Dimitris Papailiopoulos
Assistant Professor @ UW-Madison
Electrical and Computer Engineering
Computer Sciences (by courtesy)
I am an assistant professor of Electrical and Computer Engineering and Computer Sciences (by courtesy) at the University of Wisconsin-Madison, a faculty fellow at the Grainger Institute, and a faculty affiliate with the Optimization group at the Wisconsin Institute for Discovery.
My research lies in the intersection of machine learning, coding theory, and distributed systems. I am particularly interested in the theory and practice of large-scale machine learning and the challenges that arise due to communication bottlenecks, straggler nodes, and adversarial attacks.
Before coming to Madison, I spent two wonderful years as a postdoc at UC Berkeley, where I was a member of the AMPLab and BLISS, and had the pleasure to collaborate with
Ben Recht and Kannan Ramchandran.
I received my Ph.D. in 2014 from UT Austin, where I was fortunate to be advised by
Alex Dimakis. Before UT, I spent 3.5 years as a grad student at USC.
Before all that, I received my M.Sc. (2009) and ECE Diploma (2007) from the Technical University of Crete (TUC), located in the beautiful city of Chania.
Google Scholar
News
Very excited to be co-organizing SysML! Please consider submitting your best Sys+ML papers.
Teaching
ECE901/CS838: Large-scale Machine Learning and Optimization (Spring 2018, Fall 2016) ECE330: Signals and Systems (Fall 2017, Fall 2018)
Publications
2018
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ATOMO: Communication-efficient Learning via Atomic SparsificationNIPS 2018. [arxiv]
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The Effect of Network Width on the Performance of Large-batch TrainingNIPS 2018. [arxiv]
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DRACO: Byzantine-resilient Distributed Training via Redundant GradientsICML 2018. [arxiv]
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Stability and Generalization of Learning Algorithms that Converge to Global OptimaICML 2018. [arxiv]
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Gradient Coding Using the Stochastic Block ModelISIT 2018. [arxiv]
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Gradient Diversity: a Key Ingredient for Scalable Distributed LearningAISTATS 2018. [arxiv]
2017
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Gradient Diversity: a Key Ingredient for Scalable Distributed LearningNIPS 2017, OPT Workshop (oral). [arxiv]
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UberShuffle: Communication-efficient Data Shuffling for SGD via Coding TheoryNIPS 2017, ML Systems Workshop [pdf]
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Perturbed Iterate Analysis for Asynchronous Stochastic OptimizationSIAM Journal on Optimization. [arxiv]
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Speeding-up Distributed Machine Learning Using CodesIEEE Transactions on Information Theory. [arxiv]
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Coded Computation for Multicore SetupsISIT 2017. [pdf]
2016
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CYCLADES: Conflict-free Asynchronous Machine LearningNIPS 2016. [arxiv]
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Speeding-up Distributed Machine Learning Using CodesISIT 2016. [arxiv]
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On the Worst-Case Approximability of Sparse PCACOLT 2016. [arxiv]
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Bipartite Correlation Clustering - Maximizing AgreementsAISTATS 2016. [arxiv]
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Locality and Availability in Distributed StorageIEEE Transactions on Information Theory. [preprint]
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Optimal Locally Repairable Codes and Connections to Matroid TheoryIEEE Trans. on Information Theory. [preprint]
2015
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Perturbed Iterate Analysis for Asynchronous Stochastic OptimizationNIPS 2015, OPT Workshop (oral). [long version]
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Speeding-up Distributed Machine Learning Using CodesNIPS 2015, ML-Systems Workshop. [arxiv]
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Parallel Correlation Clustering on Big GraphsNIPS 2015. [long version]
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Sparse PCA via Bipartite MatchingsNIPS 2015. [long version]
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Orthogonal NMF through Subspace ExplorationNIPS 2015. [long version]
2014
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Provable Deterministic Leverage Score SamplingKDD 2014. [long version]
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Finding Dense Subgraphs via Low-Rank Bilinear Optimization
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Nonnegative Sparse PCA with Provable Guarantees
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Locality and Availability in Distributed StorageISIT 2014. [long version]
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Locally Repairable CodesIEEE Transactions on Information Theory, September 2014. ,
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A Repair Framework for Scalar MDS CodesIEEE Journal on Selected Areas in Communications, May 2014. ,
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The Sparse Principal Component of a Constant-rank MatrixIEEE Transactions on Information Theory, March 2014. , implementation by Megas.
