Constantine Caramanis
55 papers · 2008–2025 · 8 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (17) π Conference Polyglot (8)
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Cross-Pollinator
(9)
πΊοΈ
Taxonomy Completionist
(17)
π§
Keyword Pioneer
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Conference Loyalist
(23)
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Keyword Trendsetter Combo
(3)
π€
Dynamic Duo
(13)
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Keyword Champion
(3)
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Grand Slam
π¬
Deep Specialist
(21)
ποΈ
Keyword Collector
(62)
π₯
Unstoppable
(7)
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Trend Setter
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Conference Pioneer
β‘
Prolific Year
(6)
π
Century Club
(55)
Conferences
NIPS (23)
ICML (13)
AISTATS (7)
COLT (5)
ICLR (3)
JMLR (2)
AAAI (1)
CVPR (1)
Top co-authors
Keywords
sample complexity
(7)
multi-armed bandit
(7)
regret bound
(6)
contextual bandit
(4)
convergence rate
(4)
expectation maximization
(4)
sparse regression
(4)
non-convex optimization
(4)
convex optimization
(4)
minimax optimality
(4)
combinatorial optimization
(3)
stochastic optimization
(3)
reinforcement learning
(3)
robust optimization
(3)
parameter estimation
(3)
dimensionality reduction
(3)
statistical learning
(3)
upper confidence bound
(3)
computational complexity
(2)
stochastic gradient descent
(2)
Papers
RB-Modulation: Training-Free Stylization using Reference-Based Modulation
ICLR 2025
On Mitigating Affinity Bias through Bandits with Evolving Biased Feedback
ICML 2025
Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations
ICLR 2025
Infilling Score: A Pretraining Data Detection Algorithm for Large Language Models
ICLR 2025
Optimization Can Learn Johnson Lindenstrauss Embeddings
NIPS 2024
RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation
NIPS 2024
Contextual Pandoraβs Box
AAAI 2024
On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing
JMLR 2024
Prospective Side Information for Latent MDPs
ICML 2024
Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion
CVPR 2024
Finite-Time Logarithmic Bayes Regret Upper Bounds
NIPS 2023
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Method
NIPS 2023
Reward-Mixing MDPs with Few Latent Contexts are Learnable
ICML 2023
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models
NIPS 2023
Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD
COLT 2023
Asymptotically-Optimal Gaussian Bandits with Side Observations
ICML 2022
Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret
NIPS 2022
Tractable Optimality in Episodic Latent MABs
NIPS 2022
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance
COLT 2022
Recoverability Landscape of Tree Structured Markov Random Fields under Symmetric Noise
AISTATS 2022
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms
ICML 2022
Reinforcement Learning in Reward-Mixing MDPs
NIPS 2021
Contextual Blocking Bandits
AISTATS 2021
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression
AISTATS 2021
Combinatorial Blocking Bandits with Stochastic Delays
ICML 2021
Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits
NIPS 2021
RL for Latent MDPs: Regret Guarantees and a Lower Bound
NIPS 2021
The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians
COLT 2020
Robust compressed sensing using generative models
NIPS 2020
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
NIPS 2020
Applications of Common Entropy for Causal Inference
NIPS 2020
Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking
NIPS 2020
High Dimensional Robust Sparse Regression
AISTATS 2020
Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls
AISTATS 2020
EM Converges for a Mixture of Many Linear Regressions
AISTATS 2020
Learning Mixtures of Graphs from Epidemic Cascades
ICML 2020
Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression
COLT 2019
Primal-Dual Block Generalized Frank-Wolfe
NIPS 2019
Robust Estimation of Tree Structured Gaussian Graphical Models
ICML 2019
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning
NIPS 2016
Fast Algorithms for Robust PCA via Gradient Descent
NIPS 2016
Binary Embedding: Fundamental Limits and Fast Algorithm
ICML 2015
Optimal Linear Estimation under Unknown Nonlinear Transform
NIPS 2015
Regularized EM Algorithms: A Unified Framework and Statistical Guarantees
NIPS 2015
A Convex Formulation for Mixed Regression with Two Components: Minimax Optimal Rates
COLT 2014
Alternating Minimization for Mixed Linear Regression
ICML 2014
Finding Dense Subgraphs via Low-Rank Bilinear Optimization
ICML 2014
Greedy Subspace Clustering
NIPS 2014
Memory Limited, Streaming PCA
NIPS 2013
Robust Sparse Regression under Adversarial Corruption
ICML 2013
Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery
ICML 2013
Statistical Optimization in High Dimensions
AISTATS 2012
Robust PCA via Outlier Pursuit
NIPS 2010
Robustness and Regularization of Support Vector Machines
JMLR 2009
Robust Regression and Lasso
NIPS 2008