Every entry carries a structured overview and the reproduction evidence collected so far. A paper is listed here when it carries the category as its primary field or as a secondary classification. Both are shown on the paper page.
Showing 1–9 of 9
This paper provides the first systematic theoretical analysis of Bayesian mixture-of-experts models with softmax gating, establishing posterior contraction rates for density estimation, convergence guarantees for parameter estimation, and strategies for selecting the number of experts.
This paper presents an efficient symbolic computation algorithm for determining whether causal effects can be identified from observational data with latent confounders in linear structural causal models. The algorithm finds the lowest-degree identifying formulas in quasi-polynomial time, making causal identifiability analysis practical for larger problems.
This paper shows that decentralized machine learning can achieve the same performance as centralized learning by having clients share Gibbs measures (probability distributions over models) instead of raw data. The key innovation is using each client's Gibbs measure as a reference measure for the next client, effectively encoding prior information while preserving privacy.
This paper analyzes active sequential prediction-powered mean estimation, where labels are selectively queried and ML predictions fill in the gaps. The authors find that contrary to intuition, using a nearly constant query probability (ignoring uncertainty) often produces tighter confidence intervals than adaptive uncertainty-based querying.
FUSE is a method that combines multiple imperfect AI verifiers to better judge model outputs without needing any labeled training data. It matches or beats semi-supervised methods across diverse benchmarks by controlling how verifiers depend on each other using spectral algorithms.
This paper develops a random matrix theory model that explains why neural networks exhibit a transient learning window where signal is detectable before overfitting occurs. The key mechanism is that anisotropy in input data creates fast and slow learning directions, causing a learnable eigenvalue to temporarily separate from noise before being reabsorbed.
This paper introduces a bandit algorithm for learning smooth functions over graphs, where neighboring nodes have similar payoffs. The approach achieves regret bounds that scale with an effective dimension rather than the total number of nodes, enabling efficient online learning for applications like content recommendation with thousands of items from just tens of evaluations.
This paper develops a simulation-based Bayesian inference method that corrects for selection bias by embedding the selection mechanism directly into the generative model, enabling accurate parameter estimation in complex models where traditional likelihood-based approaches fail. The approach allows both debiased estimation and explicit testing for bias presence without requiring tractable likelihoods.
This paper develops a general mathematical theory explaining how symmetries in target distributions and variational families guarantee that variational inference can accurately recover certain statistics, even when the approximation is imperfect. The framework unifies existing results and enables derivation of new recovery guarantees across diverse settings including directional statistics.