2013
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Sparse PCA through Low-rank Approximations
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Optimal Locally Repairable Codes and Connections to Matroid TheoryISIT 2013. [IEEEXplore], [arXiv]
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XORing Elephants: Novel Erasure Codes for Big Data
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Repair Optimal Erasure Codes through Hadamard DesignsIEEE Transactions on Information Theory, May 2013. [IEEEXplore], [arXiv]
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Maximum-Likelihood Noncoherent PAM DetectionIEEE Transactions on Communications, March 2013. [IEEEXplore], [draft]
2012
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A Repair Framework for Scalar MDS CodesAllerton 2012. [IEEEXplore], [arXiv]
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Locally Repairable Codes
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Feedback in the K-user Interference channel
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Simple Regenerating Codes: Network Coding for Cloud Storage
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Maximum-likelihood Blind PAM DetectionICC 2012. [IEEEXplore], [long]
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Interference Alignment as a Rank Constrained Rank Minimization
2011
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Repair Optimal Erasure Codes through Hadamard Designs
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Distributed Storage Codes through Hadamard Designs
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Sparse Principal Component of a Rank-deficient Matrix
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Repairing Erasure CodesUSENIX FAST 2011, Work-In-Progress (WiP). [short abstract]
2010
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Distributed Storage Codes Meet Multiple-Access Wiretap Channels
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MCMC Methods for Integer Least-Squares ProblemsAllerton 2010. [IEEEXplore], [draft]
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Interference Alignment as a Rank Constrained Rank Minimization
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Maximum-likelihood Noncoherent OSTBC Detection with Polynomial ComplexityIEEE Transactions on Wireless Communications, June 2010. [IEEEXplore], [draft]
2007-2009
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Optimal OSTBC Sequence Detection over Unknown Correlated Fading ChannelsAsilomar 2009. [IEEEXplore], [draft]
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Efficient maximum-likelihood noncoherent orthogonal STBC detection
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Polynomial-complexity maximum-likelihood block noncoherent MPSK detectionICASSP 2008. [IEEEXplore], [draft]
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Near ML detection of nonlinearly distorted OFDM signals
Collaborators
I am very grateful to have collaborated with an amazing set of people, listed below in reverse chronological order.
Zachary Charles (UW-Madison), Jordan Ellenberg (UW-Madison), Jichan Chung (KAIST), Dong Yin (UC Berkeley), Ashwin Pananjady (UC Berkeley), Peter L. Bartlett (UC Berkeley), Chris Ré (Stanford), Ce Zhang (ETH Zurich), Stephen Tu (UC Berkeley), Ramtin Pedarsani (UC Santa Barbara), Maximilian Lam (Stanford), Kangwook Lee (KAIST), Siu On Chan (CUHK), Aviad Rubinstein (Stanford) Horia Mania (UC Berkeley), Michael I. Jordan (UC Berkeley), Kannan Ramchandran (UC Berkeley), Ben Recht (UC Berkeley), Samet Oymak (UC Riverside), Xinghao Pan (UC Berkeley), Christos Boutsidis (Goldman Sachs), Anastasios Kyrillidis (IBM TJ Watson), Sriram Vishwanath (UT Austin), Ankit Singh Rawat (MIT), Constantine Caramanis (UT Austin), Ioannis Mitliagkas (UMontreal), Dhruba Borthakur (facebook), Scott Chen (facebook), Ramkumar Vadali (facebook), Maheswaran Sathiamoorthy (Google), Stavros Korokythakis (Stochastic Technologies), Itzhak Tamo (Tel Aviv University), Guiseppe Caire (TU Berlin), Karthikeyan Shanmugam (IBM TJ Watson), Jin Li (MSR Redmond), Cheng Huang (MSR Redmond), Jianqiang Luo (Google), Georgina Abou-Elkheir (UPiraeus), Changho Suh (KAIST), Viveck Cadambe (Penn State), Asteris Megasthenis (Google), Babak Hassibi (Caltech), Alex Dimakis (UT Austin), George Karystinos (TUC).