Christos Tzamos
41 papers · 2017–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (11) π§ Keyword Pioneer π Conference Polyglot (6)
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Keyword Pioneer
π£
Hot Topic Early Bird
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Cross-Pollinator
(15)
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Dynamic Duo
(16)
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Keyword Champion
(2)
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Grand Slam
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Deep Specialist
(11)
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Century Club
(41)
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Prolific Year
(5)
ποΈ
Keyword Collector
(158)
π₯
Unstoppable
(9)
Conferences
COLT (15)
NIPS (13)
ICML (10)
AAAI (1)
AISTATS (1)
ICLR (1)
Top co-authors
Research topics
Keywords
massart noise
(6)
stochastic optimization
(5)
halfspace learning
(4)
stochastic gradient descent
(4)
pac learning
(4)
adversarial label noise
(4)
maximum likelihood estimation
(3)
log-concave distribution
(3)
sample complexity
(3)
noisy label
(2)
query complexity
(2)
online algorithm
(2)
approximation algorithm
(2)
non-convex optimization
(2)
active learning
(2)
agnostic learning
(2)
expectation maximization
(2)
combinatorial optimization
(2)
online learning
(2)
statistical query
(2)
Papers
Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise
ICML 2025
Online Linear Classification with Massart Noise
ICML 2025
Oracle efficient truncated statistics
ICLR 2025
Fast Co-Training under Weak Dependence via Stream-Based Active Learning
ICML 2024
Active Classification with Few Queries under Misspecification
NIPS 2024
Optimization Can Learn Johnson Lindenstrauss Embeddings
NIPS 2024
Contextual Pandoraβs Box
AAAI 2024
Weitzman's Rule for Pandora's Box with Correlations
NIPS 2023
Self-Directed Linear Classification
COLT 2023
Buying Information for Stochastic Optimization
ICML 2023
Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions
COLT 2023
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Method
NIPS 2023
First Order Stochastic Optimization with Oblivious Noise
NIPS 2023
The Gain from Ordering in Online Learning
NIPS 2023
Linear Label Ranking with Bounded Noise
NIPS 2022
Perfect Sampling from Pairwise Comparisons
NIPS 2022
Learning a Single Neuron with Adversarial Label Noise via Gradient Descent
COLT 2022
Clustering with Queries under Semi-Random Noise
COLT 2022
Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent
ICML 2022
Online Learning for Min Sum Set Cover and Pandoraβs Box
ICML 2022
Learning Online Algorithms with Distributional Advice
ICML 2021
On Robust Mean Estimation under Coordinate-level Corruption
ICML 2021
A Statistical Taylor Theorem and Extrapolation of Truncated Densities
COLT 2021
Agnostic Proper Learning of Halfspaces under Gaussian Marginals
COLT 2021
Boosting in the Presence of Massart Noise
COLT 2021
Efficient Algorithms for Learning from Coarse Labels
COLT 2021
ReLU Regression with Massart Noise
NIPS 2021
Forster Decomposition and Learning Halfspaces with Noise
NIPS 2021
Learning Halfspaces with Massart Noise Under Structured Distributions
COLT 2020
Optimal Private Median Estimation under Minimal Distributional Assumptions
NIPS 2020
Efficient Parameter Estimation of Truncated Boolean Product Distributions
COLT 2020
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise
NIPS 2020
Black-Box Methods for Restoring Monotonicity
ICML 2020
Computationally and Statistically Efficient Truncated Regression
COLT 2019
Distribution-Independent PAC Learning of Halfspaces with Massart Noise
NIPS 2019
Learning to Prune: Speeding up Repeated Computations
COLT 2019
Bootstrapping EM via Power EM and Convergence in the Naive Bayes Model
AISTATS 2018
Actively Avoiding Nonsense in Generative Models
COLT 2018
Certified Computation from Unreliable Datasets
COLT 2018
Ten Steps of EM Suffice for Mixtures of Two Gaussians
COLT 2017
Improving Viterbi is Hard: Better Runtimes Imply Faster Clique Algorithms
ICML 2017