Miranda C. N. Cheng — accepted AI-conference papers

2026

Analytic Bijections for Smooth and Interpretable Normalizing Flows

ICML 2026poster

A key challenge in normalizing flows is finding expressive invertible scalar bijections. Existing approaches face trade-offs: affine transformations are smooth and analytically invertible but lack expressivity; monotonic splines offer local control but are only piecewise smooth and act on bounded do…

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2026

Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation

Zier Mensch, Lars Holdijk, Samuel Duffield, Maxwell Aifer, Patrick Coles, Max Welling +1

ICML 2026poster

Stochastic-gradient MCMC methods enable scalable Bayesian posterior sampling but often suffer from sensitivity to minibatch size and gradient noise. To address this, we propose Stochastic Gradient Lattice Random Walk (SGLRW), an extension of the Lattice Random Walk discretization. Unlike conventiona…

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