A popular algorithm configuration tuning paradigm is dynamic algorithm configuration (DAC), in which an agent learns dynamic configuration policies across instances by reinforcement learning (RL). However, in many complex algorithms, there may exist different types of configuration hyperparameters, and such heterogeneity may bring difficulties for classic DAC which uses a single-agent RL policy. In this paper, we aim to address this issue and propose multi-agent DAC (MA-DAC), with one agent working for one type of configuration hyperparameter. MA-DAC formulates the dynamic configuration of a complex algorithm with multiple types of hyperparameters as a contextual multi-agent Markov decision process and solves it by a cooperative multi-agent RL (MARL) algorithm. To instantiate, we apply MA-DAC to a well-known optimization algorithm for multi-objective optimization problems. Experimental results show the effectiveness of MA-DAC in not only achieving superior performance compared with other configuration tuning approaches based on heuristic rules, multi-armed bandits, and single-agent RL, but also being capable of generalizing to different problem classes. Furthermore, we release the environments in this paper as a benchmark for testing MARL algorithms, with the hope of facilitating the application of MARL.

A popular algorithm configuration tuning paradigm is dynamic algorithm configuration (DAC), in which an agent learns dynamic configuration policies across instances by reinforcement learning (RL). However, in many complex algorithms, there may exist different types of configuration hyperparameters, and such heterogeneity may bring difficulties for classic DAC which uses a single-agent RL policy. In this paper, we aim to address this issue and propose multi-agent DAC (MA-DAC), with one agent working for one type of configuration hyperparameter. MA-DAC formulates the dynamic configuration of a complex algorithm with multiple types of hyperparameters as a contextual multi-agent Markov decision process and solves it by a cooperative multi-agent RL (MARL) algorithm. To instantiate, we apply MA-DAC to a well-known optimization algorithm for multi-objective optimization problems.

This paper gives a complete analysis of how many iterations are required for a Krylov subspace method to approximately solve the "trust region" and "cubic-regularized" quadratic minimization problems. These problems take the form: min x T A x b Tx subject to x R or with an additional regularization term of param* x 3. A is a symmetric, but not necessarily PSD matrix (i.e. it can have negative eigenvalues). The objective function is not necessarily convex. Problems of this form are important in a number of applications, especially as subroutines for regularized Newton methods. In addition to their practical importance, such methods have recently been used to give the fastest theoretical runtimes for finding stationary points and local minima of general non-convex objective functions.

ChatGPT is an AI language model developed by OpenAI that has gained widespread recognition for its ability to generate human-like responses to natural language input. One of the key technologies that underlie the development of ChatGPT is Python, which is a high-level, interpreted programming language widely used in the field of artificial intelligence and machine learning. Python is an ideal language for developing AI models like ChatGPT because of its simplicity, flexibility, and vast ecosystem of libraries and frameworks. Python has become the language of choice for machine learning and natural language processing due to its ease of use, readability, and high-level syntax, which makes it easy to write and understand complex algorithms. One of the key libraries used in the development of ChatGPT is TensorFlow, an open-source machine learning library developed by Google.

Consumer Financial Protection Bureau (CFPB) issues policy statement on credit decisions based on complex algorithms. On May 26, the CFPB issued Circular 2022-03, which addresses an important question about algorithmic decision-making: "When creditors make credit decisions based on complex algorithms that prevent creditors from accurately identifying the specific reasons for denying credit or taking other adverse actions, do these creditors need to comply with the Equal Credit Opportunity Act's requirement to provide a statement of specific reasons to applicants against whom adverse action is taken?" The Circular says yes, compliance with ECOA and Regulation B is required even if complex algorithms (including AI and machine learning) make it difficult to accurately identify the specific reasons for taking the adverse action. Further, the Circular makes clear that those laws "do not permit creditors to use complex algorithms when doing so means they cannot provide the specific and accurate reasons for adverse actions." White House executive order calls for study of predictive algorithms used by law enforcement agencies.

Before we understand more about Logistic Regression, let's first recap some important definitions which will give us a better understanding of the topic. Logistic Regression comes under Supervised Learning. Supervised Learning is when the algorithm learns on a labeled dataset and analyses the training data. These labeled data sets have inputs and expected outputs. Supervised learning can be further split into classification and regression. Classification is about predicting a label, by identifying which category an object belongs to based on different parameters.

In the next few decades, Artificial Intelligence (AI) will be the biggest commercial opportunity in the world. As we gain access to an ever-richer tapestry of data and knowledge, the enhancement of deep learning through AI is intrinsically linked to the rise in commercial space and satellite activity. As the space and satellite industry in Cornwall county, UK scales at pace, so too does the region's AI capabilities. From edge AI for manufacturing, to AI algorithms being developed to remove cloud cover and unlock satellite data for business transformation – Cornwall is home to a hugely unique mix of companies tapping into the mutually beneficial relationship between the two technologies, in turn becoming a hub for innovation in AI applications. Spearheading Cornwall's acceleration towards becoming the UK's premier location for space manipulated AI and deep learning are the team at Goonhilly Earth Station Ltd, home of the UK's Space AI institute.

Are you planning to start a career in Artificial Intelligence and Machine learning? Well, then this article is for you. Building a career in AI and ML is not easy nor hard either. But it requires a dedicated approach. Sometimes when you're from an IT background, you may feel like swapping the career options too, because of the diverse opportunities.

According to Stanford Researcher, John McCarthy, "Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial Intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable." Simply put, AI's goal is to make computers/computer programs smart enough to imitate the human mind behaviour. Knowledge Engineering is an essential part of AI research. Machines and programs need to have bountiful information related to the world to often act and react like human beings.

"So what are the characteristics of these clustered residents?" We had used the most advanced, recently released model to segment the residents of a smart city. The whole model was a black box, so we have no idea how it does the segmentation but gave the highest accurate clusters. I thought for a minute; I couldn't come up with an answer. Our model had no interpretability.