palaskar S, Sanabria R, Wang J, Arora R (2020).  Grounded Sequence to Sequence Transduction.  IEEE Journal on Selected Topics in Signal Processing.  14(3).  577-591.
  • Li X, Lu J, Arora R, Haupt J, Liu H, Wang Z, Zhao T (2019).  Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization.  IEEE Transactions on Information Theory.  65(6).  3489-3514.
  • Holzenberger N, Palaskar S, Madhyastha P, Metze F, Arora R (2019).  Learning from Multiview Correlations in Open-domain Videos.  ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  2019-May.  8628-8632.
  • Paxton C, Barnoy Y, Katyal K, Arora R, Hager GD (2019).  Visual robot task planning.  Proceedings - IEEE International Conference on Robotics and Automation.  2019-May.  8832-8838.
  • Li X, Jiang H, Haupt J, Arora R, Liu H, Hong M, Zhao T (2019).  On fast convergence of proximal algorithms for SQRT-lasso optimization: Don’t worry about its nonsmooth loss function.  35th Conference on Uncertainty in Artificial Intelligence, UAI 2019.
  • Mianjy P, Arora R (2019).  On dropout and nuclear norm regularization.  36th International Conference on Machine Learning, ICML 2019.  2019-June.  8052-8061.
  • Li X, Jiang H, Haupt J, Arora R, Liu H, Hong M, Zhao T (2019).  On fast convergence of proximal algorithms for SQRT-lasso optimization: Don’t worry about its nonsmooth loss function.  35th Conference on Uncertainty in Artificial Intelligence, UAI 2019.
  • Li X, Jiang H, Haupt J, Arora R, Liu H, Hong M, Zhao T (2019).  On fast convergence of proximal algorithms for SQRT-lasso optimization: Don’t worry about its nonsmooth loss function.  35th Conference on Uncertainty in Artificial Intelligence, UAI 2019.
  • Li X, Haupt J, Lu J, Wang Z, Arora R, Liu H, Zhao T (2018).  Symmetry. saddle points, and global optimization landscape of nonconvex matrix factorization.  2018 Information Theory and Applications Workshop, ITA 2018.
  • Stein-O'Brien GL, Arora R, Culhane AC, Favorov AV, Garmire LX, Greene CS, Goff LA, Li Y, Ngom A, Ochs MF, Xu Y, Fertig EJ (2018).  Enter the Matrix: Factorization Uncovers Knowledge from Omics.  Trends in Genetics.  34(10).  790-805.
  • Orozco-Arroyave JR, Vásquez-Correa JC, Vargas-Bonilla JF, Arora R, Dehak N, Nidadavolu PS, Christensen H, Rudzicz F, Yancheva M, Chinaei H, Vann A, Vogler N, Bocklet T, Cernak M, Hannink J, Nöth E (2018).  NeuroSpeech.  SoftwareX.  8.  69-70.
  • Li X, Wang Z, Lu J, Arora R, Haupt J, Liu H, Zhao T (2018).  Symmetry, Saddle Points, and Global Geometry of Nonconvex Matrix Factorization.  IEEE Transactions on Information Theory.  64(6).  1-45.
  • Orozco-Arroyave JR, Vásquez-Correa JC, Vargas-Bonilla JF, Arora R, Dehak N, Nidadavolu PS, Christensen H, Rudzicz F, Yancheva M, Chinaei H, Vann A, Vogler N, Bocklet T, Cernak M, Hannink J, Nöth E (2018).  NeuroSpeech: An open-source software for Parkinson's speech analysis.  Digital Signal Processing: A Review Journal.  77.  207-221.
  • Li X, Zhao T, Arora R, Liu H, Hong M (2018).  On faster convergence of cyclic block coordinate descent-type methods for strongly convex minimization.  Journal of Machine Learning Research.  18.  1-24.
  • Arora R, Li X, Zhao T, Liu H, Hong M (2018).  On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization.  Journal of Machine Learning Research (JMLR).  18.  1-22.
