Machine learning

Causal effect estimation is a vital field of study employed in critical sectors like healthcare, economics, and social sciences. It concerns the evaluation of how modifications to one variable cause changes in another. Traditional approaches for this assessment, such as randomized controlled trials (RCTs) and observational studies, often involve structured data collection and experiments, making…

The methods of parameter-efficient fine-tuning (PEFT) are essential in machine learning as they allow large models to adapt to new tasks without requiring extensive computational resources. PEFT methods achieve this by only fine-tuning a small subset of parameters while leaving the majority of the model unchanged, aiming to make the adaptation process more efficient and…

Reinforcement Learning (RL) finetuning is an integral part of programming language models (LMs) to behave in a particular manner. However, in the current digital landscape, RL finetuning has to cater to numerous aims due to diverse human preferences. Therefore, multi-objective finetuning (MOFT) has come to the forefront as a superior method to train an LM,…

Aligning artificial intelligence (AI) models with human preferences is a complex process, especially in high-dimensional and sequential decision-making tasks. This alignment is critical for advancing AI technologies like fine-tuning large language models and enhancing robotic policies but is hindered by challenges such as computational complexity, high variance in policy gradients and instability in dynamic programming.…

Autonomous web navigation deals with the development of AI agents used in automating complex online tasks from data mining to booking delivery services. This helps in enhancing productivity by automating certain tasks in both consumer and enterprise domains. Traditional web agents working on such complex web tasks are usually inefficient and prone to errors due…

Autonomous web navigation, which involves using AI agents to perform complex online tasks, is growing in significance. Presently, these AI agents are typically used for tasks such as data retrieval, form submissions, and more sophisticated activities like finding cheap flights or booking accommodations. Utilizing large language models (LLMs) and other AI methodologies, the aim of…

Evaluating the performance of Artificial Intelligence (AI) and Machine Learning (ML) models is crucial, especially with the advent of Large Language Models (LLMs). These evaluations help to assess the abilities of these models and establish reliable systems based on their capabilities. However, certain practices, termed as Questionable Research Practices (QRPs), frequently compromise the authenticity and…

Artificial Intelligence and Machine Learning are rapidly advancing fields, and a crucial aspect of their progress is the evaluation of model performance, particularly with the advent of Large Language Models (LLMs). However, the integrity of these evaluations is often compromised by what are known as Questionable Research Practices (QRPs), which can severely inflate published results…