Search Results for author:
Found 26 papers, 5 papers with code
Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity
The proposed data-dependent framework exhibits a higher degree of sample efficiency and is empirically demonstrated to enhance the classification model's performance on both full- and missing-modality data in the low-data regime across various multimodal learning tasks.
Federated Fairness without Access to Sensitive Groups
Current approaches to group fairness in federated learning assume the existence of predefined and labeled sensitive groups during training.
YAMLE: Yet Another Machine Learning Environment
YAMLE: Yet Another Machine Learning Environment is an open-source framework that facilitates rapid prototyping and experimentation with machine learning (ML) models and methods.
SAE: Single Architecture Ensemble Neural Networks
Ensembles of separate neural networks (NNs) have shown superior accuracy and confidence calibration over single NN across tasks.
PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
Our paper offers a new approach to certify the performance of machine learning models in the presence of adversarial attacks with population level risk guarantees.
HgbNet: predicting hemoglobin level/anemia degree from EHR data
EHR-based hemoglobin level/anemia degree prediction is non-invasive and rapid but still faces some challenges due to the fact that EHR data is typically an irregular multivariate time series containing a significant number of missing values and irregular time intervals.
Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions
The deployment of large language models (LLMs) raises concerns regarding their cultural misalignment and potential ramifications on individuals from various cultural norms.
Navigating Noise: A Study of How Noise Influences Generalisation and Calibration of Neural Networks
Enhancing the generalisation abilities of neural networks (NNs) through integrating noise such as MixUp or Dropout during training has emerged as a powerful and adaptable technique.
An information-Theoretic Approach to Semi-supervised Transfer Learning
We focus on the task of semi-supervised transfer learning, in which unlabeled samples from the target dataset are available during network training on the source dataset.
On the Generalization Error of Meta Learning for the Gibbs Algorithm
We analyze the generalization ability of joint-training meta learning algorithms via the Gibbs algorithm.
How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?
Our findings offer new insights that the generalization performance of SSL with pseudo-labeling is affected not only by the information between the output hypothesis and input training data but also by the information {\em shared} between the {\em labeled} and {\em pseudo-labeled} data samples.
Simple Regularisation for Uncertainty-Aware Knowledge Distillation
We demonstrate the generality of the approach on combinations of toy data, SVHN/CIFAR-10, simple to complex NN architectures and different tasks.
Tighter Expected Generalization Error Bounds via Convexity of Information Measures
Due to the convexity of the information measures, the proposed bounds in terms of Wasserstein distance and total variation distance are shown to be tighter than their counterparts based on individual samples in the literature.
Minimax Demographic Group Fairness in Federated Learning
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models.
An Exact Characterization of the Generalization Error for the Gibbs Algorithm
Various approaches have been developed to upper bound the generalization error of a supervised learning algorithm.
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm
We provide an information-theoretic analysis of the generalization ability of Gibbs-based transfer learning algorithms by focusing on two popular transfer learning approaches, $\alpha$-weighted-ERM and two-stage-ERM.
Federating for Learning Group Fair Models
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models.
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator
To further improve the overall algorithmic-hardware performance, a co-design framework is proposed to explore the most fitting algorithmic-hardware configurations for Bayesian RNNs.
High-Performance FPGA-based Accelerator for Bayesian Neural Networks
Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems.
ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation
Fully convolutional U-shaped neural networks have largely been the dominant approach for pixel-wise image segmentation.
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks
Bayesian neural networks (BNNs) are making significant progress in many research areas where decision-making needs to be accompanied by uncertainty estimation.
VINNAS: Variational Inference-based Neural Network Architecture Search
In recent years, neural architecture search (NAS) has received intensive scientific and industrial interest due to its capability of finding a neural architecture with high accuracy for various artificial intelligence tasks such as image classification or object detection.
Learning data-derived privacy preserving representations from information metrics
We study space-preserving transformations where the utility provider can use the same algorithm on original and sanitized data, a critical and novel attribute to help service providers accommodate varying privacy requirements with a single set of utility algorithms.
Learning to Collaborate for User-Controlled Privacy
As such, users and utility providers should collaborate in data privacy, a paradigm that has not yet been developed in the privacy research community.
Designed Measurements for Vector Count Data
We consider design of linear projection measurements for a vector Poisson signal model.
Generalized Bregman Divergence and Gradient of Mutual Information for Vector Poisson Channels
We investigate connections between information-theoretic and estimation-theoretic quantities in vector Poisson channel models.
