Proceedings of the International Conference on Machine Learning 2021

This paper introduces an advanced framework leveraging Large Language Model-based Multi-Agent Systems (LLMMA) for the automated search and optimization of Quantum Machine Learning (QML) algorithms. Inspired by Google DeepMind's FunSearch, the proposed system works on abstract level to iteratively generates and refines quantum transformations of classical machine learning algorithms (concepts), such as the Multi-Layer Perceptron, forward-forward and backpropagation algorithms. As a proof of concept, this work highlights the potential of agentic frameworks to systematically explore classical machine learning concepts and adapt them for quantum computing, paving the way for efficient and automated development of QML algorithms. Future directions include incorporating planning mechanisms and optimizing strategy in the search space for broader applications in quantum-enhanced machine learning.

We will revisit this example later on. Using large language models (LLMs) to generate source code In this paper, we propose to utilise Dafny as an intermediate from natural language prompts is a popular and promising technology within a code-generating chatbot prototype idea with a wide range of applications. One of its limitations on the way to higher quality mainstream-language code. is that the generated code can be faulty at times, often in a Dafny is particularly well-suited for this purpose, because: subtle way, despite being presented to the user as correct. In this paper, we explore ways in which formal methods can assist with increasing the quality of code generated by an LLM. Instead of emitting code in a target language directly, we propose that the user guides the LLM to first generate 1. It allows for a clear distinction between the specification an opaque intermediate representation, in the verificationaware of a program--derived from a natural language language Dafny, that can be automatically validated prompt using an LLM--and its implementation, which for correctness against agreed on specifications. The correct can again be dialogically derived using an LLM with Dafny program is then compiled to the target language and verification feedback in the loop.

The rapid advancement of natural language processing (NLP) technologies, such as instruction-tuned large language models (LLMs), urges the development of modern evaluation protocols with human and machine feedback. We introduce Evalica, an open-source toolkit that facilitates the creation of reliable Figure 1: Evalica facilitates the highlighted aspects of and reproducible model leaderboards. This leaderboard-making that involve aggregation of judgements, paper presents its design, evaluates its performance, scoring the models with bootstrapped confidence and demonstrates its usability through intervals (CIs), and getting the final model ranks.

ICLabel is an important plug-in function in EEGLAB, the most widely used software for EEG data processing. A powerful approach to automated processing of EEG data involves decomposing the data by Independent Component Analysis (ICA) and then classifying the resulting independent components (ICs) using ICLabel. While EEGLAB pipelines support high-performance computing (HPC) platforms running the open-source Octave interpreter, the ICLabel plug-in is incompatible with Octave because of its specialized neural network architecture. To enhance cross-platform compatibility, we developed a Python version of ICLabel that uses standard EEGLAB data structures. We compared ICLabel MATLAB and Python implementations to data from 14 subjects. ICLabel returns the likelihood of classification in 7 classes of components for each ICA component. The returned IC classifications were virtually identical between Python and MATLAB, with differences in classification percentage below 0.001%.

In recent years, urban safety has become a paramount concern for city planners and law enforcement agencies. Accurate prediction of likely crime occurrences can significantly enhance preventive measures and resource allocation. However, many law enforcement departments lack the tools to analyze and apply advanced AI and ML techniques that can support city planners, watch programs, and safety leaders to take proactive steps towards overall community safety. This paper explores the effectiveness of ML techniques to predict spatial and temporal patterns of crimes in urban areas. Leveraging police dispatch call data from San Jose, CA, the research goal is to achieve a high degree of accuracy in categorizing calls into priority levels particularly for more dangerous situations that require an immediate law enforcement response. This categorization is informed by the time, place, and nature of the call. The research steps include data extraction, preprocessing, feature engineering, exploratory data analysis, implementation, optimization and tuning of different supervised machine learning models and neural networks. The accuracy and precision are examined for different models and features at varying granularity of crime categories and location precision. The results demonstrate that when compared to a variety of other models, Random Forest classification models are most effective in identifying dangerous situations and their corresponding priority levels with high accuracy (Accuracy = 85%, AUC = 0.92) at a local level while ensuring a minimum amount of false negatives. While further research and data gathering is needed to include other social and economic factors, these results provide valuable insights for law enforcement agencies to optimize resources, develop proactive deployment approaches, and adjust response patterns to enhance overall public safety outcomes in an unbiased way.

The availability of easy-to-use and reliable software implementations is important for allowing researchers in academia and industry to test, assess and take into use eXplainable AI (XAI) methods. This paper describes the \texttt{py-ciu} Python implementation of the Contextual Importance and Utility (CIU) model-agnostic, post-hoc explanation method and illustrates capabilities of CIU that go beyond the current state-of-the-art that could be useful for XAI practitioners in general.

This paper introduces $\textit{arfpy}$, a python implementation of Adversarial Random Forests (ARF) (Watson et al., 2023), which is a lightweight procedure for synthesizing new data that resembles some given data. The software $\textit{arfpy}$ equips practitioners with straightforward functionalities for both density estimation and generative modeling. The method is particularly useful for tabular data and its competitive performance is demonstrated in previous literature. As a major advantage over the mostly deep learning based alternatives, $\textit{arfpy}$ combines the method's reduced requirements in tuning efforts and computational resources with a user-friendly python interface. This supplies audiences across scientific fields with software to generate data effortlessly.

TensorFlow Addons is a repository of contributions that conform to well-established API patterns, but implement new functionality not available in core TensorFlow. TensorFlow natively supports a large number of operators, layers, metrics, losses, and optimizers. However, in a fast moving field like ML, there are many interesting new developments that cannot be integrated into core TensorFlow (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the community). The maintainers of TensorFlow Addons can be found in the CODEOWNERS file of the repo. This file is parsed and pull requests will automatically tag the owners using a bot.

In this article, we will look at the knapsack problem, a classic in computer science. We will explain why it is difficult to solve using traditional computational methods, and how genetic programming can help find a "good enough" solution. Afterwards, we will look at a Python implementation of just such a solution to test out for ourselves. The knapsack problem can be used to illustrate the difficulty of solving complex computational problems. In its simplest form, one is given a knapsack of a certain capacity, a set of items with their sizes and values, and asked to maximize the value of the items placed in the knapsack without exceeding the capacity.