  • Mianjy P, Arora R, Vidal R (2018).  On the implicit bias of dropout.  35th International Conference on Machine Learning, ICML 2018.  8.  5701-5717.
  • Ullah E, Mianjy P, Marinov TV, Arora R (2018).  Streaming kernel PCA with õ(√n) random features.  Advances in Neural Information Processing Systems.  2018-December.  7311-7321.
  • Marinov TV, Mianjy P, Arora R (2018).  Streaming principal component analysis in noisy settings.  35th International Conference on Machine Learning, ICML 2018.  8.  5463-5483.
  • Yang LF, Arora R, Zhao T, Braverman V (2018).  The physical systems behind optimization algorithms.  Advances in Neural Information Processing Systems.  2018-December.  4372-4381.
  • Arora R, Dinitz M, Marinov TV, Mohri M (2018).  Policy regret in repeated games.  Advances in Neural Information Processing Systems.  2018-December.  6732-6741.
  • Mianjy P, Arora R (2018).  Stochastic PCA with l2 and l1 regularization.  35th International Conference on Machine Learning, ICML 2018.  8.  5687-5700.
  • Arora R, Basu A, Mianjy P, Mukherjee A (2018).  Understanding deep neural networks with rectified linear units.  6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings.
  • Arora R, Basu A, Mianjy P, Mukherjee A (2018).  Understanding deep neural networks with rectified linear units.  6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings.
  • Arora R, Braverman V, Upadhyay J (2018).  Differentially private robust low-rank approximation.  Advances in Neural Information Processing Systems.  2018-December.  4137-4145.
  • Arora R, Stein-O’Brien G, Culhane A C, Favorov A V, Garmire L X, Greene C S, Goff L A, Li Y, Ngom A, Ochs M F, Xu Y, Fertig E J (2017).  Enter the matrix: factorization uncovers knowledge from omics.  Trends in Genetics.  1-27.
  • Cernak M, Nöth E, Rudzicz F, Christensen H, Orozco-Arroyave JR, Arora R, Bocklet T, Chinaei H, Hannink J, Nidadavolu PS, Vasquez JC, Yancheva M, Vann A, Vogler N (2017).  On the impact of non-modal phonation on phonological features.  ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  5090-5094.
  • Vasquez-Correa JC, Orozco-Arroyave JR, Arora R, Noth E, Dehak N, Christensen H, Rudzicz F, Bocklet T, Cernak M, Chinaei H, Hannink J, Nidadavolu PS, Yancheva M, Vann A, Vogler N (2017).  Multi-view representation learning via gcca for multimodal analysis of Parkinson's disease.  ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  2966-2970.
  • Badino L, Franceschi L, Arora R, Donini M, Pontil M (2017).  A speaker adaptive DNN training approach for speaker-independent acoustic inversion.  Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH.  2017-August.  984-988.
  • Benton A, Khayrallah H, Gujral B, Reisinger D, Zhang S, Arora R (2017).  Deep Generalized Canonical Correlation Analysis.  arXiv preprint arXiv:1702.02519.
  • Arora R, Marinov TV, Mianjy P, Srebro N (2017).  Stochastic approximation for canonical correlation analysis.  Advances in Neural Information Processing Systems.  2017-December.  4776-4785.
  • Wang W, Arora R, Livescu K, Srebro N (2016).  Stochastic optimization for deep CCA via nonlinear orthogonal iterations.  2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015.  688-695.
  • Yu M, Dredze M, Arora R, Gormley MR (2016).  Embedding lexical features via low-rank tensors.  2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference.  1019-1029.
  • Li X, Zhao T, Arora R, Liu H, Haupt J (2016).  Stochastic variance reduced optimization for nonconvex sparse learning.  33rd International Conference on Machine Learning, ICML 2016.  2.  1448-1460.
  • Li X, Zhao T, Arora R, Liu H, Hong M (2016).  An improved convergence analysis of cyclic block coordinate descent-type methods for strongly convex minimization.  Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016.  491-499.
