338,742 research outputs found
Active Learning of Multi-Index Function Models
We consider the problem of actively learning \textit{multi-index} functions of the form from point evaluations of . We assume that the function is defined on an -ball in \Real^d, is twice continuously differentiable almost everywhere, and is a rank matrix, where . We propose a randomized, active sampling scheme for estimating such functions with uniform approximation guarantees. Our theoretical developments leverage recent techniques from low rank matrix recovery, which enables us to derive an estimator of the function along with sample complexity bounds. We also characterize the noise robustness of the scheme, and provide empirical evidence that the high-dimensional scaling of our sample complexity bounds are quite accurate
Prediction of multitasking performance post-longitudinal tDCS via EEG-based functional connectivity and machine learning methods
Predicting and understanding the changes in cognitive performance, especially
after a longitudinal intervention, is a fundamental goal in neuroscience.
Longitudinal brain stimulation-based interventions like transcranial direct
current stimulation (tDCS) induce short-term changes in the resting membrane
potential and influence cognitive processes. However, very little research has
been conducted on predicting these changes in cognitive performance
post-intervention. In this research, we intend to address this gap in the
literature by employing different EEG-based functional connectivity analyses
and machine learning algorithms to predict changes in cognitive performance in
a complex multitasking task. Forty subjects were divided into experimental and
active-control conditions. On Day 1, all subjects executed a multitasking task
with simultaneous 32-channel EEG being acquired. From Day 2 to Day 7, subjects
in the experimental condition undertook 15 minutes of 2mA anodal tDCS
stimulation during task training. Subjects in the active-control condition
undertook 15 minutes of sham stimulation during task training. On Day 10, all
subjects again executed the multitasking task with EEG acquisition.
Source-level functional connectivity metrics, namely phase lag index and
directed transfer function, were extracted from the EEG data on Day 1 and Day
10. Various machine learning models were employed to predict changes in
cognitive performance. Results revealed that the multi-layer perceptron and
directed transfer function recorded a cross-validation training RMSE of 5.11%
and a test RMSE of 4.97%. We discuss the implications of our results in
developing real-time cognitive state assessors for accurately predicting
cognitive performance in dynamic and complex tasks post-tDCS interventionComment: 16 pages, presented at the 30th International Conference on Neural
Information Processing (ICONIP2023), Changsha, China, November 202
An empirical learning-based validation procedure for simulation workflow
Simulation workflow is a top-level model for the design and control of
simulation process. It connects multiple simulation components with time and
interaction restrictions to form a complete simulation system. Before the
construction and evaluation of the component models, the validation of
upper-layer simulation workflow is of the most importance in a simulation
system. However, the methods especially for validating simulation workflow is
very limit. Many of the existing validation techniques are domain-dependent
with cumbersome questionnaire design and expert scoring. Therefore, this paper
present an empirical learning-based validation procedure to implement a
semi-automated evaluation for simulation workflow. First, representative
features of general simulation workflow and their relations with validation
indices are proposed. The calculation process of workflow credibility based on
Analytic Hierarchy Process (AHP) is then introduced. In order to make full use
of the historical data and implement more efficient validation, four learning
algorithms, including back propagation neural network (BPNN), extreme learning
machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture
model (FIGMN), are introduced for constructing the empirical relation between
the workflow credibility and its features. A case study on a landing-process
simulation workflow is established to test the feasibility of the proposed
procedure. The experimental results also provide some useful overview of the
state-of-the-art learning algorithms on the credibility evaluation of
simulation models
Client-server multi-task learning from distributed datasets
A client-server architecture to simultaneously solve multiple learning tasks
from distributed datasets is described. In such architecture, each client is
associated with an individual learning task and the associated dataset of
examples. The goal of the architecture is to perform information fusion from
multiple datasets while preserving privacy of individual data. The role of the
server is to collect data in real-time from the clients and codify the
information in a common database. The information coded in this database can be
used by all the clients to solve their individual learning task, so that each
client can exploit the informative content of all the datasets without actually
having access to private data of others. The proposed algorithmic framework,
based on regularization theory and kernel methods, uses a suitable class of
mixed effect kernels. The new method is illustrated through a simulated music
recommendation system
Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning
We pose an active perception problem where an autonomous agent actively
interacts with a second agent with potentially adversarial behaviors. Given the
uncertainty in the intent of the other agent, the objective is to collect
further evidence to help discriminate potential threats. The main technical
challenges are the partial observability of the agent intent, the adversary
modeling, and the corresponding uncertainty modeling. Note that an adversary
agent may act to mislead the autonomous agent by using a deceptive strategy
that is learned from past experiences. We propose an approach that combines
belief space planning, generative adversary modeling, and maximum entropy
reinforcement learning to obtain a stochastic belief space policy. By
accounting for various adversarial behaviors in the simulation framework and
minimizing the predictability of the autonomous agent's action, the resulting
policy is more robust to unmodeled adversarial strategies. This improved
robustness is empirically shown against an adversary that adapts to and
exploits the autonomous agent's policy when compared with a standard
Chance-Constraint Partially Observable Markov Decision Process robust approach
- …