Derived asymptotic differential equations describe how shallow nonlinear autoencoders sequentially learn principal components in high-dimensional data.

Maria Refinetti, Sebastian Goldt

Introduced a score-based generative model using Critically-Damped Langevin Diffusion, improving image synthesis quality.

Tim Dockhorn, Arash Vahdat, Karsten Kreis

Orthogonal polynomial-based DPP reduces gradient variance in SGD, achieving faster convergence.

Remi Bardenet, Subhro Ghosh, Meixia Lin

Neural Schrödinger-Föllmer flows enable finite-time, low-variance Bayesian inference in high dimensions via stochastic control and neural network parametrization.

Francisco Vargas, Andrius Ovsianas, David Fernandes et al.

Proposes tighter online confidence intervals for RKHS elements, reducing width growth from O(√γn) to near logarithmic, enhancing regret bounds.

Sattar Vakili, Jonathan Scarlett, Tara Javidi

Proposes a Gaussian Process-based computational graph completion framework, enabling robust inference of unknown functions and variables in complex systems.

Houman Owhadi

This paper analyzes adversarial attacks on Gaussian process bandits, proposing white-box and black-box methods that successfully steer algorithms toward target regions with low budgets.

Eric Han, Jonathan Scarlett

Proposes generalized kernel thinning (TARGET KT) with dimension-free error bounds, improving high-dimensional distribution compression.

Raaz Dwivedi, Lester Mackey

Moser Flow (MF), a divergence-based continuous normalizing flow, avoids ODE solving, enabling efficient sampling and density estimation on complex manifolds.

Noam Rozen, Aditya Grover, Maximilian Nickel et al.

Adaptive embedding in kernel and neural bandits achieves sample complexity depending only on effective dimension.

Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang et al.

The paper introduces the Optimum-statistical Collaboration framework and VHCT algorithm for efficient black-box optimization.

Wenjie Li, Chi-Hua Wang, Guang Cheng et al.

Proposes IXOMD, a model-free algorithm for high-probability convergence to NE in two-player zero-sum partially observable Markov games, with rate O(1/√T).

Tadashi Kozuno, Pierre Ménard, Rémi Munos et al.

Using measure representation and sparse regularization, proves gradient descent can recover teacher network parameters with high probability in two-layer ReLU models.

Shunta Akiyama, Taiji Suzuki

Kernel Thinning compresses n points to √n with provably comparable MMD error, outperforming i.i.d. sampling.

Raaz Dwivedi, Lester Mackey

Proposes dataset inference leveraging model memorization to verify ownership with over 99% confidence, using statistical tests and distance estimation techniques.

Pratyush Maini, Mohammad Yaghini, Nicolas Papernot

Proposes SDE-BNN, an infinite-depth Bayesian neural network using stochastic differential equations, with a zero-variance gradient estimator for scalable, expressive inference.

Winnie Xu, Ricky T. Q. Chen, Xuechen Li et al.

Proposes TAX4CS framework integrating SHAP, LIME, and PDP to enhance transparency and auditability of credit scoring models.

Michael Bücker, Gero Szepannek, Alicja Gosiewska et al.

Proposes Linear Optimal Transport (LOT) embedding for high-dimensional distribution classification, nearly matching Wasserstein-2 distances, especially effective under shift and scale perturbations.

Caroline Moosmüller, Alexander Cloninger

Proposed algorithm-independent lower bounds for Gaussian process bandit optimization, improving error probability dependence in standard and robust settings.

Xu Cai, Jonathan Scarlett

Proposed low-rank tensor bandit algorithms with finite regret bounds, outperforming existing methods in high-dimensional online decision tasks.

Jie Zhou, Botao Hao, Zheng Wen et al.