  • Benton A, Arora R, Dredze M (2016).  Learning multiview embeddings of Twitter users.  54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Short Papers.  14-19.
  • Schulam P, Arora R (2016).  Disease trajectory maps.  Advances in Neural Information Processing Systems.  4716-4724.
  • Li X, Haupt J, Arora R, Liu H, Hong M, Zhao T (2016).  A First Order Free Lunch for SQRT-Lasso.  arXiv preprint arXiv:1605.07950.
  • Yang LF, Arora R, Braverman V, Zhao T (2016).  The Physical Systems Behind Optimization Algorithms.  arXiv preprint arXiv:1612.02803.
  • Li X, Arora R, Liu H, Haupt J, Zhao T (2016).  Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction.  arXiv preprint arXiv:1605.02711.
  • Wang W, Arora R, Livescu K, Bilmes J (2016).  On Deep Multi-View Representation Learning: Objectives and Optimization.  arXiv preprint arXiv:1602.01024.
  • Wang W, Arora R, Livescu K, Bilmes JA (2015).  Unsupervised learning of acoustic features via deep canonical correlation analysis.  ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  2015-August.  4590-4594.
  • Wang W, Arora R, Livescu K, Bilmes J (2015).  On deep multi-view representation learning.  32nd International Conference on Machine Learning, ICML 2015.  2.  1083-1092.
  • Rastogi P, Van Durme B, Arora R (2015).  Multiview LSA: Representation learning via generalized CCA.  NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference.  556-566.
  • Woolf TB, Nutanong S, Ahmad Y, Arora R (2015).  Learning about Transitions: Adaptive Control in the Molecular Marshal (M2) Framework.  Biophysical Journal.  108(2).  379a.
  • Wang W, Arora R, Livescu K (2014).  Reconstruction of articulatory measurements with smoothed low-rank matrix completion.  2014 IEEE Workshop on Spoken Language Technology, SLT 2014 - Proceedings.  54-59.
  • Goes J, Zhang T, Arora R, Lerman G (2014).  Robust stochastic principal component analysis.  Journal of Machine Learning Research.  33.  266-274.
  • Zhao T, Yu M, Wang Y, Arora R, Liu H (2014).  Accelerated mini-batch randomized block coordinate descent method.  Advances in Neural Information Processing Systems.  4(January).  3329-3337.
  • Arora R, Livescu K (2014).  Multi-view learning with supervision for transformed bottleneck features.  ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  2499-2503.
  • Arora R, Livescu K (2013).  Multi-view CCA-based acoustic features for phonetic recognition across speakers and domains.  ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  7135-7139.
  • Arora R, Gupta MR, Kapila A, Fazel M (2013).  Similarity-based clustering by left-stochastic matrix factorization.  Journal of Machine Learning Research.  14.  1715-1746.
  • Arora R, Cotter A, Srebro N (2013).  Stochastic optimization of PCA with capped MSG.  Advances in Neural Information Processing Systems.
  • Andrew G, Arora R, Bilmes J, Livescu K (2013).  Deep canonical correlation analysis.  30th International Conference on Machine Learning, ICML 2013.  (PART 3).  2284-2292.
  • Meila M, Arora R (2013).  Consensus ranking with signed permutations.  Journal of Machine Learning Research.  31.  117-125.
  • Arora R, Cotter A, Livescu K, Srebro N (2012).  Stochastic optimization for PCA and PLS.  2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012.  861-868.
  • Arora R, Dekel O, Tewari A (2012).  Deterministic MDPs with adversarial rewards and bandit feedback.  Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012.  93-101.
  • Arora R, Dekel O, Tewari A (2012).  Online bandit learning against an adaptive adversary: From regret to policy regret.  Proceedings of the 29th International Conference on Machine Learning, ICML 2012.  2.  1503-1510.
  • Garcia E, Arora R, Gupta MR (2012).  Optimized regression for efficient function evaluation.  IEEE Transactions on Image Processing.  21(9).  4128-4140.
