Hypothesis transfer learning (HTL) contrasts domain adaptation by allowing
for a previous task leverage, named the source, into a new one, the target,
without requiring access to the source data. Indeed, HTL relies only on a
hypothesis learnt from such source data, relieving the hurdle of expansive data
storage and providing great practical benefits. Hence, HTL is highly beneficial
for real-world applications relying on big data. The analysis of such a method
from a theoretical perspective faces multiple challenges, particularly in
classification tasks. This paper deals with this problem by studying the
learning theory of HTL through algorithmic stability, an attractive theoretical
framework for machine learning algorithms analysis. In particular, we are
interested in the statistical behaviour of the regularized empirical risk
minimizers in the case of binary classification. Our stability analysis
provides learning guarantees under mild assumptions. Consequently, we derive
several complexity-free generalization bounds for essential statistical
quantities like the training error, the excess risk and cross-validation
estimates. These refined bounds allow understanding the benefits of transfer
learning and comparing the behaviour of standard losses in different scenarios,
leading to valuable insights for practitioners.

通过算法稳定性研究 HTL 的学习理论，本文提供了一些条件下的学习保证和复杂度自由的泛化界限，揭示了迁移学习的优点并比较了不同场景下标准损失函数的行为，为从业者提供了有价值的见解。

使用代理分类损失的假设迁移学习

Hypothesis Transfer Learning with Surrogate Classification Losses

We propose a hypothesis disparity regularized mutual information
maximization~(HDMI) approach to tackle unsupervised hypothesis transfer -- as
an effort towards unifying hypothesis transfer learning (HTL) and unsupervised
domain adaptation (UDA) -- where the knowledge from a source domain is
transferred solely through hypotheses and adapted to the target domain in an
unsupervised manner. In contrast to the prevalent HTL and UDA approaches that
typically use a single hypothesis, HDMI employs multiple hypotheses to leverage
the underlying distributions of the source and target hypotheses. To better
utilize the crucial relationship among different hypotheses -- as opposed to
unconstrained optimization of each hypothesis independently -- while adapting
to the unlabeled target domain through mutual information maximization, HDMI
incorporates a hypothesis disparity regularization that coordinates the target
hypotheses jointly learn better target representations while preserving more
transferable source knowledge with better-calibrated prediction uncertainty.
HDMI achieves state-of-the-art adaptation performance on benchmark datasets for
UDA in the context of HTL, without the need to access the source data during
the adaptation.

提出了一种假设差异规则化的互信息最大化方法，用于处理无监督假设转移，通过使用多个假设来传递源领域的知识，而不需要在适应过程中访问源数据，从而在假设转移学习的上下文中实现了最先进的 UDA 适应性能。

假设差异规范化的互信息最大化

Hypothesis Disparity Regularized Mutual Information Maximization

Most of the existing approaches for person re-identification consider a
static setting where the number of cameras in the network is fixed. An
interesting direction, which has received little attention, is to explore the
dynamic nature of a camera network, where one tries to adapt the existing
re-identification models after on-boarding new cameras, with little additional
effort. There have been a few recent methods proposed in person
re-identification that attempt to address this problem by assuming the labeled
data in the existing network is still available while adding new cameras. This
is a strong assumption since there may exist some privacy issues for which one
may not have access to those data. Rather, based on the fact that it is easy to
store the learned re-identifications models, which mitigates any data privacy
concern, we develop an efficient model adaptation approach using hypothesis
transfer learning that aims to transfer the knowledge using only source models
and limited labeled data, but without using any source camera data from the
existing network. Our approach minimizes the effect of negative transfer by
finding an optimal weighted combination of multiple source models for
transferring the knowledge. Extensive experiments on four challenging benchmark
datasets with a variable number of cameras well demonstrate the efficacy of our
proposed approach over state-of-the-art methods.

探索摄像头网络的动态适应性，通过假说转移学习和模型组合来进行知识传递，并在四个具有不同数量摄像头的基准数据集上进行了广泛的实验，证明了该方法的有效性。

使用假设传递学习进行人员重识别相机接入

Camera On-boarding for Person Re-identification using Hypothesis  Transfer Learning

We consider the Hypothesis Transfer Learning (HTL) problem where one
incorporates a hypothesis trained on the source domain into the learning
procedure of the target domain. Existing theoretical analysis either only
studies specific algorithms or only presents upper bounds on the generalization
error but not on the excess risk. In this paper, we propose a unified
algorithm-dependent framework for HTL through a novel notion of transformation
function, which characterizes the relation between the source and the target
domains. We conduct a general risk analysis of this framework and in
particular, we show for the first time, if two domains are related, HTL enjoys
faster convergence rates of excess risks for Kernel Smoothing and Kernel Ridge
Regression than those of the classical non-transfer learning settings.
Experiments on real world data demonstrate the effectiveness of our framework.

本文提出了用于假设转移学习（HTL）的一种基于算法的新型框架，通过变换函数对源域和目标域之间的关系进行表征及风险分析，首次证明了，如果两个域之间存在关系，则 HTL 对于 Kernel Smoothing 和 Kernel Ridge Regression 的超额风险收敛速度比经典的非转移学习设定更快，实验结果表明了本框架的有效性。