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Constantine Caramanis

55 papers · 2008–2025 · 8 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸƒ Academic Marathon (17) 🌍 Conference Polyglot (8) 🌈 Renaissance Researcher (8) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (9) πŸ—ΊοΈ Taxonomy Completionist (17) 🧭 Keyword Pioneer 🏠 Conference Loyalist (23) 🌟 Keyword Trendsetter Combo (3) 🀝 Dynamic Duo (13) πŸ† Keyword Champion (3) πŸ† Grand Slam πŸ”¬ Deep Specialist (21) πŸ—ƒοΈ Keyword Collector (62) πŸ”₯ Unstoppable (7) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer ⚑ Prolific Year (6) πŸ’Ž Century Club (55)

Conferences

NIPS (23) ICML (13) AISTATS (7) COLT (5) ICLR (3) JMLR (2) AAAI (1) CVPR (1)

Top co-authors

Sanjay Shakkottai (13) Jeongyeol Kwon (12) Shie Mannor (11) Yonathan Efroni (7) Xinyang Yi (7) Litu Rout (6) Sujay Sanghavi (6) Orestis Papadigenopoulos (6) Yudong Chen (5) Matthew Faw (4)

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 Pandora’s 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