  • Arora R, Gupta MR, Kapila A, Fazel M (2011).  Clustering by left-stochastic matrix factorization.  Proceedings of the 28th International Conference on Machine Learning, ICML 2011.  761-768.
  • Arora R, Gupta MR (2011).  Minimizing bearing bias in tracking by de-coupled rotation and translation estimates.  Fusion 2011 - 14th International Conference on Information Fusion.
  • Arora R, Dyer CR, Hu YH, Boston N (2010).  Distributed curve matching in camera networks using projective joint invariant signatures.  ICDSC - 4th ACM/IEEE International Conference on Distributed Smart Cameras.  103-110.
  • Arora R, Sethares WA (2010).  An efficient and stable algorithm for learning rotations.  Proceedings - International Conference on Pattern Recognition.  2993-2996.
  • Arora R, Parthasarathy H (2010).  Optimal estimation and detection in homogeneous spaces.  IEEE Transactions on Signal Processing.  58(5).  2623-2635.
  • Arora R (2009).  On learning rotations.  Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference.  55-63.
  • Arora R, Sethares WA, Dewey C (2009).  Reconstructing latent periods in genome sequences with insertions and deletions.  2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009.
  • Arora R, Yu HH, Dyer C (2009).  Estimating correspondence between multiple cameras using joint invariants.  ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  805-808.
  • Arora R, Lutfi RA (2009).  An efficient code for environmental sound classification.  Journal of the Acoustical Society of America.  126(1).  7-10.
  • Arora R, Parthasarathy H (2008).  Spherical wiener filter.  Proceedings - International Conference on Image Processing, ICIP.  549-552.
  • Arora R, Sethares WA, Bucklew JA (2008).  Localizing time-varying periodicities in symbolic sequences.  ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  641-644.
  • Arora R, Sethares WA, Bucklew JA (2008).  Latent periodicities in genome sequences.  IEEE Journal on Selected Topics in Signal Processing.  2(3).  332-342.
  • Arora R, Lutfi RA (2008).  Detection-theoretic analysis of the observer-based psychophysical procedure.  Journal of the Acoustical Society of America.  123(4).  1850-1853.
  • Arora R, Parthasarathy H (2008).  Navigation using a spherical camera.  Proceedings - International Conference on Pattern Recognition.
  • Arora R, Sethares WA (2007).  Detection of periodicities in gene sequences: A maximum likelihood approach.  GENSIPS'07 - 5th IEEE International Workshop on Genomic Signal Processing and Statistics.
  • Arora R, Parthasarathy H (2007).  Wiener filter for isotropic signal fields.  Conference Record - Asilomar Conference on Signals, Systems and Computers.  540-544.
  • Arora R, Sethares W (2007).  Decomposing statistical periodicities.  IEEE Workshop on Statistical Signal Processing Proceedings.  195-199.
  • Arora R, Sethares WA (2007).  Adaptive wavetable oscillators.  IEEE Transactions on Signal Processing.  55(9).  4382-4392.
  • Sethares WA, Arora R (2007).  Equilibria of adaptive wavetable oscillators with applications to beat tracking.  ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  4.
  • Arora R, Patil S, Parthasarathy H (2006).  Computationally efficient ESPRIT-like algorithm for estimating quadratic phase coupling.  2006 IEEE 12th Digital Signal Processing Workshop and 4th IEEE Signal Processing Education Workshop.  398-403.
Conference Proceedings
  • Arora R, Paxton C, Barnoy Y, Katyal K, Hager G (2018).  Visual Robot Task Planning.  IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
  • Arora R, Benton A, Khayrallah H, Gujral B, Reisinger D, Zhang S, (2018).  Deep Generalized Canonical Correlation Analysis.  Joint International Conference on Artificial Intelligence (IJCAI).
  • Arora R, Mianjy P, Vidal R (2018).  On the Implicit Bias of Dropout.  International Conference on Machine Learning (ICML).
  • Arora R, Marinov T, Mianjy P (2018).  Streaming Principal Component Analysis in Noisy Settings.  International Conference on Machine Learning (ICML).
