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Found 15 papers, 6 papers with code
Semantic Consistency for Assuring Reliability of Large Language Models
Large Language Models (LLMs) exhibit remarkable fluency and competence across various natural language tasks.
Network Security Modelling with Distributional Data
We investigate the detection of botnet command and control (C2) hosts in massive IP traffic using machine learning methods.
Measuring Reliability of Large Language Models through Semantic Consistency
While large pretrained language models (PLMs) demonstrate incredible fluency and performance on many natural language tasks, recent work has shown that well-performing PLMs are very sensitive to what prompts are feed into them.
Towards Algorithmic Fairness in Space-Time: Filling in Black Holes
New technologies and the availability of geospatial data have drawn attention to spatio-temporal biases present in society.
Feature Selection using e-values
In the context of supervised parametric models, we introduce the concept of e-values.
Scaling New Peaks: A Viewership-centric Approach to Automated Content Curation
Summarizing video content is important for video streaming services to engage the user in a limited time span.
Generalized Multivariate Signs for Nonparametric Hypothesis Testing in High Dimensions
High-dimensional data, where the dimension of the feature space is much larger than sample size, arise in a number of statistical applications.
An Interpretable Graph-based Mapping of Trustworthy Machine Learning Research
Although considerable progress has been made in the field of Trustworthy ML (TwML) in the recent past, much of the current characterization of this progress is qualitative.
Towards an Open Global Air Quality Monitoring Platform to Assess Children's Exposure to Air Pollutants in the Light of COVID-19 Lockdowns
This ongoing work attempts to understand and address the requirements of UNICEF, a leading organization working in children's welfare, where they aim to tackle the problem of air quality for children at a global level.
Detecting Bias in the Presence of Spatial Autocorrelation
In spite of considerable practical importance, current algorithmic fairness literature lacks technical methods to account for underlying geographic dependency while evaluating or mitigating bias issues for spatial data.
Bias Detection
Fairness
Applications
Computers and Society
Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs
Collections of probability distributions arise in a variety of applications ranging from user activity pattern analysis to brain connectomics.
Towards Integrating Fairness Transparently in Industrial Applications
Numerous Machine Learning (ML) bias-related failures in recent years have led to scrutiny of how companies incorporate aspects of transparency and accountability in their ML lifecycles.
On Weighted Multivariate Sign Functions
Multivariate sign functions are often used for robust estimation and inference.
Methodology
Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models
Following this, we develop a debiasing technique and asymptotic distributions of inter-layer directed edge weights that utilize already computed neighborhood selection coefficients for nodes in the upper layer.
Robust estimation of principal components from depth-based multivariate rank covariance matrix
Analyzing principal components for multivariate data from its spatial sign covariance matrix (SCM) has been proposed as a computationally simple and robust alternative to normal PCA, but it suffers from poor efficiency properties and is actually inadmissible with respect to the maximum likelihood estimator.
Statistics Theory Statistics Theory
