## Alexandros G. Dimakis (Alex Dimakis)
I am interested in information theory, coding theory and machine learning. Resume. ## NewsPreprint The Robust Manifold Defense: Adversarial Training using Generative Models A. Ilyas, A. Jalal, E. Asteri, C. Daskalakis, A. G. Dimakis (arxiv)
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. (preprint)
Teaching resources for Data Science and Machine learning Honored to receive the James Massey award NIPS and ICML Area chair 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. DimakisGradient 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: 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 informationTwo papers accepted to AISTATS 2017: 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: 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)
E.R. Elenberg, K. Shanmugam, M. Borokhovich and A.G. Dimakis, Beyond Triangles: A Distributed Framework for Estimating 3-profiles of Large Graphs (KDD 2015, to appear)I. Mitliagkas, M. Borokhovich, A.G. Dimakis and C. Caramanis, FrogWild!-Fast PageRank Approximations on Graph Engines (to appear in VLDB 2015). Two papers accepted to ISIT 2015 On approximating the sum-rate for multiple unicasts K. Shanmugam, M. Asteris and A.G. Dimakis Batch Codes through Dense Graphs with High Girth A.S. Rawat, Z. Song, A.G. Dimakis and A. Gal
Two papers accepted in NIPS 2014. Sparse Polynomial Learning and Graph Sketching (Oral) M. Kocaoglu, K. Shanmugam, A.G. Dimakis, A. KlivansOn the Information Theoretic Limits of Learning Ising Models R. Tandon, K. Shanmugam, P. Ravikumar, A.G. Dimakis
Batch Codes through Dense Graphs without Short Cycles A.G. Dimakis, A. Gal, A.S. Rawat, Z. Song Invited talk on coding theory for distributed storage at Algebra Codes and Networks at Bordaux. Talk Slides. Two papers accepted in ICML 2014. Nonnegative Sparse PCA with Provable Guarantees M. Asteris, D. Papailiopoulos, A.G. Dimakis ICML videoFinding Dense Subgraphs via Low-Rank Bilinear Optimization D. Papailiopoulos, I. Mitliagkas, A.G. Dimakis, C. Carmanis ICML video
Three papers accepted in ISIT 2014. Our Locally Repairable Storage codes featured on High Scalability, StorageMojo and TechCrunch. Online Milibo tutorial Erasure codes over Hadoop New paper: Sparse PCA through Low-rank Approximations (to appear in ICML 2013). video of Sparse PCA talk Our Xorbas Hadoop locally repairable codes paper will appear in VLDB 2013. Visit the Xorbas HDFS project homepage. I have moved to UT Austin. My USC homepage will no longer be updated. Received a Google Faculty Research Award. Support gratefully acknowledged. Our paper received the Communications Society & Information Theory Society Joint Paper Award.
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) |