The training-conditional coverage performance of the conformal prediction is known to be empirically sound. Recently, there have been efforts to support this observation with theoretical guarantees. The training-conditional coverage bounds for jackknife+ and full-conformal prediction regions have been established via the notion of $(m,n)$-stability by Liang and Barber~[2023]. Although this notion is weaker than uniform stability, it is not clear how to evaluate it for practical models. In this paper, we study the training-conditional coverage bounds of full-conformal, jackknife+, and CV+ prediction regions from a uniform stability perspective which is known to hold for empirical risk minimization over reproducing kernel Hilbert spaces with convex regularization. We derive coverage bounds for finite-dimensional models by a concentration argument for the (estimated) predictor function, and compare the bounds with existing ones under ridge regression.

Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously and identifying the shared information among tasks. Most of existing MTL methods focus on learning linear models under the supervised setting. We propose a novel semi-supervised and nonlinear approach for MTL using vector fields. A vector field is a smooth mapping from the manifold to the tangent spaces which can be viewed as a directional derivative of functions on the manifold. We argue that vector fields provide a natural way to exploit the geometric structure of data as well as the shared differential structure of tasks, both of which are crucial for semi-supervised multi-task learning. In this paper, we develop multi-task vector field learning (MTVFL) which learns the predictor functions and the vector fields simultaneously. MTVFL has the following key properties.

Fine-tuning and testing a multilingual large language model is expensive and challenging for low-resource languages (LRLs). While previous studies have predicted the performance of natural language processing (NLP) tasks using machine learning methods, they primarily focus on high-resource languages, overlooking LRLs and shifts across domains. Focusing on LRLs, we investigate three factors: the size of the fine-tuning corpus, the domain similarity between fine-tuning and testing corpora, and the language similarity between source and target languages. We employ classical regression models to assess how these factors impact the model's performance. Our results indicate that domain similarity has the most critical impact on predicting the performance of Machine Translation models.

Machine learning (ML) models, and deep neural networks (DNNs) in particular, are very successful at solving problems both within and outside of science; the latest, spectacular scientific example is the prediction of protein folding (Jumper et al., 2021). However, many of these models are black boxes, and we do not know why they are so successful. As a consequence, the interpretability of ML models - understanding or gaining insight into how they work - is an important area of research in computer science. One kind of effort is towards a better grasp of theoretical properties of ML models, and to formulate what is called a theory of deep learning (Berner et al., 2021; Bahri et al., 2020). Another kind of effort is to provide ML practitioners with tools to understand predictions made by the ML models they deploy. This latter effort often runs under the label of explainable AI (xAI, see, e.g., Adadi and Berrada 2018). Philosophers have also started to pay more attention to interpretability recently; see Beisbart and Räz (2022) for a survey.

Today we're going to learn how to work with images to detect faces and extract facial features such as the eyes, nose, and mouth. This method has the potential to do many incredible things from analyzing faces to capturing facial features to tag people in photos, either manually or through machine learning. Also, you can create effects and filters to "enhance" your images, similar to the ones you see in Snapchat. We've previously covered how to work with OpenCV to detect shapes in images, but today we're taking it to a new level by introducing DLib, and abstracting face features from an image. But first of all, what is DLib?

This lecture discusses how decision trees can be used to represent predictor functions. Variations of the basic decision tree model provide some of the most powerful machine learning methods curren... Alexander Jung uploaded a video 1 week ago Classification Methods - Duration: 46 minutes. Our focus is on linear regression methods which can be expanded by feature constructions. Guest lecture of Prof. Minna Huotilainen on learning processes in human brains. Alexander Jung subscribed to a channel 3 weeks ago Playing For Change - Channel PFC is a movement created to inspire and connect the world through music. The idea for this project came from a common belief that music has the power to break down boundaries and overcome distances SubscribeSubscribedUnsubscribe1.9M This video explains how network Lasso can be used to learn localized linear models that allow "personalized" predictions for individual data points within a network.

Data science with the kind of power it gives you to analyze each and every bit of data you have at your disposal, to make smart & intelligent business decisions, is becoming a must have tool to understand and implement in your organization, it is very important that your business decisions are not based on intuition rather based on data analysis. "Data which you have in your repository is a gold mine, which needs to be harnessed with an intent to serve the humanity at large, as they are the key source of the same data. Data has a story to tell. Being a data engineer and a business leader it's your primary responsibility to treat them well, process it with appropriate ML model and build a solution which is relevant for both current and future user needs. With this intent, let's begin our journey of understanding supervised ML using Linear Regression model.

In this paper, we propose a compositional nonparametric method in which a model is expressed as a labeled binary tree of $2k+1$ nodes, where each node is either a summation, a multiplication, or the application of one of the $q$ basis functions to one of the $p$ covariates. We show that in order to recover a labeled binary tree from a given dataset, the sufficient number of samples is $O(k\log(pq)+\log(k!))$, and the necessary number of samples is $\Omega(k\log (pq)-\log(k!))$. We further propose a greedy algorithm for regression in order to validate our theoretical findings through synthetic experiments.

Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously and identifying the shared information among tasks. Most of existing MTL methods focus on learning linear models under the supervised setting. We propose a novel semi-supervised and nonlinear approach for MTL using vector fields. A vector field is a smooth mapping from the manifold to the tangent spaces which can be viewed as a directional derivative of functions on the manifold. We argue that vector fields provide a natural way to exploit the geometric structure of data as well as the shared differential structure of tasks, both are crucial for semi-supervised multi-task learning. In this paper, we develop multi-task vector field learning (MTVFL) which learns the prediction functions and the vector fields simultaneously. MTVFL has the following key properties: (1) the vector fields we learned are close to the gradient fields of the prediction functions; (2) within each task, the vector field is required to be as parallel as possible which is expected to span a low dimensional subspace; (3) the vector fields from all tasks share a low dimensional subspace. We formalize our idea in a regularization framework and also provide a convex relaxation method to solve the original non-convex problem. The experimental results on synthetic and real data demonstrate the effectiveness of our proposed approach.

Social annotation has become a popular manner for web users to manage and share their information and interests. While users' interests vary with time, tag correlation also changes from users' perspectives. In this work, we explore four methods for estimating temporal correlation between social tags and detect if a long-term trend emerges from the history of temporal correlation between two tags. Three types of trends are specified: steadily-shifting, stabilizing, and cyclic. To compare the results of the four estimation methods, an indirect evaluation is realized by applying detected trends to tag recommendation.