Introduces NSD-UCRL2 algorithm to tackle learning in non-stationary delayed environments; experiments show it outperforms existing methods.

Claire Vernade, Andras Gyorgy, Timothy Mann

Doubly-stochastic Gaussian kernel normalization automatically corrects heteroskedastic noise, ensuring robust high-dimensional affinity matrices.

Boris Landa, Ronald R. Coifman, Yuval Kluger

Embedding a Variational Auto-Encoder (VAE) into Bayesian Optimization (BO) enables efficient high-dimensional parameter estimation in personalized cardiac models, achieving over 10x speedup.

Jwala Dhamala, Sandesh Ghimire, John L. Sapp et al.

Proposes Fast-Slow GP-UCB for adversarially corrupted Bayesian optimization, achieving regret bounds that adapt to corruption level.

Ilija Bogunovic, Andreas Krause, Jonathan Scarlett

Introduces continuous volume sampling (VS) for kernel interpolation, achieving near-optimal error bounds based on spectral properties, applicable to any Mercer kernel.

Ayoub Belhadji, Rémi Bardenet, Pierre Chainais

This paper introduces a multi-model approach combining diverse approximate posteriors with multiple importance sampling to improve decision-making over standard ELBO-based methods.

Romain Lopez, Pierre Boyeau, Nir Yosef et al.

Introduces Temporal Fusion Transformer (TFT), combining multi-scale temporal modeling with interpretability, achieving 20% accuracy improvement in multi-horizon forecasting.

Bryan Lim, Sercan O. Arik, Nicolas Loeff et al.

AUGMIX combines stochastic augmentation and Jensen-Shannon loss to improve image classifier robustness and uncertainty, reducing corruption errors by over 50% on benchmarks.

Dan Hendrycks, Norman Mu, Ekin D. Cubuk et al.

Proposes a Mixture-of-Experts Variational Autoencoder (MMVAE) for multi-modal generative modeling, achieving four key criteria with state-of-the-art results.

Yuge Shi, N. Siddharth, Brooks Paige et al.

Neural network-based continuous and discrete survival prediction methods, including quantile discretization and piecewise hazard models, outperform traditional approaches.

Håvard Kvamme, Ørnulf Borgan

This paper analyzes MCMC challenges in Bayesian neural networks, showing that non-converged chains can still produce accurate predictive distributions.

Theodore Papamarkou, Jacob Hinkle, M. Todd Young et al.

Wasserstein-based distributionally robust optimization enhances model robustness against high-dimensional distributional uncertainty.

Daniel Kuhn, Peyman Mohajerin Esfahani, Viet Anh Nguyen et al.

Proposes a cost- and fidelity-layered adaptive sampling strategy for multi-fidelity Gaussian processes, significantly reducing predictive uncertainty.

Sayan Ghosh, Jesper Kristensen, Yiming Zhang et al.

Introduced a deep active learning method with adaptive acquisition, showing superior performance across datasets.

Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir

Introduces transductive linear bandit problem; proposes an algorithm matching the information-theoretic lower bound with instance-dependent sample complexity.

Tanner Fiez, Lalit Jain, Kevin Jamieson et al.

Introduces DPP-based kernel quadrature leveraging spectral properties, achieving error bounds tied to kernel eigenvalues, outperforming classical methods.

Ayoub Belhadji, Rémi Bardenet, Pierre Chainais

Using adversarial robustness as a prior, the paper achieves near-invertible, interpretable feature representations with improved visualization and manipulation, surpassing standard models.

Logan Engstrom, Andrew Ilyas, Shibani Santurkar et al.

Study the inductive bias of neural tangent kernels, analyzing smoothness and stability in convolutional networks.

Alberto Bietti, Julien Mairal

GraphNVP employs invertible normalizing flows for molecular graph generation, decomposing structure and attributes, achieving high validity and uniqueness with exact likelihood maximization.

Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago et al.

Proposes an efficient Weingarten map estimator with proven asymptotic convergence rate, applied to curvature estimation and point cloud simplification.

Yueqi Cao, Didong Li, Huafei Sun et al.