Jonathan Scarlett
39 papers · 2015–2025 · 9 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Conference Polyglot (9) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (15) π Academic Marathon (10)
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Renaissance Researcher
(6)
π§
Keyword Pioneer
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Cross-Pollinator
(12)
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Dynamic Duo
(10)
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Triple Crown
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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
(39)
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Prolific Year
(6)
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Keyword Collector
(136)
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Trend Setter
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Unstoppable
(11)
Conferences
AISTATS (11)
NIPS (9)
ICML (8)
AAAI (4)
ALT (2)
COLT (2)
CVPR (1)
ICLR (1)
UAI (1)
Top co-authors
Keywords
gaussian process
(14)
regret bound
(12)
bayesian optimization
(12)
multi-armed bandit
(5)
sample complexity
(5)
kernel methods
(5)
robust optimization
(4)
group testing
(4)
reproducing kernel hilbert space
(4)
bandit optimization
(4)
compressed sensing
(4)
black-box optimization
(3)
generative prior
(3)
compressive sensing
(2)
sparse recovery
(2)
combinatorial optimization
(2)
upper confidence bound
(2)
graphical model selection
(2)
adversarial attack
(2)
gibbs sampling
(2)
Papers
Lower Bounds for Time-Varying Kernelized Bandits
AISTATS 2025
Quantile Multi-Armed Bandits with 1-bit Feedback
ALT 2025
Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization
NIPS 2024
Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization
AAAI 2024
No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints
AISTATS 2024
Max-Quantile Grouped Infinite-Arm Bandits
ALT 2023
A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing
NIPS 2023
Communication-Constrained Bandits under Additive Gaussian Noise
ICML 2023
Benefits of monotonicity in safe exploration with Gaussian processes
UAI 2023
Adversarial Attacks on Gaussian Process Bandits
ICML 2022
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
ICML 2022
Max-Min Grouped Bandits
AAAI 2022
A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits
NIPS 2022
Generative Principal Component Analysis
ICLR 2022
Gaussian Process Bandit Optimization with Few Batches
AISTATS 2022
Stochastic Linear Bandits Robust to Adversarial Attacks
AISTATS 2021
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors
NIPS 2021
High-Dimensional Bayesian Optimization via Tree-Structured Additive Models
AAAI 2021
On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization
ICML 2021
Lenient Regret and Good-Action Identification in Gaussian Process Bandits
ICML 2021
Open Problem: Tight Online Confidence Intervals for RKHS Elements
COLT 2021
Learning Gaussian Graphical Models via Multiplicative Weights
AISTATS 2020
The Generalized Lasso with Nonlinear Observations and Generative Priors
NIPS 2020
A MaxSAT-Based Framework for Group Testing
AAAI 2020
Corruption-Tolerant Gaussian Process Bandit Optimization
AISTATS 2020
A Characteristic Function Approach to Deep Implicit Generative Modeling
CVPR 2020
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
ICML 2020
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries
NIPS 2019
Tight Regret Bounds for Bayesian Optimization in One Dimension
ICML 2018
Adversarially Robust Optimization with Gaussian Processes
NIPS 2018
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups
AISTATS 2018
Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization
COLT 2017
Lower Bounds on Active Learning for Graphical Model Selection
AISTATS 2017
Robust Submodular Maximization: A Non-Uniform Partitioning Approach
ICML 2017
Phase Transitions in the Pooled Data Problem
NIPS 2017
Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices
AISTATS 2016
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation
NIPS 2016
Time-Varying Gaussian Process Bandit Optimization
AISTATS 2016
Sparsistency of \ell_1-Regularized M-Estimators
AISTATS 2015