Scarlett, Jonathan

43 publications

AISTATS 2025 Lower Bounds for Time-Varying Kernelized Bandits Xu Cai, Jonathan Scarlett

ALT 2025 Quantile Multi-Armed Bandits with 1-Bit Feedback Ivan Lau, Jonathan Scarlett

AAAI 2024 Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization Xu Cai, Jonathan Scarlett

NeurIPS 2024 Memory-Efficient Gradient Unrolling for Large-Scale Bi-Level Optimization Qianli Shen, Yezhen Wang, Zhouhao Yang, Xiang Li, Haonan Wang, Yang Zhang, Jonathan Scarlett, Zhanxing Zhu, Kenji Kawaguchi

AISTATS 2024 No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints Arpan Losalka, Jonathan Scarlett

TMLR 2024 Regret Bounds for Noise-Free Cascaded Kernelized Bandits Zihan Li, Jonathan Scarlett

NeurIPSW 2024 Smoothing-Based Adversarial Defense Methods for Inverse Problems Yang Sun, Jonathan Scarlett

NeurIPS 2023 A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing Junren Chen, Jonathan Scarlett, Michael Ng, Zhaoqiang Liu

UAI 2023 Benefits of Monotonicity in Safe Exploration with Gaussian Processes Arpan Losalka, Jonathan Scarlett

ICML 2023 Communication-Constrained Bandits Under Additive Gaussian Noise Prathamesh Mayekar, Jonathan Scarlett, Vincent Y. F. Tan

ALT 2023 Max-Quantile Grouped Infinite-Arm Bandits Ivan Lau, Yan Hao Ling, Mayank Shrivastava, Jonathan Scarlett

TMLR 2023 On Average-Case Error Bounds for Kernel-Based Bayesian Quadrature Xu Cai, Thanh Lam, Jonathan Scarlett

AISTATS 2022 Gaussian Process Bandit Optimization with Few Batches Zihan Li, Jonathan Scarlett

NeurIPS 2022 A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits Ilija Bogunovic, Zihan Li, Andreas Krause, Jonathan Scarlett

ICML 2022 Adversarial Attacks on Gaussian Process Bandits Eric Han, Jonathan Scarlett

ICLR 2022 Generative Principal Component Analysis Zhaoqiang Liu, Jiulong Liu, Subhroshekhar Ghosh, Jun Han, Jonathan Scarlett

ICML 2022 Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning Sattar Vakili, Jonathan Scarlett, Da-Shan Shiu, Alberto Bernacchia

AAAI 2022 Max-Min Grouped Bandits Zhenlin Wang, Jonathan Scarlett

AISTATS 2021 Stochastic Linear Bandits Robust to Adversarial Attacks Ilija Bogunovic, Arpan Losalka, Andreas Krause, Jonathan Scarlett

AAAI 2021 High-Dimensional Bayesian Optimization via Tree-Structured Additive Models Eric Han, Ishank Arora, Jonathan Scarlett

ICML 2021 Lenient Regret and Good-Action Identification in Gaussian Process Bandits Xu Cai, Selwyn Gomes, Jonathan Scarlett

ICML 2021 On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization Xu Cai, Jonathan Scarlett

COLT 2021 Open Problem: Tight Online Confidence Intervals for RKHS Elements Sattar Vakili, Jonathan Scarlett, Tara Javidi

NeurIPS 2021 Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett

CVPR 2020 A Characteristic Function Approach to Deep Implicit Generative Modeling Abdul Fatir Ansari, Jonathan Scarlett, Harold Soh

AAAI 2020 A MaxSAT-Based Framework for Group Testing Lorenzo Ciampiconi, Bishwamittra Ghosh, Jonathan Scarlett, Kuldeep S. Meel

AISTATS 2020 Corruption-Tolerant Gaussian Process Bandit Optimization Ilija Bogunovic, Andreas Krause, Jonathan Scarlett

AISTATS 2020 Learning Gaussian Graphical Models via Multiplicative Weights Anamay Chaturvedi, Jonathan Scarlett

ICML 2020 Sample Complexity Bounds for 1-Bit Compressive Sensing and Binary Stable Embeddings with Generative Priors Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari, Jonathan Scarlett

NeurIPS 2020 The Generalized Lasso with Nonlinear Observations and Generative Priors Zhaoqiang Liu, Jonathan Scarlett

NeurIPS 2019 Learning Erdos-Renyi Random Graphs via Edge Detecting Queries Zihan Li, Matthias Fresacher, Jonathan Scarlett

NeurIPSW 2019 Sample Complexity Lower Bounds for Compressive Sensing with Generative Models Zhaoqiang Liu, Jonathan Scarlett

NeurIPS 2018 Adversarially Robust Optimization with Gaussian Processes Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, Volkan Cevher

AISTATS 2018 High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups Paul Rolland, Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher

ICML 2018 Tight Regret Bounds for Bayesian Optimization in One Dimension Jonathan Scarlett

AISTATS 2017 Lower Bounds on Active Learning for Graphical Model Selection Jonathan Scarlett, Volkan Cevher

COLT 2017 Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher

NeurIPS 2017 Phase Transitions in the Pooled Data Problem Jonathan Scarlett, Volkan Cevher

ICML 2017 Robust Submodular Maximization: A Non-Uniform Partitioning Approach Ilija Bogunovic, Slobodan Mitrović, Jonathan Scarlett, Volkan Cevher

AISTATS 2016 Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices Jonathan Scarlett, Volkan Cevher

AISTATS 2016 Time-Varying Gaussian Process Bandit Optimization Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher

NeurIPS 2016 Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, Volkan Cevher

AISTATS 2015 Sparsistency of 1-Regularized M-Estimators Yen-Huan Li, Jonathan Scarlett, Pradeep Ravikumar, Volkan Cevher