Alexandros G. Dimakis (Alex Dimakis)

Alex Dimakis Photo 

dimakis at

I am interested in information theory, coding theory and machine learning. Resume.

Google Scholar Profile.


  • Deep Generative models and Inverse Problems, talk at 2019 Texas AI summit
    (Slides.pdf) (Slides.pptx)
    Related Video: GANs and Compressed Sensing talk

  • New Paper accepted to NeurIPS 2018

    • E. Lindgren, M. Kocaoglu, A.G. Dimakis and Sriram Vishwanath
      Experimental Design for Cost-Aware Learning of Causal Graphs
      Neural Information Processing Systems, 2019. (pdf) (Short video ft. Erik's radio voice)

  • Preprint

  • Preprint

    • The Robust Manifold Defense: Adversarial Training using Generative Models
      A. Ilyas, A. Jalal, E. Asteri, C. Daskalakis, A. G. Dimakis

  • Paper accepted to Annals of Statistics

    • E. R. Elenberg, R. Khanna, A.G. Dimakis, and S. Negahban
      ‘‘Restricted Strong Convexity Implies Weak Submodularity’’,
      to appear in Annals of Statistics, 2018.

  • Teaching resources for Data Science and Machine learning

  • Honored to receive the James Massey award

  • NeurIPS, ICML, AISTATS Area chair

  • Compressed Sensing using Generative models Pre-trained models see also GitHub CSGM

  • ICLR 2018

    • A. Bora, E. Price, A.G. Dimakis
      AmbientGAN: Generative models from lossy measurements
      (Oral Presentation) (openreview), (code)

    • M. Kocaoglu, C. Snyder, A.G. Dimakis and S. Vishwanath
      CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
      (arxiv) (code)

  • NIPS 2017:

    • E.R. Elenberg, A.G. Dimakis, M. Feldman, and A. Karbasi
      ‘‘Streaming Weak Submodularity: Interpreting Neural Networks on the Fly’’,
      (NIPS), 2017. (Oral presentation) (arxiv) code

    • Model-Powered Conditional Independence Test R. Sen, A.T. Suresh, K. Shanmugam, A.G. Dimakis and S. Shakkottai (NIPS), 2017.

  • Preprint

    • Gradient Coding from Cyclic MDS Codes and Expander Graphs
      N Raviv, I Tamo, R Tandon, AG Dimakis (arxiv)

  • Six papers accepted to ICML 2017:

    • Compressed Sensing using Generative Models (arxiv) Code and Demo
      A. Bora, A. Jalal, E. Price, A. G. Dimakis

    • Gradient Coding (pdf)
      R. Tandon, Q. Lei, A. G. Dimakis, N. Karampatziakis. (Slides from ITA)

    • On Approximation Guarantees for Greedy Low Rank Optimization (arxiv)
      R. Khanna, E. Elenberg, A. G. Dimakis, S. Negahban.

    • Exact MAP Inference by Avoiding Fractional Vertices (arxiv)
      E. M. Lindgren, A. G. Dimakis, A. Klivans.

    • Cost-Optimal Learning of Causal Graphs (arxiv)
      M. Kocaoglu, A. G. Dimakis, S. Vishwanath.

    • Identifying Best Interventions through Online Importance Sampling (arxiv)
      R. Sen, K. Shanmugam, A. G. Dimakis and S. Shakkottai.

  • Two papers accepted to ISIT 2017:

    • Entropic Causality and Greedy Minimum Entropy Coupling (arxiv)
      M. Kocaoglu, A. G. Dimakis, S. Vishwanath and B. Hassibi.

    • Coded Caching with Linear Subpacketization is Possible using Ruzsa-Szeméredi Graphs. (arxiv)
      K. Shanmugam, A. M. Tulino and A. G. Dimakis.

  • Recent work on decoding brain signals using interpretable features:

    • H. Yi, Z. Xie, R. Reetzke, A.G. Dimakis and B. Chandrasekaran.
      Vowel decoding from single-trial speech-evoked electrophysiological responses: A feature-based machine learning approach.
      Brain and Behavior. April 2017; (Open Access)

  • Check the cool projects from our undergraduate Data Science Lab course

  • Entropic Causality paper to appear in AAAI 2017. (Arxiv)
    More information

  • Two papers accepted to AISTATS 2017:

    • Contextual Bandits with Latent Confounders: An NMF Approach (pdf)
      R. Sen, K. Shanmugam, M. Kocaoglu, A. G. Dimakis and S. Shakkottai.

    • Scalable Greedy Feature Selection via Weak Submodularity. (pdf)
      R. Khanna, E. Elenberg, A. G. Dimakis, S. Neghaban and J. Ghosh

  • Slides and notes from GraphDay overview talk on graph analytics and machine learning (pdf)

  • Upcoming talk at Canadian Workshop on Information Theory (CWIT) (CWIT link)

  • Two papers accepted to NIPS 2016:

    • Leveraging Sparsity for Efficient Submodular Data Summarization
      E. Lindgren, S. Wu, A. G. Dimakis (pdf)

    • Single Pass PCA of Matrix Products S. Wu, S. Bhojanapalli, S. Sanghavi, A. G. Dimakis (pdf)

  • Preprint: Restricted Strong Convexity Implies Weak Submodularity
    E. Elenberg, R. Khanna, A. G. Dimakis, S. Negahban (pdf)

  • Distributed Estimation of Graph 4-profiles
    E. R. Elenberg, K. Shanmugam, M. Borokhovich, A. G. Dimakis.
    in Proc. International World Wide Web Conference (WWW), 2016 (Arxiv)

  • Bipartite Correlation Clustering: Maximizing Agreements
    M. Asteris, A. Kyrillidis, D. Papailiopoulos, A. G. Dimakis, AISTATS 2016 (pdf)

  • Three papers accepted to NIPS 2015

    • Orthogonal NMF through Subspace Exploration
      M. Asteris D. Papailiopoulos A. G. Dimakis (pdf)

    • Sparse PCA via Bipartite Matchings
      M. Asteris D. Papailiopoulos A. Kyrillidis A. G. Dimakis (pdf)

    • Learning Causal Graphs with Small Interventions
      K. Shanmugam, M. Kocaoglu, A.G. Dimakis, S. Vishwanath (arxiv)

  • Stay on path: PCA along graph paths M. Asteris A. Kyrillidis A. G. Dimakis H. Yi B. Chandrasekaran International Conference on Machine Learning (ICML), Lille, France, 2015, (pdf) (slides)

I maintain the Distributed Storage Wiki, an online bibliography about theoretical problems in large-scale distributed storage systems. (Temprary offline, will need new host for this)