Arthur Gretton
Professor of Machine Learning
Develops kernel methods for machine learning, including two-sample tests, dependence measures and causal inference.
Arthur Gretton's lab at UCL works on learning theory & optimization, probabilistic & causal inference and neuroscience & brain-computer interfaces. 36 papers since 2023, 34 at top venues, mostly NeurIPS and AISTATS. 4 PhD students. 22 alumni.
At a glance
- papers, 2023–2026
- 9/ year
- at T1 venues
- 94%
- PhD students
- 4
- MS students
- 0
- alumni listed
- 22
Papers by yearfrom DBLP, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 19 papers2025 · T1 17
- 9 papers2024 · T1 9
- 7 papers2023 · T1 7
T1 venuesOtherVenues: NeurIPS, AISTATS, ICML, ICLR, JMLR
Research focuspaper titles that use each topic's words
- Learning theory & optimization11 papers
- Probabilistic & causal inference4 papers
- Neuroscience & brain-computer interfaces0 papers
- measures0 papers
- gatsby0 papers
Paper list
From DBLP.
20261 paper
202519 papers
- JMLR (T1)(De)-regularized Maximum Mean Discrepancy Gradient Flow (opens in a new tab)
- UAI (T1)A Unified Data Representation Learning for Non-parametric Two-sample Testing (opens in a new tab)
- ICML (T1)Accelerated Diffusion Models via Speculative Sampling (opens in a new tab)
- JMLR (T1)Composite Goodness-of-fit Tests with Kernels (opens in a new tab)
- AISTATS (T1)Credal Two-Sample Tests of Epistemic Uncertainty (opens in a new tab)
- ICLR (T1)Deep MMD Gradient Flow without adversarial training (opens in a new tab)
- NeurIPS (T1)Demystifying Spectral Feature Learning for Instrumental Variable Regression (opens in a new tab)
- AISTATS (T1)Density Ratio-based Proxy Causal Learning Without Density Ratios (opens in a new tab)
- NeurIPS (T1)Density Ratio-Free Doubly Robust Proxy Causal Learning (opens in a new tab)
- ICML (T1)Distributional Diffusion Models with Scoring Rules (opens in a new tab)
- NeurIPS (T1)Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings (opens in a new tab)
- AISTATS (T1)Kernel Single Proxy Control for Deterministic Confounding (opens in a new tab)
20249 papers
- ICML (T1)A Distributional Analogue to the Successor Representation (opens in a new tab)
- UAI (T1)Conditional Bayesian Quadrature (opens in a new tab)
- ICML (T1)Distributional Bellman Operators over Mean Embeddings (opens in a new tab)
- NeurIPS (T1)Foundations of Multivariate Distributional Reinforcement Learning (opens in a new tab)
- NeurIPS (T1)Mind the Graph When Balancing Data for Fairness or Robustness (opens in a new tab)
- NeurIPS (T1)Near-Optimality of Contrastive Divergence Algorithms (opens in a new tab)
- NeurIPS (T1)Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms (opens in a new tab)
- AISTATS (T1)Proxy Methods for Domain Adaptation (opens in a new tab)
- JMLR (T1)Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm (opens in a new tab)
20237 papers
- ICML (T1)A Kernel Stein Test of Goodness of Fit for Sequential Models (opens in a new tab)
- ICLR (T1)A Neural Mean Embedding Approach for Back-door and Front-door Adjustment (opens in a new tab)
- AISTATS (T1)Adapting to Latent Subgroup Shifts via Concepts and Proxies (opens in a new tab)
- ICLR (T1)Efficient Conditionally Invariant Representation Learning (opens in a new tab)
- UAI (T1)Fast and scalable score-based kernel calibration tests (opens in a new tab)
- JMLR (T1)MMD Aggregated Two-Sample Test (opens in a new tab)
- NeurIPS (T1)MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting (opens in a new tab)
Where alumni went22 listed
Apply to UCLFall 2027
Not announced yet
Computer Science MPhil/PhD
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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