arXiv research

This paper examines allocation mechanisms in markets with transfer costs, showing how these costs affect economic efficiency.

New attention mechanism improves meta-transfer learning in dynamic tasks.

Transfer learning across different reinforcement learning (RL) tasks is becoming an increasingly valuable area of research. We consider a goal-based multi-task RL framework and mechanisms by which previously solved tasks can reduce sample complexity and regret when the agent is faced with a new task. Specifically, we i…

New smart contract mechanisms evade traditional AML systems by decoupling transaction roles.

We study few-shot supervised domain adaptation (DA) for regression problems, where only a few labeled target domain data and many labeled source domain data are available. Many of the current DA methods base their transfer assumptions on either parametrized distribution shift or apparent distribution similarities, e.g.…

Recently, in order to explore the mechanism behind wealth or income distribution, several models have been proposed by applying principles of statistical mechanics. These models share some characteristics, such as consisting of a group of individual agents, a pile of money and a specific trading rule. Whatever the trad…

New framework explains fast transfer of hyperparameters across model scales.

CICME estimates common and domain-specific causal mechanisms from multi-sensor data.

Taxonomy of knowledge modalities in RL for better transfer.

Statistical learning relies upon data sampled from a distribution, and we usually do not care what actually generated it in the first place. From the point of view of causal modeling, the structure of each distribution is induced by physical mechanisms that give rise to dependences between observables. Mechanisms, howe…

Adaptive transfer learning model for varying mechanisms across domains.

New method improves transfer and robustness of supervised contrastive learning.

EWS-GCN improves credit scoring by analyzing money transfer connections.

New method identifies optimal subset of stable information to transfer for better model generalization.

Proposes a method to improve treatment policies in data-scarce clinical settings.

This paper presents novel Gaussian process decentralized data fusion algorithms exploiting the notion of agent-centric support sets for distributed cooperative perception of large-scale environmental phenomena. To overcome the limitations of scale in existing works, our proposed algorithms allow every mobile sensing ag…

Geometric Graph Alignment enhances IoT intrusion detection using NID data.

The demand of artificial intelligent adoption for condition-based maintenance strategy is astonishingly increased over the past few years. Intelligent fault diagnosis is one critical topic of maintenance solution for mechanical systems. Deep learning models, such as convolutional neural networks (CNNs), have been succe…

DPLTM uses private winners to improve privacy and utility.

Modeling tech transfer to explain convergence in Central and Eastern Europe.

Humans can learn a variety of concepts and skills incrementally over the course of their lives while exhibiting many desirable properties, such as continual learning without forgetting, forward transfer and backward transfer of knowledge, and learning a new concept or task with only a few examples. Several lines of mac…

New PAC-Bayesian framework for flexible meta-learning.

We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able to efficiently use different data sources that are related to the sam…

Researchers use DT to transfer policies from one environment to another using causal reasoning.

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prosp…

Current paper addresses topology issues in PBSHM to enable transfer learning.

Improved language models with talking-heads attention.

Double descent in transfer learning explained for linear regression problems.

OpFlow predicts robust OD flows by learning choice potentials conditioned on spatial exposures.

DeTraC CNN detects COVID-19 from chest X-rays with high accuracy.

RTFE provides adversarial robustness to multiple models.

Paper tackles robust knowledge transfer in parallel RL tasks.

Paper tackles hypothesis transfer learning for black-box models.

Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task. As an alternative, we propose transfer with adapter modules. Adapter modules yield a compact and extens…

Improves transfer learning by weighting importance based on test-over-training density.

A simple strategy prevents negative transfer in transfer learning.

RECaST calibrates source models for target populations with uncertainty quantification.

Study on neural networks' performance in sequential task learning.

TAP transfers knowledge from unlabeled data to improve cross-modal learning.

GraphOpt learns the formation mechanism of graphs from observed structures.

We present an agent behavior based microscopic model that induces jumps, spikes and high volatility phases in the price process of a traded asset. We transfer dynamics of thermally activated jumps of an unexcited/ excited two state system discussed in the context of quantum mechanics to agent socio-economic behavior an…

We have studied numerically the statistical mechanics of the dynamic phenomena, including money circulation and economic mobility, in some transfer models. The models on which our investigations were performed are the basic model proposed by A. Dragulescu and V. Yakovenko [1], the model with uniform saving rate develop…

DDSTN improves breast cancer diagnosis by leveraging imbalanced ultrasound modalities.

A novel incentive mechanism improves fairness and participation in federated learning.

The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements. We introduce Regularized Hierarchical Policy Optimization (RHPO) to improve data-efficiency for domains with multiple dominant tasks and ultimately reduce require…

Bayesian meta-learning improves health prediction models across similar diseases.

Transfer learning methods for reinforcement learning (RL) domains facilitate the acquisition of new skills using previously acquired knowledge. The vast majority of existing approaches assume that the agents have the same design, e.g. same shape and action spaces. In this paper we address the problem of transferring pr…

End-to-end analysis of SGD for STL with adaptive sub-sampling.