In multi-label classification, each training instance is associated with
multiple class labels simultaneously. Unfortunately, collecting the fully
precise class labels for each training instance is time- and labor-consuming
for real-world applications. To alleviate this problem, a novel labeling
setting termed \textit{Determined Multi-Label Learning} (DMLL) is proposed,
aiming to effectively alleviate the labeling cost inherent in multi-label
tasks. In this novel labeling setting, each training instance is associated
with a \textit{determined label} (either "Yes" or "No"), which indicates
whether the training instance contains the provided class label. The provided
class label is randomly and uniformly selected from the whole candidate labels
set. Besides, each training instance only need to be determined once, which
significantly reduce the annotation cost of the labeling task for multi-label
datasets. In this paper, we theoretically derive an risk-consistent estimator
to learn a multi-label classifier from these determined-labeled training data.
Additionally, we introduce a similarity-based prompt learning method for the
first time, which minimizes the risk-consistent loss of large-scale pre-trained
models to learn a supplemental prompt with richer semantic information.
Extensive experimental validation underscores the efficacy of our approach,
demonstrating superior performance compared to existing state-of-the-art
methods.

多标签分类中引入一种新的标注方法，通过确定性标签减少标注成本，并使用基于相似度的提示学习方法来提高模型性能。

基于相似度的确定性多标签学习

Determined Multi-Label Learning via Similarity-Based Prompt

Active learning is a promising paradigm to reduce the labeling cost by
strategically requesting labels to improve model performance. However, existing
active learning methods often rely on expensive acquisition function to
compute, extensive modeling retraining and multiple rounds of interaction with
annotators. To address these limitations, we propose a novel approach for
active learning, which aims to select batches of unlabeled instances through a
learned surrogate model for data acquisition. A key challenge in this approach
is developing an acquisition function that generalizes well, as the history of
data, which forms part of the utility function's input, grows over time. Our
novel algorithmic contribution is a bilevel multi-task bilevel optimization
framework that predicts the relative utility -- measured by the validation
accuracy -- of different training sets, and ensures the learned acquisition
function generalizes effectively. For cases where validation accuracy is
expensive to evaluate, we introduce efficient interpolation-based surrogate
models to estimate the utility function, reducing the evaluation cost. We
demonstrate the performance of our approach through extensive experiments on
standard active classification benchmarks. By employing our learned utility
function, we show significant improvements over traditional techniques, paving
the way for more efficient and effective utility maximization in active
learning applications.

活动学习是通过有选择性地请求标签以提高模型性能从而减少标注成本的一种有前途的范式。本研究提出了一种新的活动学习方法，通过一个学习的代理模型选择批次的未标记实例来进行数据获取，从而克服了现有活动学习方法所依赖于昂贵的获取函数计算、广泛的建模重训练和与注释者的多轮交互等局限性。

通过多任务双层优化学习主动学习的排序

Learning to Rank for Active Learning via Multi-Task Bilevel Optimization

We study actively labeling streaming data, where an active learner is faced
with a stream of data points and must carefully choose which of these points to
label via an expensive experiment. Such problems frequently arise in
applications such as healthcare and astronomy. We first study a setting when
the data's inputs belong to one of $K$ discrete distributions and formalize
this problem via a loss that captures the labeling cost and the prediction
error. When the labeling cost is $B$, our algorithm, which chooses to label a
point if the uncertainty is larger than a time and cost dependent threshold,
achieves a worst-case upper bound of $O(B^{\frac{1}{3}} K^{\frac{1}{3}}
T^{\frac{2}{3}})$ on the loss after $T$ rounds. We also provide a more nuanced
upper bound which demonstrates that the algorithm can adapt to the arrival
pattern, and achieves better performance when the arrival pattern is more
favorable. We complement both upper bounds with matching lower bounds. We next
study this problem when the inputs belong to a continuous domain and the output
of the experiment is a smooth function with bounded RKHS norm. After $T$ rounds
in $d$ dimensions, we show that the loss is bounded by $O(B^{\frac{1}{d+3}}
T^{\frac{d+2}{d+3}})$ in an RKHS with a squared exponential kernel and by
$O(B^{\frac{1}{2d+3}} T^{\frac{2d+2}{2d+3}})$ in an RKHS with a Mat\'ern
kernel. Our empirical evaluation demonstrates that our method outperforms other
baselines in several synthetic experiments and two real experiments in medicine
and astronomy.

研究主动标注流数据的问题，提出了一种基于确定性阈值和时间成本依赖性阈值的算法，并考虑了数据输入属于离散或连续域的情况，得到了上限约束模型。通过模拟实验，并在医学和天文学领域进行实际测试，证明了该算法在实验结果上的优势。

流数据的主动成本感知标记

Active Cost-aware Labeling of Streaming Data

We study how to effectively leverage expert feedback to learn sequential
decision-making policies. We focus on problems with sparse rewards and long
time horizons, which typically pose significant challenges in reinforcement
learning. We propose an algorithmic framework, called hierarchical guidance,
that leverages the hierarchical structure of the underlying problem to
integrate different modes of expert interaction. Our framework can incorporate
different combinations of imitation learning (IL) and reinforcement learning
(RL) at different levels, leading to dramatic reductions in both expert effort
and cost of exploration. Using long-horizon benchmarks, including Montezuma's
Revenge, we demonstrate that our approach can learn significantly faster than
hierarchical RL, and be significantly more label-efficient than standard IL. We
also theoretically analyze labeling cost for certain instantiations of our
framework.

论文提出了一种算法框架，称为层次性指导，旨在通过利用专家的反馈来学习序贯决策制定策略，以应对奖励稀疏和时间范围长等问题，该框架可以在不同的层次上组合不同的 imitation learning 和 reinforcement learning，从而大大减少专家工作量和探索成本，并对该框架的某些实例的标注成本进行了理论分析。