Jonathan Scarlett
Associate Professor
Works on information theory, machine learning and high-dimensional statistics, including Bayesian optimisation and group testing.
Jonathan Scarlett's lab at NUS works on learning theory & optimization and probabilistic & causal inference. 32 papers since 2023, 10 at top venues, mostly AISTATS and ICML. 3 PhD students. 7 alumni. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 31%
- PhD students
- 3
- MS students
- 0
- alumni listed
- 7
Papers by yearfrom the lab homepage, 2023–2026
- 10 papers2026 · T1 2 · collecting
- 6 papers2025 · T1 1
- 8 papers2024 · T1 3
- 8 papers2023 · T1 4
T1 venuesOtherVenues: AISTATS, ICML, TMLR, AAAI, COLT
Research focuspaper titles that use each topic's words
- testing9 papers
- Learning theory & optimization4 papers
- Probabilistic & causal inference3 papers
- bayesian optimisation0 papers
- high-dimensional statistics0 papers
Paper list
From the lab homepage.
202610 papers
- COLT (T1)A Distribution Testing Approach to Clustering Distributions
- IEEE Transactions on Information TheoryA Fast Binary Splitting Approach for Non-Adaptive Learning of Erdős-Rényi Graphs
- Accepted to NeurIPSBatched Stochastic Linear Bandits with 1-Bit Communication Constraints
- International Conference on Randomization and ComputationEntropy Equivalence Testing
- ICML (T1)Envy-Free Allocation of Indivisible Goods via Noisy Queries
- Foundations and Trends in Communications and Information TheoryGroup Testing: An Information Theory Perspective
- Accepted to NeurIPSNear-Optimal Regret in Adversarial Kernel Bandits
- IEEE Transactions on Information TheoryOptimal Non-Adaptive Group Testing with One-Sided Error Guarantees
- Information and InferenceRobust Instance Optimal Phase-Only Compressed Sensing
- IEEE Transactions on Information TheoryStatistical Mean Estimation with Coded Relayed Observations
20256 papers
- 2116-2139, MarchA General Framework for Clustering and Distribution Matching with Bandit Feedback
- IEEE Journal on Selected Areas in Information TheoryError Exponents for DNA Storage Codes with a Variable Number of Reads
- IEEE Transactions on Information TheoryExact Error Exponents of Concatenated Codes for DNA Storage
- ACM-SIAM Symposium on Discrete AlgorithmsExact Thresholds for Noisy Non-Adaptive Group Testing
- AISTATS (T1)Lower Bounds for Time-Varying Kernelized Bandits
- International Conference on Algorithmic Learning TheoryQuantile Multi-Armed Bandits with 1-bit Feedback
20248 papers
- IEEE Transactions on Information Theory1525-1539, March 2024
- SIAM Journal on Mathematics of Data ScienceApproximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing
- IEEE Transactions on Information TheoryConcomitant Group Testing
- AAAI (T1)Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization
- IEEE Transactions on Information TheoryMaxflow-Based Bounds for Low-Rate Information Propagation over Noisy Networks
- AISTATS (T1)No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints
- IEEE Transactions on Information TheoryOptimal 1-bit Error Exponent for 2-hop Relaying with Binary-Input Channels
- TMLR (T1)Regret Bounds for Noise-Free Cascaded Kernelized Bandits
20238 papers
- NeurIPS (T1)A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing
- UAI (T1)Benefits of Monotonicity in Safe Exploration with Gaussian Processes
- ICML (T1)Communication-Constrained Bandits under Additive Gaussian Noise
- Information and InferenceFast Splitting Algorithms for Sparsity-Constrained and Noisy Group Testing
- International Conference on Algorithmic Learning TheoryMax-Quantile Grouped Infinite-Arm Bandits
- IEEE Transactions on Information TheoryMulti-Bit Relaying over a Tandem of Channels
- TMLR (T1)On Average-Case Error Bounds for Kernel-Based Bayesian Quadrature
- IEEE Transactions on Information TheoryPerformance Bounds for Group Testing With Doubly-Regular Designs
Where alumni went7 listed
Apply to NUSFall 2027
School of Computing PhD in Computer Science
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