  • Arora R, Mianjy R (2018).  Stochastic PCA with $ell_2$ and $ell_1$ Regularization.  International Conference on Machine Learning (ICML).
  • Arora R, Li X, Jiang H, Haupt J, Liu H, Arora R, Hong M, Zhao T (2018).  On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About Its Nonsmooth Loss Function.  International Conference on Machine Learning (ICML).
  • Arora R, Braverman V, Upadhyay J (2018).  A Unified Approach to Differentially Private Clustering.  International Conference on Machine Learning (ICML).
  • Arora R, Yang L, Braverman V, Zhao T (2018).  Physical Systems behind Optimization Algorithms.  International Conference on Machine Learning (ICML).
  • Arora R, Braverman V, Upadhyay J (2018).  Differentially Private Robust PCA.  International Conference on Machine Learning (ICML).
  • Arora R, Basu A, Mianjy P, Mukherjee A (2018).  Understanding Deep Neural Networks with Rectified Linear Units.  International Conference on Learning Representations (ICLR).
  • Arora R, Mianjy P, Marinov T, Srebro N (2017).  Stochastic approximation for canonical correlation analysis.  Advances in Neural Information Processing (NIPS).
  • Vasquez-Correa JC, Orozco-Arroyave JR, Arora R, Nöth E, Dehak N, Christensen H, Rudzicz F, Bocklet T, Cernak M, Chinaei H, others (2017).  Multi-view Representation Learning Via GCCA for Multimodal Analysis of Parkinson" s Disease.  Proceedings of 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2017).  (EPFL-CONF-224545).
  • Arora R, Mianjy P, Marinov T (2016).  Stochastic optimization for multiview representation learning using partial least squares.  Proceedings of The 33rd International Conference on Machine Learning.  1786-1794.
  • Benton A, Arora R, Dredze M (2016).  Learning multiview embeddings of twitter users.  Proceedings of ACL.
  • Li X, Zhao T, Arora R, Liu H, Haupt J (2016).  Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning.  Proceedings of The 33rd International Conference on Machine Learning (ICML).
  • Yu M, Dredze M, Arora R, Gormley M (2016).  Embedding Lexical Features via Low-Rank Tensors.  Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT).
  • Li X, Zhao T, Arora R, Liu H, Hong M (2016).  An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization.  Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS).  491-499.
  • Wang W, Arora R, Livescu K, Srebro N (2015).  Stochastic optimization for deep cca via nonlinear orthogonal iterations.  Communication, Control, and Computing (Allerton), 2015 53rd Annual Allerton Conference on.  688-695.
  • Wang W, Arora R, Livescu K, Bilmes JA (2015).  Unsupervised learning of acoustic features via deep canonical correlation analysis.  Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on.  4590-4594.
  • Wang W, Arora R, Livescu K, Bilmes J (2015).  On deep multi-view representation learning.  Proc. of the 32st Int. Conf. Machine Learning (ICML 2015).  1083-1092.
  • Rastogi P, Van Durme B, Arora R (2015).  Multiview LSA: Representation Learning via Generalized CCA..  HLT-NAACL.  556-566.
  • Arora R, Cotter A, Srebro N (2013).  Stochastic optimization of PCA with capped MSG.  Advances in Neural Information Processing Systems.  1815-1823.
  • Arora R, Livescu K (2012).  Kernel CCA for multiview acoustic feature learning using articulatory measurements.  Machine Learning Symposium on Language and Speech Processing, INTERSPEECH.
  • Arora R, Bharadwaj S, Livescu K, Hasegawa-Johnson M, (2012).  Multi-View acoustic feature learning using articulatory measurements.  Workshop on Statistical Machine Learning for Speech Processing, ICASSP.
  • Arora R, Cotter A, Livescu K, Srebro N (2012).  Stochastic optimization for PCA and PLS.  Communication, Control, and Computing (Allerton), 2012 50th Annual Allerton Conference on.  861-868.
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