Semiparametric Efficient Tests for Interpretable Distributional Treatment Effects

Houssam Zenati, Arthur Gretton

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Learning Gaussian Conditional Distributions using Neural Ratio Estimation is Hard

Pierre Glaser, Arthur Gretton

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Sobolev Regularized MMD Gradient Flow

Sobolev-regularized MMD gradient flow penalizes witness function gradients to ensure global convergence without isoperimetric assumptions, applying to both sampling and generative modeling.

Chenyang Tian, Bharath Sriperumbudur, Arthur Gretton, Zonghao Chen

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity

A Hilbert-valued one-step estimator enables semiparametrically efficient inference and bootstrap-calibrated tests for kernel noise heterogeneity in additive noise models.

Jakub Wornbard, Zikai Shen, Dimitri Meunier, Arthur Gretton

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Closed-Form Last Layer Optimization

Closed-form last-layer optimization treats final weights as backbone-dependent functions, yielding convergence guarantees and outperforming SGD and Adam on regression tasks.

Alexandre Galashov, Nathaël Da Costa, Liyuan Xu, Philipp Hennig and 1 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026