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Found 17 papers, 5 papers with code
Debiasing Machine Unlearning with Counterfactual Examples
This bias emerges from two main sources: (1) data-level bias, characterized by uneven data removal, and (2) algorithm-level bias, which leads to the contamination of the remaining dataset, thereby degrading model accuracy.
$\nabla τ$: Gradient-based and Task-Agnostic machine Unlearning
In this study, we introduce Gradient-based and Task-Agnostic machine Unlearning ($\nabla \tau$), an optimization framework designed to remove the influence of a subset of training data efficiently.
Community Membership Hiding as Counterfactual Graph Search via Deep Reinforcement Learning
Community detection techniques are useful tools for social media platforms to discover tightly connected groups of users who share common interests.
Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models
In this paper, we present a groundbreaking paradigm for human-computer interaction that revolutionizes the traditional notion of an operating system.
A Survey on Decentralized Federated Learning
In contrast to standard ML, where data must be collected at the exact location where training is performed, FL takes advantage of the computational capabilities of millions of edge devices to collaboratively train a shared, global model without disclosing their local private data.
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples
By reversing the learning process of the recommendation model, we thus develop a proficient greedy algorithm to generate fabricated user profiles and their associated interaction records for the aforementioned surrogate model.
A Byzantine-Resilient Aggregation Scheme for Federated Learning via Matrix Autoregression on Client Updates
In this work, we propose FLANDERS, a novel federated learning (FL) aggregation scheme robust to Byzantine attacks.
MUSTACHE: Multi-Step-Ahead Predictions for Cache Eviction
In this work, we propose MUSTACHE, a new page cache replacement algorithm whose logic is learned from observed memory access requests rather than fixed like existing policies.
Sparse Vicious Attacks on Graph Neural Networks
In this work, we focus on a specific, white-box attack to GNN-based link prediction models, where a malicious node aims to appear in the list of recommended nodes for a given target victim.
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations
Although powerful, it is very difficult for a GNN-based recommender system to attach tangible explanations of why a specific item ends up in the list of suggestions for a given user.
ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models
However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood, thus they are hard to generalize for complex models and inefficient for large datasets.
NEWRON: A New Generalization of the Artificial Neuron to Enhance the Interpretability of Neural Networks
In this work, we formulate NEWRON: a generalization of the McCulloch-Pitts neuron structure.
Turning Federated Learning Systems Into Covert Channels
Although the effect of the model poisoning is negligible to other participants, and does not alter the overall model performance, it can be observed by a malicious receiver and used to transmit a single bit.
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
In this work, we propose a method for generating CF explanations for GNNs: the minimal perturbation to the input (graph) data such that the prediction changes.
Treant: Training Evasion-Aware Decision Trees
Despite its success and popularity, machine learning is now recognized as vulnerable to evasion attacks, i. e., carefully crafted perturbations of test inputs designed to force prediction errors.
You Must Have Clicked on this Ad by Mistake! Data-Driven Identification of Accidental Clicks on Mobile Ads with Applications to Advertiser Cost Discounting and Click-Through Rate Prediction
We then estimate dwell time thresholds of accidental clicks from that component.
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
There are many circumstances however where it is important to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter such a prediction when the mutated instance is input back to the model.
