1 citations · 3 across the 12 of their papers we have counts for
11 papers · 1 filter
KNN Implementation Details Can Dramatically Change Performance: An Example from Cover Trees
Amol Khanna, Edward Raff
Nearest neighbor search algorithms are widely used and have a long history, but we find that key implementation details are seldom reviewed in articles about them. These details ca…
Cybersecurity Detection Classification with Reasoning-enabled Language Models
Amol Khanna, Manu Nandan, Cristian Viorel Popa +10
A major issue in Security Operations Centers (SOCs) is alert fatigue, as the number of detections reported is more than staff can triage in a given day. Prior work prompts or fine-…
Adversarial Samples Are Not Created Equal
Jennifer Crawford, Amol Khanna, Fred Lu +4
Over the past decade, numerous theories have been proposed to explain the widespread vulnerability of deep neural networks to adversarial evasion attacks. Among these, the theory o…
Differentially Private Iterative Screening Rules for Linear Regression
Amol Khanna, Fred Lu, Edward Raff
Linear -regularized models have remained one of the simplest and most effective tools in data science. Over the past decade, screening rules have risen in popularity as a way…
Multi-layer Radial Basis Function Networks for Out-of-distribution Detection
Amol Khanna, Chenyi Ling, Derek Everett +2
Existing methods for out-of-distribution (OOD) detection use various techniques to produce a score, separate from classification, that determines how ``OOD'' an input is. Our insig…
Feature Selection from Differentially Private Correlations
Ryan Swope, Amol Khanna, Philip Doldo +2
Data scientists often seek to identify the most important features in high-dimensional datasets. This can be done through -regularized regression, but this can become ineffici…
SoK: A Review of Differentially Private Linear Models For High-Dimensional Data
Amol Khanna, Edward Raff, Nathan Inkawhich
Linear models are ubiquitous in data science, but are particularly prone to overfitting and data memorization in high dimensions. To guarantee the privacy of training data, differe…
Comprehensive OOD Detection Improvements
Anish Lakkapragada, Amol Khanna, Edward Raff +1
As machine learning becomes increasingly prevalent in impactful decisions, recognizing when inference data is outside the model's expected input distribution is paramount for givin…
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations
Edward Raff, Amol Khanna, Fred Lu
To the best of our knowledge, there are no methods today for training differentially private regression models on sparse input data. To remedy this, we adapt the Frank-Wolfe algori…
Sparse Private LASSO Logistic Regression
Amol Khanna, Fred Lu, Edward Raff +1
LASSO regularized logistic regression is particularly useful for its built-in feature selection, allowing coefficients to be removed from deployment and producing sparse solutions.…
The Challenge of Differentially Private Screening Rules
Amol Khanna, Fred Lu, Edward Raff
Linear -regularized models have remained one of the simplest and most effective tools in data analysis, especially in information retrieval problems where n-grams over text wi…