Weak supervision (WS) frameworks are a popular way to bypass hand-labeling
large datasets for training data-hungry models. These approaches synthesize
multiple noisy but cheaply-acquired estimates of labels into a set of
high-quality pseudolabels for downstream training. However, the synthesis
technique is specific to a particular kind of label, such as binary labels or
sequences, and each new label type requires manually designing a new synthesis
algorithm. Instead, we propose a universal technique that enables weak
supervision over any label type while still offering desirable properties,
including practical flexibility, computational efficiency, and theoretical
guarantees. We apply this technique to important problems previously not
tackled by WS frameworks including learning to rank, regression, and learning
in hyperbolic space. Theoretically, our synthesis approach produces a
consistent estimators for learning some challenging but important
generalizations of the exponential family model. Experimentally, we validate
our framework and show improvement over baselines in diverse settings including
real-world learning-to-rank and regression problems along with learning on
hyperbolic manifolds.

该研究提出了一种通用的技术，以实现对任何标签类型的弱监督，同时仍提供实用灵活性、计算效率和理论保证，并应用于以往没有使用弱监督框架解决的重要问题，包括排序学习、回归和超似曲空间中的学习。

通用弱监督

Universalizing Weak Supervision

We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptive
MCMC algorithm based on Hamiltonian Monte Carlo (HMC). On target densities
where classical HMC is not an option due to intractable gradients, KMC
adaptively learns the target's gradient structure by fitting an exponential
family model in a Reproducing Kernel Hilbert Space. Computational costs are
reduced by two novel efficient approximations to this gradient. While being
asymptotically exact, KMC mimics HMC in terms of sampling efficiency, and
offers substantial mixing improvements over state-of-the-art gradient free
samplers. We support our claims with experimental studies on both toy and
real-world applications, including Approximate Bayesian Computation and
exact-approximate MCMC.

本研究提出了核哈密顿蒙特卡罗算法（KMC）, 一种基于哈密顿蒙特卡罗的无梯度自适应 MCMC 算法。在目标密度函数不可求梯度时，KMC 通过拟合重现核希尔伯特空间中的指数族模型，自适应地学习目标的梯度结构。其具有收敛到精确解的能力，且在采样效率上与 HMC 相当并且具有极大的混合改进，通过玩具和现实应用的实验研究来支持我们的结论，包括近似贝叶斯计算和精确 - 近似 MCMC。