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Found 25 papers, 8 papers with code
A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks
Recent advancements in Vision-Language Models (VLMs) have enabled complex multimodal tasks by processing text and image data simultaneously, significantly enhancing the field of artificial intelligence.
SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation
Large Language Models (LLMs) have transformed machine learning but raised significant legal concerns due to their potential to produce text that infringes on copyrights, resulting in several high-profile lawsuits.
Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs
In this paper, we propose GraphEval to evaluate an LLM's performance using a substantially large test dataset.
Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch
Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that require domain-specific expertise, making their widespread adoption challenging.
Learning the irreversible progression trajectory of Alzheimer's disease
Specifically, we introduce a monotonicity constraint that encourages the model to predict disease risk in a consistent and ordered manner across follow-up visits.
FADES: Fair Disentanglement with Sensitive Relevance
Learning fair representation in deep learning is essential to mitigate discriminatory outcomes and enhance trustworthiness.
SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
This new framework utilizes data that have similar labels when estimating fairness on a particular label group for better stability, and can unify DP and EOp.
Difficulty-Based Sampling for Debiased Contrastive Representation Learning
We theoretically show that the triplet loss amplifies the bias in self-supervised representation learning.
Fairness without Demographics through Knowledge Distillation
Most of existing work on fairness assumes available demographic information in the training set.
Forecast combinations: an over 50-year review
Forecast combinations have flourished remarkably in the forecasting community and, in recent years, have become part of the mainstream of forecasting research and activities.
A Unified Study of Machine Learning Explanation Evaluation Metrics
The growing need for trustworthy machine learning has led to the blossom of interpretability research.
Self-Interpretable Model with Transformation Equivariant Interpretation
With the proliferation of machine learning applications in the real world, the demand for explaining machine learning predictions continues to grow especially in high-stakes fields.
Self-Interpretable Model with TransformationEquivariant Interpretation
In this paper, we propose a self-interpretable model SITE with transformation-equivariant interpretations.
Group-Aware Threshold Adaptation for Fair Classification
As we only need an estimated probability distribution of model output instead of the classification model structure, our post-processing model can be applied to a wide range of classification models and improve fairness in a model-agnostic manner and ensure privacy.
Adversarial Fairness Network
To achieve this goal, we reformulate the data input by eliminating the sensitive information and strengthen model fairness by minimizing the marginal contribution of the sensitive feature.
Shapley Explanation Networks
This intrinsic explanation approach enables layer-wise explanations, explanation regularization of the model during training, and fast explanation computation at test time.
Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting
In this paper, a MTS forecasting framework that can capture the long-term trends and short-term fluctuations of time series in parallel is proposed.
Distributed ARIMA Models for Ultra-long Time Series
Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management.
Applications Computation
Approaching Machine Learning Fairness through Adversarial Network
In order to achieve this goal, we reformulate the data input by removing the sensitive information and strengthen model fairness by minimizing the marginal contribution of the sensitive feature.
Que será será? The uncertainty estimation of feature-based time series forecasts
In the training part, we use a collection of time series to train a model to explore how time series features affect the interval forecasting accuracy of different forecasting methods, which makes our proposed framework interpretable in terms of the contribution of each feature to the models' uncertainty prediction.
Methodology Applications Computation
An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms
This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace clustering, feature selection, robust principal component analysis, and so on.
Group Sparse Additive Machine
Among them, learning models with grouped variables have shown competitive performance for prediction and variable selection.
Regularized Modal Regression with Applications in Cognitive Impairment Prediction
Linear regression models have been successfully used to function estimation and model selection in high-dimensional data analysis.
Learning A Structured Optimal Bipartite Graph for Co-Clustering
In graph based co-clustering methods, a bipartite graph is constructed to depict the relation between features and samples.
