Search Results for author:
Found 8 papers, 5 papers with code
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations
Adversarial attacks curated against NLP models are increasingly becoming practical threats.
TCAB: A Large-Scale Text Classification Attack Benchmark
In addition to the primary tasks of detecting and labeling attacks, TCAB can also be used for attack localization, attack target labeling, and attack characterization.
Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees
We also find that IBUG can achieve improved probabilistic performance by using different base GBRT models, and can more flexibly model the posterior distribution of a prediction than competing methods.
Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees
In the pursuit of better understanding GBDT predictions and generally improving these models, we adapt recent and popular influence-estimation methods designed for deep learning models to GBDTs.
Identifying Adversarial Attacks on Text Classifiers
The landscape of adversarial attacks against text classifiers continues to grow, with new attacks developed every year and many of them available in standard toolkits, such as TextAttack and OpenAttack.
TREX: Tree-Ensemble Representer-Point Explanations
The weights in the kernel expansion of the surrogate model are used to define the global or local importance of each training example.
Machine Unlearning for Random Forests
The upper levels of DaRE trees use random nodes, which choose split attributes and thresholds uniformly at random.
EGGS: A Flexible Approach to Relational Modeling of Social Network Spam
In this paper, we present Extended Group-based Graphical models for Spam (EGGS), a general-purpose method for classifying spam in online social networks.
