Alexandros G. Dimakis (Alex Dimakis)

Alexandros G. Dimakis (Alex Dimakis)
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Alexandros G. Dimakis (Alex Dimakis)
Professor,
EECS Department, UC Berkeley
Co-Founder
BespokeLabs.ai
Co-Director of the National
AI Institute for Foundations of Machine Learning
Office: 269 Cory Hall, Berkeley, CA 94720
alexdimakis at berkeley.edu
I am interested in Generative AI, Information Theory and Machine Learning.
Resume
Google Scholar Profile
News
Co-founded
BespokeLabs.ai
a startup working on data curation and data centric AI. We are hiring.
DeepSeek-R1 did not release its reasoning data. We are curating a reasoning dataset in
OpenThoughts
and training
OpenThinker
aiming to make the best open-source (open-weights and open-data) reasoning models.
N. Raoof, L. Rout, G. Daras, S. Sanghavi, C. Caramanis, S. Shakkottai, A.G. Dimakis,
"Infilling Score: A Pretraining Data Detection Algorithm for Large Language Models,"
ICLR 2025,
(Paper)
(Code)
S. Gadre, G. Smyrnis, V. Shankar, et al.
"Language models scale reliably with over-training and on downstream tasks,"
ICLR 2025,
(Arxiv)
(Code)
A. Aali, G. Daras, B. Levac, S. Kumar, A. G. Dimakis, J. Tamir
"Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data,"
ICLR 2025,
(Arxiv)
(Code)
Datacomp-LM released:
DCLM
. This is the largest public dataset for LLM training and includes 300T tokens, effective LLM per-training code based on
Open_LM
and 53 evaluations. Also we release truly open-source pretrained DCLM LMs of sizes up to 7B with excellent performance, full training data and source code.
D.J. Diaz, C. Gong, J.Ouyang-Zhang, J.M. Loy, J. Wells, D. Yang, A. D. Ellington, A. G. Dimakis and A. R. Klivans
Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations
Nature Communications
bioarxiv
Four papers accepted to NeurIPS 2023
S. Gadre, G. Ilharco, A. Fang, J. Hayase, G. Smyrnis, T. Nguyen, R. Marten, M. Wortsman, D. Ghosh, J. Zhang, E. Orgad,
R. Entezari, G. Daras, S. M. Pratt, V. Ramanujan, Y. Bitton, K. Marathe, S. Mussmann, R. Vencu, M. Cherti, R. Krishna,
P. W. Koh, O. Saukh, A. Ratner, S. Song, H. Hajishirzi, A. Farhadi, R. Beaumont, S. Oh, A.G. Dimakis,
J. Jitsev, Y. Carmon, V. Shankar, L. Schmidt,
“DataComp: In search of the next generation of multimodal datasets”
Proc. of Neural Information Processing Systems (NeurIPS), Dec. 2023. Datasets and Benchmarks Track,
Selected for Oral presentation.
(Project Page)
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)
Our paper on deep learning for time-series modeling used for electronic system design and electromagnetic (EM) interconnect analysis for signal integrity, accepted to ICCAD:
S. Ravula, V. Gorti, B. Deng, S. Chakraborty, J. Pingenot, B. Mutnury, D. Wallace, D. Winterberg, A. R. Klivans, A. G. Dimakis,
“One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers,”
2023 International Conference on Computer-Aided Design (ICCAD 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
‘‘Score-Guided 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 Pre-trained Contrastive Representations
(Project Page)
(Arxiv)
Six papers accepted to ICML 2021
Ajil Jalal
Sushrut Karmalkar
A.G. Dimakis and E. Price,
Instance-Optimal Compressed Sensing via Posterior Sampling,
(Project Page)
Ajil Jalal
Jessica Hoffmann
Sushrut Karmalkar
A.G. Dimakis and E. Price,
Fairness for Image Generation with Uncertain Sensitive Attributes
(Project page)
Giannis Daras
Joseph Dean,
Ajil Jalal
and A.G. Dimakis,
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
(Project Page)
Matt Jordan
A.G. Dimakis,
Provable Lipschitz Certification for Generative Models
(Arxiv)
(Code)
Jay Whang
Qi Lei
A.G. Dimakis
Solving Inverse Problems with a Flow-based Noise Model
(Arxiv)
Jay Whang
Erik Lindgren
, A.G. Dimakis,
Composing Normalizing Flows for Inverse Problems
(Arxiv)
Best Paper Award at UAI 2021 Workshop on Tractable Probabilistic Modeling
Congratulations to
Qi Lei
for winning the
Oden Institute Outstanding Dissertation Award
for her
awesome Phd dissertation
Recent service:
TPC chair for
MLSys 2021
AAAI 2021 Area chair
NeurIPS 2020 Area chair
New paper: Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
by G. Daras, J. Dean, A. Jalal and A.G. Dimakis.
(Code)
(Paper)
Four papers accepted to NeurIPS 2020
A. Jalal, L. Liu, A.G. Dimakis and C. Caramanis,
Robust compressed sensing of generative models,
(arxiv)
M. Jordan and A.G. Dimakis,
Exactly Computing the Local Lipschitz Constant of ReLU Networks,
(arxiv)
I. Daras, N. Kitaev, A. Odena and A.G. Dimakis
SMYRF - Efficient attention using asymmetric clustering
(arxiv)
M. Kocaoglu, S. Shakkottai, A.G. Dimakis, C. Caramanis and S. Vishwanath
Applications of Common Entropy in Causal Inference
(pdf)
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
Deep Generative models and Inverse Problems, talk at 2019 Texas AI summit
(Slides.pdf)
(Slides.pptx)
Related Video:
GANs and Compressed Sensing talk
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)
Five papers accepted to NeurIPS 2019
Matt Jordan
, Justin Lewis, Alexandros G. Dimakis
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes
(pdf)
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
Primal-Dual Block Frank-Wolfe
(pdf)
Shanshan Wu
, Sujay Sanghavi, Alexandros G. Dimakis
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models (Spotlight)
(pdf)
Shanshan Wu
, Alexandros G. Dimakis, Sujay Sanghavi
Learning Distributions Generated by One-Layer ReLU Networks
(pdf)
SysML 2019 paper on adversarial attacks on text classifiers
Q. Lei
, L. Wu, P. Chen, A.G. Dimakis, I.S. Dhillon and M. Witbrock,
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification
Systems and Machine Learning (SysML), April 2019.
(pdf)
slides
code
Press coverage:
Nature News
VentureBeat
TechTalks
ICML 2019 paper on learned compressed sensing matrices
S. Wu
, A.G. Dimakis, S. Sanghavi, F.X. Yu, D. Holtmann-Rice, D. Storcheus, A. Rostamizadeh, S. Kumar,
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
International Conference on Machine Learning (ICML), 2019.
(slides)
(pdf)
(Tensorflow Code)
New Paper accepted to NeurIPS 2018
Erik Lindgren
Murat 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
D. Van Veen, A. Jalal, E. Price, S. Vishwanath, and A.G. Dimakis
Compressed Sensing with Deep Image Prior and Learned Regularization
https:
arxiv.org
abs
1806.06438 (arxiv)
(Code)
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
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)
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. Klivans
On 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 video
Finding Dense Subgraphs via Low-Rank Bilinear Optimization
D. Papailiopoulos, I. Mitliagkas, A.G. Dimakis, C. Carmanis
ICML video
Three papers accepted in ISIT 2014.
Bounding Multiple Unicasts through Index Coding and
Locally Repairable Codes
Graph Theory versus Minimum Rank for Index Coding
Locality and Availability in Distributed Storage
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)
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