Alexandros G. Dimakis (Alex Dimakis)

dimakis at austin.utexas.edu

I am interested in information theory, coding theory and machine learning.
Resume.
Google Scholar Profile.
News

G. Daras, K. Shah, Y. Dagan, A. Gollakota, A. G. Dimakis, A. R. Klivans,
“Ambient Diffusion: Learning Clean Distributions from Corrupted Data,”
Proc. of Neural Information Processing Systems (NeurIPS), Dec. 2023.
(Project Page)
(Arxiv),
(Code).

L. Rout, N. Raoof, G. Daras, C. Caramanis, A. G. Dimakis, S. Shakkottai,
“Solving Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models,”
Proc. of Neural Information Processing Systems (NeurIPS), Dec. 2023.
(Project Page),
(Arxiv),(Code)

G. Daras, Y. Dagan, A.G. Dimakis, C. Daskalakis,
“Martingale Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent,
” Proc. of Neural Information Processing Systems (NeurIPS), Dec. 2023.
(Project Page)
T. Chen, C. Gong, D. J. Diaz, X. Chen, J. T. Wells, Q. Liu, Z. Wang, A. D. Ellington, A.G. Dimakis, A. R. Klivans,
“HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing,”
Proceedings on the International Conference on Representation Learning (ICLR 2023).
Elected IEEE Fellow for contributions to distributed coding and learning, 2022.
Selected as Commissioner. Artificial Intelligence Commission on Competition, Inclusion, and Innovation
by the US Chamber of Commerce to provide a roadmap for tech leadership to US policy makers.
Faculty of the year award (for 2022). MS Program in Information Technology Management, (Voted by students).
Keynote speaker, 14th IEEE Image and Multidimensional Signal Processing Workshop (IVMSP) 2022.
Plenary speaker, 13th International Conference on the Image, 2022.
Best Paper Award at UAI 2021 Workshop on Tractable Probabilistic Modeling.
Recent talk: Generative models are the new sparsity
Recent Berkeley Simons talk on deep generative models and how they can be used to solve inverse problems including Denoising, Missing data, Compressed Sensing and MRI.
G. Daras, N. Raoof, Z. Gkalitsiou and A.G. Dimakis
‘‘Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve,’’
Proc. of Neural Information Processing Systems (NeurIPS), Dec. 2022.
(Project Page) arxiv
M. Jordan, J. Hayase, A. G. Dimakis, S. Oh
‘‘Zonotope Domains for Lagrangian Neural Network Verification,’’
Proc. of Neural Information Processing Systems (NeurIPS), Dec. 2022.
(Arxiv)
G. Daras, Y. Dagan, A. G. Dimakis, C. Daskalakis
‘‘ScoreGuided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems.’’
International Conference on Machine Learning (ICML), 2022.
(Project Page)
J. Whang, M. Delbracio, H. Talebi, C. Saharia, A. G. Dimakis, P. Milanfar,
‘‘Deblurring via Stochastic Refinement.’’
Computer Vision and Pattern Recognition (CVPR) June 2022. (Oral Presentation)
(Arxiv)
Two papers accepted to NeurIPS 2021
Ajil Jalal, Marius Arvinte,
Giannis Daras, Eric Price,
Alexandros G. Dimakis and Jonathan I. Tamir,
Robust Compressed Sensing MRI with Deep Generative Priors,
(Project Page), (Arxiv)
Sriram Ravula,
Georgios Smyrnis ,Matt Jordan, Alexandros G. Dimakis
Inverse Problems Leveraging Pretrained Contrastive Representations
(Project Page)
(Arxiv)
New Survey: Deep Learning Techniques for Inverse Problems in Imaging
G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis and R. Willett
Journal on Selected Areas in Information Theory, May 2020.
(arxiv), ieeeXplore
Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models
Giannis Daras, Augustus Odena, Han Zhang, Alexandros G. Dimakis, CVPR 2020.
(arxiv)  Code  Colab Notebook  Twitter Thread
Gradient Coding from Cyclic MDS Codes and Expander Graphs
N. Raviv, I. Tamo, R. Tandon and A. G. Dimakis, IEEE Transactions on Information Theory
(arxiv)

Qi Lei, Ajil Jalal, Inderjit S. Dhillon, and Alexandros G. Dimakis
Inverting Deep Generative models, One layer at a time.
(pdf)

Qi Lei, Jiacheng Zhuo, Constantine Caramanis, Inderjit S Dhillon, Alexandros G Dimakis
PrimalDual Block FrankWolfe
(pdf)
New Paper accepted to NeurIPS 2018
Preprint
Preprint
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
NeurIPS, ICML, AISTATS Area chair
Compressed Sensing using Generative models
Pretrained 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
ModelPowered 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.
CostOptimal 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 RuzsaSzeméredi 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 singletrial speechevoked electrophysiological responses: A featurebased 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 4profiles
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 3profiles 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
Two papers accepted in NIPS 2014.
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.
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 Lowrank 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 largescale distributed storage systems.
(Temprary offline, will need new host for this)
