Kernel Selection is Model Selection: A Unified Complexity-Penalised Approach for MMD Two-Sample Tests

Yijin Ni, Xiaoming Huo

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

Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling

Jack Sandberg, Morteza Haghir Chehreghani

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Approximate Matrix–Vectors Under a Bounded $\ell_1$ Assumption and Applications to Kernel Matrices

Rikhav Shah, Sandeep Silwal, Tony C Wang

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

Local Gaussian Processes on Compact Lie Groups

Bochuan Liu, Mingyang Zhao, Xiaohong Jia

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

Locality Sensitive Hashing for p-Exponential Kernels with Applications to Density Estimation

Barak Gorodissky, Tal Wagner

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Outlier-Robust Multi-Output Gaussian Processes

Joshua Rooijakkers, Leiv Rønneberg, Francois-Xavier Briol, Jeremias Knoblauch and 1 more

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

How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs

Mark Kozdoba, Shie Mannor

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

A general kernel framework for non-CND distance measures using $|\mathcal{D}|$-dimensional sparse landmark embeddings

Marcus Noack, Maher B Alghalayini, Mark D Risser

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

Manifold Random Features

Manifold Random Features approximate bivariate manifold functions via graph discretization and continuous fields, yielding positive bounded features with low variance.

Ananya Parashar, Derek Long, Dwaipayan Saha, Krzysztof M Choromanski

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

Gaussian Mixture Models in Hilbert Spaces via Kernel Methods

A kernel-based Gaussian mixture model for Hilbert-space data uses mean embeddings to cluster infinite-dimensional objects with theoretical guarantees and scalable estimation.

Daniel López Montero, Antonio Álvarez-López, Marcos Matabuena

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

Sparse Attention as Compact Kernel Regression

Sparse attention corresponds to compact kernel regression, with normalized ReLU and sparsemax arising from Epanechnikov kernels and α-entmax mapping to biweight and triweight kernels. This unifies sparsity with kernel design and yields competitive kernel-based transformers on language modeling and i

Saul Santos, Nuno Gonçalves, Daniel McNamee, Marcos Treviso and 1 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

Improved Regret Analysis For Parallel Gaussian Process Bandit Optimization

Parallel GP-BTS achieves batch-size-independent regret without initial uncertainty sampling, with stronger noiseless bounds than noisy.

Shion Takeno, Shogo Iwazaki

Sydney Poster Session 4, Wed, Dec 9, 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

Conditioning Gaussian Processes on Almost Anything

Gaussian processes are recast as linear diffusion models to enable conditioning on arbitrary likelihoods, including language and physics, via ODE sampling without bespoke derivations.

Henry Moss, Lachlan Astfalck, Tom Cowperthwaite, Colin Doumont and 4 more

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

A Kernel Nonconformity Score for Multivariate Conformal Prediction

Multivariate Kernel Score yields geometry-adapted conformal regions via anisotropic MMD, guaranteeing finite-sample coverage with dimension-free rates and smaller volumes than ellipsoidal baselines.

Louis Meyer, Wenkai Xu

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

State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives

New wavelet-based DPPs offer superior accuracy and a conversion method yields low-rank discrete kernels that preserve variance decay for rough objectives.

Hoang Son Tran, Pranav Gupta, Rémi Bardenet, Subhroshekhar Ghosh

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026