I am a Postdoctoral Scholar with the Departments of Computer Science and Statistics at Stanford University. I work with Chris Ré and Lester Mackey.

I am currently on the job market: CV and research statement.

I am interested in the intersection of systems and theory applied to modern machine learning and data analysis. Some recent topics include:
  • Statistical learning and inference
  • Large-scale optimization
  • Data-dependent guarantees
  • Self-tuning systems
Email, Scholar, LinkedIn, Github.
That's me.

Ioannis Mitliagkas
Department of Computer Science
Stanford University
353 Serra Mall
Stanford, CA 94305
  

Recent Projects



Recent News

  • March 2017: New preprint! Custom scan sequences for super fast Gibbs sampling.
  • February 2017: Invited to talk at ITA in San Diego.
  • January 2017: Visiting Microsoft Research, Cambridge
  • December 2016: At NIPS, presenting our Gibbs sampling paper dispelling some common beliefs regarding scan orders.
  • November 2016: Visiting Microsoft Research New England
  • November 2016: Full version of asynchrony paper.
  • September 2016: Talk at Allerton
  • August 2016: I had the pleasure to give a talk MIT Lincoln Labs.
  • August 2016: Gave an asynchronous optimization talk at Google.
  • August 2016: Blog post on our momentum work.
  • July 2016: Invited to talk at NVIDIA.
  • June 2016: Poster at non-convex optimization ICML workshop.
  • June 2016: Poster at OptML 2016 workshop.
  • In a recent note, we show that asynchrony in SGD introduces momentum. In the companion systems paper, we use this theory to train deep networks faster.
  • Does periodic model averaging always help? Recent results.
  • Excited to start Postdoc at Stanford University. Will be working with Lester Mackey and Chris Ré.
  • Successfully defended my PhD thesis!
  • SILO seminar talk at the Wisconsin Institute of Discovery. Loved both Madison and the WID!
  • Densest k-Subgraph work picked up by NVIDIA!
  • Our latest work has been accepted for presentation at VLDB 2015!


Large-scale, Distributed Algorithms and Systems

 
Asynchrony begets Momentum, with an Application to Deep Learning
Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, and Christopher Ré. Presented at Allerton, 2016 [.pdf ]
 
Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs
Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, and Christopher Ré In submission, arXiv:1606.04487 (2016) [.pdf ]
 
FrogWild! Fast PageRank Approximations on Graph Engines
Ioannis Mitliagkas, Michael Borokhovich, Alex Dimakis, and Constantine Caramanis. VLDB 2015 (Earlier version at NIPS 2014 workshop). [ bib | .pdf ]
 
Finding Dense Subgraphs via Low-rank Bilinear Optimization
Dimitris S Papailiopoulos, Ioannis Mitliagkas, Alexandros G Dimakis, and Constantine Caramanis. ICML, 2014. [ bib | .pdf ]
 
Distributed joint power and admission control for ad-hoc and cognitive underlay networks
I. Mitliagkas, ND Sidiropoulos, and A. Swami. In Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, pages 3014-3017. IEEE. [ bib ]


Machine Learning Theory

 
Improving Gibbs Sampler Scan Quality with DoGS
Ioannis Mitliagkas and Lester Mackey In submission [.pdf ]
 
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much
Bryan He, Christopher De Sa, Ioannis Mitliagkas, and Christopher Ré NIPS 2016 [.pdf ]
 
Parallel SGD: When does averaging help?
Jian Zhang, Christopher De Sa, Ioannis Mitliagkas, and Christopher Ré OptML workshop at ICML 2016 [.pdf ]
 
Streaming PCA with Many Missing Entries
Ioannis Mitliagkas, Constantine Caramanis, and Prateek Jain. Preprint, 2015. [ bib | .pdf ]
 
Memory Limited, Streaming PCA
Ioannis Mitliagkas, Constantine Caramanis, and Prateek Jain. NIPS 2013, 2013. [ bib | .pdf ]
 
User Rankings from Comparisons: Learning Permutations in High Dimensions
I. Mitliagkas, A. Gopalan, C. Caramanis, and S. Vishwanath. In Proc. of Allerton Conf. on Communication, Control and Computing, Monticello, USA, 2011. [ bib | .pdf ]
 
Strong Information-Theoretic Limits for Source/Model Recovery
I. Mitliagkas and S. Vishwanath. In Proc. of Allerton Conf. on Communication, Control and Computing, Monticello, USA, 2010. [ bib | .pdf ]


Older Publications

 
Joint Power and Admission Control for Ad-hoc and Cognitive Underlay Networks: Convex Approximation and Distributed Implementation
I. Mitliagkas, ND Sidiropoulos, and A. Swami. IEEE Transactions on Wireless Communications, 2011. [ bib ]
 
Distributed joint power and admission control for ad-hoc and cognitive underlay networks
I. Mitliagkas, ND Sidiropoulos, and A. Swami. In Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, pages 3014-3017. IEEE. [ bib ]
 
Convex approximation-based joint power and admission control for cognitive underlay networks
I. Mitliagkas, ND Sidiropoulos, and A. Swami. In Wireless Communications and Mobile Computing Conference, 2008. IWCMC'08. International, pages 28-32. IEEE. [ bib ]

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