Alex Dimakis
33 papers · 2015–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Conference Polyglot (7) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (11) π Academic Marathon (10)
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Academic Marathon
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(11)
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Renaissance Researcher
(6)
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Topic Evolution
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Triple Crown
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Mega-Team
(34)
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(3)
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(11)
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Keyword Collector
(142)
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Prolific Year
(6)
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Unstoppable
(9)
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Trend Setter
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Century Club
(33)
Conferences
ICML (15)
NIPS (7)
ICLR (4)
AISTATS (3)
ECCV (2)
CVPR (1)
EMNLP (1)
Top co-authors
Keywords
generative model
(6)
compressed sensing
(5)
diffusion model
(4)
inverse problem
(4)
posterior sampling
(4)
generative modeling
(2)
image restoration
(2)
normalizing flow
(2)
neural network
(2)
representation learning
(2)
image denoising
(2)
distributed learning
(2)
image generation
(2)
zero-shot learning
(1)
stochastic gradient descent
(1)
contrastive learning
(1)
causal inference
(1)
wasserstein distance
(1)
principal component analysis
(1)
langevin dynamics
(1)
Papers
Geometric Median (GM) Matching for Robust k-Subset Selection from Noisy Data
ICML 2025
Viewpoint Rosetta Stone: Unlocking Unpaired Ego-Exo Videos for View-invariant Representation Learning
CVPR 2025
Large Language Models as Realistic Microservice Trace Generators
EMNLP 2025
Infilling Score: A Pretraining Data Detection Algorithm for Large Language Models
ICLR 2025
Language models scale reliably with over-training and on downstream tasks
ICLR 2025
Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data
ICLR 2025
4Diff: 3D-Aware Diffusion Model for Third-to-First Viewpoint Translation
ECCV 2024
SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
NIPS 2024
Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data
ICML 2024
Put Myself in Your Shoes: Lifting the Egocentric Perspective from Exocentric Videos
ECCV 2024
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models
NIPS 2023
HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing
ICLR 2023
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers
ICML 2023
Ambient Diffusion: Learning Clean Distributions from Corrupted Data
NIPS 2023
DataComp: In search of the next generation of multimodal datasets
NIPS 2023
Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent
NIPS 2023
Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems
ICML 2022
Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve
NIPS 2022
Zonotope Domains for Lagrangian Neural Network Verification
NIPS 2022
Solving Inverse Problems with a Flow-based Noise Model
ICML 2021
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
ICML 2021
Fairness for Image Generation with Uncertain Sensitive Attributes
ICML 2021
Instance-Optimal Compressed Sensing via Posterior Sampling
ICML 2021
Provable Lipschitz Certification for Generative Models
ICML 2021
Composing Normalizing Flows for Inverse Problems
ICML 2021
SGD Learns One-Layer Networks in WGANs
ICML 2020
Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls
AISTATS 2020
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
ICML 2019
Gradient Coding from Cyclic MDS Codes and Expander Graphs
ICML 2018
Contextual Bandits with Latent Confounders: An NMF Approach
AISTATS 2017
Cost-Optimal Learning of Causal Graphs
ICML 2017
Scalable Greedy Feature Selection via Weak Submodularity
AISTATS 2017
Stay on path: PCA along graph paths
ICML 2015