This paper studies how to define an algorithm that, given an increasing number of tasks sampled from a defined environment, will train on them and learn a model that will be well suited for any new task sampled from the same environment. The scenario just described corresponds to the'learning to lean' problem where a learning agent improves its learning performance with the number of tasks. Specifically in this work the focus is on the'ridge regression' family of algorithms and the environment consists in tasks that can be solved by ridge regression with models around a common mean. In other words, we need a learning algorithm that besides solving regression problems, progressively learns how to approximate the environment model mean. The transfer risk is a measure of how much the knowledge acquired over certain available tasks allow to improve future learning.

Disclaimer: my background is in control theory and only recently I have invested most of time in reading and doing research in the area of machine learning and reinforcement learning with specific focus on robotics and control. I went through the submitted paper carefully, including the supplementary material. Therefore I am quite confident with my assessment, especially since the problem that the addressed problem is well inside my core expertise (adaptive control). As I previously said, I am very confident with the problem, less confident with the theoretical framework (reinforcement learning) used to solve it. The math presented in the paper is relatively shallow and carefully checked.

The paper presents a supervised non-parametric hypothesis transfer learning (HTL) approach for regression and its analysis, aimed at the cases where one has plenty of training data coming from the source task and few examples from the target one. The paper makes an assumption that the source and the target regression functions are related through so called transformation function (TF). The TF is assumed to have some parametric form (e.g. Once these parameters are learned, the hypothesis trained on the source task can be transformed to the hypothesis designated for the target task. The paper proposes two ways for estimation of these parameters, that is through kernel smoothing and kernel ridge regression.

Summary: This paper presents a new transfer learning approach using Bayesian Neural Network in MDPs. They are building on the existing framework of Hidden Parameter MDPs, and replace the Gaussian process with BNNs, thereby also modeling the joint uncertainty in the latent weights and the state space. Overall, this proposed approach is sound, well developed and seems to help scale the inference. The authors have also shown that it works well by applying it to multiple domains. The paper is extremely well written.

This paper proposes a novel Bayesian Optimization approach that is able to do transfer learning across tasks while remaining scalable. Originality: This is very original work. Bayesian Optimization can work with any probabilistic regression algorithm, so the use of Bayesian linear regression to make it more scalable is well-known, as are its limitations (e.g. it doesn't extrapolate well). The main novelty here lies in the extension to multi-task learning, which allows it to benefit from prior evaluations on previous tasks. When such evaluations are available, this can provide a significant advantage.

The objective of active level set estimation for a black-box function is to precisely identify regions where the function values exceed or fall below a specified threshold by iteratively performing function evaluations to gather more information about the function. This becomes particularly important when function evaluations are costly, drastically limiting our ability to acquire large datasets. A promising way to sample-efficiently model the black-box function is by incorporating prior knowledge from a related function. However, this approach risks slowing down the estimation task if the prior knowledge is irrelevant or misleading. In this paper, we present a novel transfer learning method for active level set estimation that safely integrates a given prior knowledge while constantly adjusting it to guarantee a robust performance of a level set estimation algorithm even when the prior knowledge is irrelevant. We theoretically analyze this algorithm to show that it has a better level set convergence compared to standard transfer learning approaches that do not make any adjustment to the prior. Additionally, extensive experiments across multiple datasets confirm the effectiveness of our method when applied to various different level set estimation algorithms as well as different transfer learning scenarios.

Key to multitask learning is exploiting the relationships between different tasks in order to improve prediction performance. Most previous methods have focused on the case where tasks relations can be modeled as linear operators and regularization approaches can be used successfully. However, in practice assuming the tasks to be linearly related is often restrictive, and allowing for nonlinear structures is a challenge. In this paper, we tackle this issue by casting the problem within the framework of structured prediction. Our main contribution is a novel algorithm for learning multiple tasks which are related by a system of nonlinear equations that their joint outputs need to satisfy. We show that our algorithm can be efficiently implemented and study its generalization properties, proving universal consistency and learning rates. Our theoretical analysis highlights the benefits of non-linear multitask learning over learning the tasks independently. Encouraging experimental results show the benefits of the proposed method in practice.

We consider the Hypothesis Transfer Learning (HTL) problem where one incorporates a hypothesis trained on the source domain into the learning procedure of the target domain. Existing theoretical analysis either only studies specific algorithms or only presents upper bounds on the generalization error but not on the excess risk. In this paper, we propose a unified algorithm-dependent framework for HTL through a novel notion of transformation function, which characterizes the relation between the source and the target domains. We conduct a general risk analysis of this framework and in particular, we show for the first time, if two domains are related, HTL enjoys faster convergence rates of excess risks for Kernel Smoothing and Kernel Ridge Regression than those of the classical non-transfer learning settings. Experiments on real world data demonstrate the effectiveness of our framework.

Chris Stanford, Ph.D. Novateur Research Solutions 20110 Ashbrook Place, STE 170, Ashburn, VA 20147 cstanford@novateur.ai Submission Date: October 8, 2024 Liao, Liu, Kuai, Ma, He, Cao, Stanford, and Ma 3 ABSTRACT Understanding human mobility patterns is crucial for urban planning, transportation management, and public health. This study tackles two primary challenges in the field: the reliance on trajectory data, which often fails to capture the semantic interdependencies of activities, and the inherent incompleteness of real-world trajectory data. We have developed a model that reconstructs and learns human mobility patterns by focusing on semantic activity chains. We introduce a semisupervised iterative transfer learning algorithm to adapt models to diverse geographical contexts and address data scarcity. Our model is validated using comprehensive datasets from the United States, where it effectively reconstructs activity chains and generates high-quality synthetic mobility data, achieving a low Jensen-Shannon Divergence (JSD) value of 0.001, indicating a close similarity between synthetic and real data. Additionally, sparse GPS data from Egypt is used to evaluate the transfer learning algorithm, demonstrating successful adaptation of US mobility patterns to Egyptian contexts, achieving a 64% of increase in similarity, i.e., a JSD reduction from 0.09 to 0.03. This mobility reconstruction model and the associated transfer learning algorithm show significant potential for global human mobility modeling studies, enabling policymakers and researchers to design more effective and culturally tailored transportation solutions. Keywords: Human Mobility Patterns Modeling, Transfer Learning, Semi-Supervised Learning, Synthetic Mobility Data Liao, Liu, Kuai, Ma, He, Cao, Stanford, and Ma 4 INTRODUCTION Understanding human mobility patterns has become increasingly crucial in various fields, including urban planning, transportation management (1, 2), and public health (3). As urbanization accelerates and population mobility increases, the ability to accurately comprehend and predict human activity patterns has gained paramount importance. This knowledge not only aids in optimizing urban resource allocation but also provides essential insights for the development of smart cities.

We describe an approach to learning from long-tailed, imbalanced datasets that are prevalent in real-world settings. Here, the challenge is to learn accurate "fewshot" models for classes in the tail of the class distribution, for which little data is available. We cast this problem as transfer learning, where knowledge from the data-rich classes in the head of the distribution is transferred to the data-poor classes in the tail. Our key insights are as follows. First, we propose to transfer meta-knowledge about learning-to-learn from the head classes.