Jonathan Scarlett's Home Page

About Me

I am an assistant professor (NUS Presidential Young Professor) in the Department of Computer Science, Department of Mathematics, and Institute of Data Science at the National University of Singapore

My research interests are in the areas of information theory, machine learning, and high-dimensional statistics. Please see my research and publications pages for details.

Office:: COM2 #03-46 (Comp. Sci.), S17 #07-01 (Maths)
Phone:: +65 6516 1179 (Comp. Sci.), +65 6516 2952 (Maths)
Email:: "scarlett" followed by "@comp.nus.edu.sg"

News:

(May 2022)Papers Adversarial Attacks on Gaussian Process Bandits and Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning accepted to ICML 2022
(March 2022) Paper Mismatched Rate-Distortion Theory: Ensembles, Bounds, and General Alphabets uploaded to arxiv
(Feb. 2022) Papers A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits and Order-Optimal Error Bounds for Noisy Kernel-Based Bayesian Quadrature uploaded to arxiv
(Jan. 2022) Paper Generative Principal Component Analysis accepted to ICLR 2022
(Jan. 2022) Paper Gaussian Process Bandit Optimization with Few Batches accepted to AISTATS 2022
(Jan. 2022) Paper Tight Regret Bounds for Noisy Optimization of a Brownian Motion accepted to IEEE Transactions on Signal Processing
(Jan. 2022) Paper Performance Bounds for Group Testing With Doubly-Regular Designs uploaded to arxiv
(Dec. 2021) Paper Near-Optimal Sparsity-Constrained Group Testing: Improved Bounds and Algorithms accepted to IEEE Transactions on Information Theory
(Dec. 2021) Paper Simple Coding Techniques for Many-Hop Relaying uploaded to arxiv
(Dec. 2021) Paper Max-Min Grouped Bandits accepted to AAAI 2022
(Nov. 2021) Paper Noisy Adaptive Group Testing via Noisy Binary Search accepted to IEEE Transactions on Information Theory
(Oct. 2021) I have been awarded the NUS Presidential Young Professorship and listed on the MIT TR35 Innovators (Asia Pacific) list
(Sept. 2021) Paper Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors accepted to NeurIPS 2021
(Aug. 2021) Paper Optimal Rates of Teaching and Learning Under Uncertainty accepted to IEEE Transactions on Information Theory
(Aug. 2021) Paper Optimal Non-Adaptive Probabilistic Group Testing in General Sparsity Regimes accepted to Information and Inference
(June 2021) Paper Fast Splitting Algorithms for Sparsity-Constrained and Noisy Group Testing uploaded to arxiv
(May 2021) Papers On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization and Lenient Regret and Good-Action Identification in Gaussian Process Bandits accepted to ICML 2021
(March 2021) Paper Sublinear-Time Non-Adaptive Group Testing with O(k log n) Tests via Bit-Mixing Coding published in IEEE Transactions on Information Theory
(Jan. 2021) Paper Stochastic Linear Bandits Robust to Adversarial Attacks accepted to AISTATS 2021
(Dec. 2020) Paper High-Dimensional Bayesian Optimization via Tree-Structured Graphical Models accepted to AAAI 2020
(Nov. 2020) Paper On the All-Or-Nothing Behavior of Bernoulli Group Testing accepted to IEEE Journal on Selected Areas in Information Theory (JSAIT)
(Sept. 2020) Paper The Generalized Lasso with Nonlinear Observations and Generative Priors accepted to NeurIPS 2020
(Sept. 2020) Paper Support Recovery in the Phase Retrieval Model: Information-Theoretic Fundamental Limits accepted to IEEE Transactions on Information Theory
(Aug. 2020) Survey monograph Information-Theoretic Foundations of Mismatched Decoding published in Foundations and Trends in Communications and Information Theory
(June 2020) Paper A Fast Binary Splitting Approach to Non-Adaptive Group Testing accepted to RANDOM 2020
(June 2020) Paper Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors accepted to ICML 2020
(May 2020) Paper Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models published in IEEE Journal on Selected Areas in Information Theory (JSAIT)
(Feb. 2020) Paper A Characteristic Function Approach to Deep Implicit Generative Modeling accepted to CVPR 2020
(Jan. 2020) Paper Noisy Non-Adaptive Group Testing: A (Near-)Definite Defectives Approach accepted to IEEE Transactions on Information Theory
(Jan. 2020) Papers Corruption-Tolerant Gaussian Process Bandit Optimization and Learning Gaussian Graphical Models via Multiplicative Weights accepted to AISTATS 2020
(Dec. 2019) Survey monograph Group Testing: An Information Theory Perspective published in Foundations and Trends in Communications and Information Theory
(Nov. 2019) Paper A MaxSAT-Based Framework for Group Testing accepted to AAAI 2020
(Sept. 2019) Paper Learning Erdős-Rényi Random Graphs via Edge Detecting Queries accepted to NeurIPS 2019
(July 2019) I have uploaded some tutorial slides on information-theoretic bounds for problems in statistics and machine learning
(July 2019) Three papers presented at ISIT 2019: (i) Overlapping Multi-Bandit Best-Arm Identification; (ii) An Efficient Algorithm for Capacity-Approaching Noisy Adaptive Group Testing; (iii) A Recursive Cost-Constrained Construction that Attains the Expurgated Exponent
(June 2019) Papers Noisy Adaptive Group Testing: Bounds and Algorithms and Generalized Random Gilbert-Varshamov Codes published in IEEE Transactions on Information Theory
(March 2019) Paper Cross-Sender Bit Mixing Coding accepted to Conference on Information Processing in Sensor Networks (IPSN)
(Feb. 2019) Paper Performance of Group Testing Algorithms With Near-Constant Tests-per-Item published in IEEE Transactions on Information Theory
(Dec. 2018) Paper Adversarially Robust Optimization with Gaussian Processes presented as a spotlight talk at NeurIPS 2018
(Nov. 2018) I have been awarded the National Research Foundation (NRF) Fellowship for the project Robust Statistical Model Under Model Uncertainty
(Oct. 2018) I have been awarded the NUS Early Career Research Award for the project Information-Theoretic Methods in Data Science
(Aug. 2018) Tutorial article An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation uploaded, to appear in a book titled Information-Theoretic Methods in Data Science
(July 2018) Paper Tight Regret Bounds for Bayesian Optimization in One Dimension presented at ICML 2018