19 citations · 37 across the 16 of their papers we have counts for
6 papers · 1 filter
Weight-Adjusted Gradients Reveal Parameter Importance and Failure Modes in LLMs
Shrestha Datta, Hongfu Liu, Anshuman Chhabra
Understanding which parameters are influential in Large Language Models (LLMs) is central to improving their efficiency, reliability, and interpretability. We introduce Weight-Adju…
What Is The Performance Ceiling of My Classifier? Utilizing Category-Wise Influence Functions for Pareto Frontier Analysis
Shahriar Kabir Nahin, Wenxiao Xiao, Joshua Liu +2
Data-centric learning seeks to improve model performance from the perspective of data quality, and has been drawing increasing attention in the machine learning community. Among it…
Unraveling Indirect In-Context Learning Using Influence Functions
Hadi Askari, Shivanshu Gupta, Terry Tong +3
In this work, we introduce a novel paradigm for generalized In-Context Learning (ICL), termed Indirect In-Context Learning. In Indirect ICL, we explore demonstration selection stra…
Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models
Anshuman Chhabra, Bo Li, Jian Chen +2
A core data-centric learning challenge is the identification of training samples that are detrimental to model performance. Influence functions serve as a prominent tool for this t…
On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses
Anshuman Chhabra, Ashwin Sekhari, Prasant Mohapatra
Clustering models constitute a class of unsupervised machine learning methods which are used in a number of application pipelines, and play a vital role in modern data science. Wit…
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework
Anshuman Chhabra, Peizhao Li, Prasant Mohapatra +1
Clustering algorithms are widely used in many societal resource allocation applications, such as loan approvals and candidate recruitment, among others, and hence, biased or unfair…