The advent of instruction-tuned language models that convincingly mimic human
writing poses a significant risk of abuse. For example, such models could be
used for plagiarism, disinformation, spam, or phishing. However, such abuse may
be counteracted with the ability to detect whether a piece of text was composed
by a language model rather than a human. Some previous approaches to this
problem have relied on supervised methods trained on corpora of confirmed human
and machine-written documents. Unfortunately, model under-specification poses
an unavoidable challenge for neural network-based detectors, making them
brittle in the face of data shifts, such as the release of further language
models producing still more fluent text than the models used to train the
detectors. Other previous approaches require access to the models that may have
generated a document in question at inference or detection time, which is often
impractical. In light of these challenges, we pursue a fundamentally different
approach not relying on samples from language models of concern at training
time. Instead, we propose to leverage representations of writing style
estimated from human-authored text. Indeed, we find that features effective at
distinguishing among human authors are also effective at distinguishing human
from machine authors, including state of the art large language models like
Llama 2, ChatGPT, and GPT-4. Furthermore, given a handful of examples composed
by each of several specific language models of interest, our approach affords
the ability to predict which model generated a given document.

利用人类文本估计的写作风格来区分人类作者和机器作者，以及预测给定文档由哪个语言模型生成。

基于风格表示的机器生成文本的少样本检测

Few-Shot Detection of Machine-Generated Text using Style Representations

Robust learning is an important issue in Scientific Machine Learning (SciML).
There are several works in the literature addressing this topic. However, there
is an increasing demand for methods that can simultaneously consider all the
different uncertainty components involved in SciML model identification. Hence,
this work proposes a comprehensive methodology for uncertainty evaluation of
the SciML that also considers several possible sources of uncertainties
involved in the identification process. The uncertainties considered in the
proposed method are the absence of theory and causal models, the sensitiveness
to data corruption or imperfection, and the computational effort. Therefore, it
was possible to provide an overall strategy for the uncertainty-aware models in
the SciML field. The methodology is validated through a case study, developing
a Soft Sensor for a polymerization reactor. The results demonstrated that the
identified Soft Sensor are robust for uncertainties, corroborating with the
consistency of the proposed approach.

提出了一种综合的科学机器学习模型参数不确定性评估方法，考虑了多种可能的不确定性来源，验证了该方法的鲁棒性和一致性，并在聚合物反应器的软传感器开发中得到了实际应用。

不确定性感知科学机器学习模型的稳健学习方法

A Robust Learning Methodology for Uncertainty-aware Scientific Machine Learning models

Reinforcement learning algorithms can acquire policies for complex tasks
autonomously. However, the number of samples required to learn a diverse set of
skills can be prohibitively large. While meta-reinforcement learning methods
have enabled agents to leverage prior experience to adapt quickly to new tasks,
their performance depends crucially on how close the new task is to the
previously experienced tasks. Current approaches are either not able to
extrapolate well, or can do so at the expense of requiring extremely large
amounts of data for on-policy meta-training. In this work, we present model
identification and experience relabeling (MIER), a meta-reinforcement learning
algorithm that is both efficient and extrapolates well when faced with
out-of-distribution tasks at test time. Our method is based on a simple
insight: we recognize that dynamics models can be adapted efficiently and
consistently with off-policy data, more easily than policies and value
functions. These dynamics models can then be used to continue training policies
and value functions for out-of-distribution tasks without using
meta-reinforcement learning at all, by generating synthetic experience for the
new task.

在本文中，我们提出了基于模型识别和经验重新标注（MIER）的元强化学习算法，它是一种有效的算法，可以在测试时面对分布外的任务进行良好拟合，而无需使用元强化学习

通过模型识别和经验再标记实现对分布偏移鲁棒的元强化学习

Meta-Reinforcement Learning Robust to Distributional Shift via Model  Identification and Experience Relabeling

Recent improvement on Tarski's procedure for quantifier elimination in the
first order theory of real numbers makes it feasible to solve small instances
of the following problems completely automatically: 1. listing all equality and
inequality constraints implied by a graphical model with hidden variables. 2.
Comparing graphyical models with hidden variables (i.e., model equivalence,
inclusion, and overlap). 3. Answering questions about the identification of a
model or portion of a model, and about bounds on quantities derived from a
model. 4. Determing whether a given set of independence assertions. We discuss
the foundation of quantifier elimination and demonstrate its application to
these problems.

本研究通过 Tarski 算法解决量词消除的问题，结合模型检验来寻找模型的相等性、包容性和重叠度等性质，并回答模型识别和独立性问题。