Related papers: Balancing Privacy, Robustness, and Efficiency in Machine Learning

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arXiv Detail & Related papers (2025-07-09T20:09:00Z)
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This thesis develops both algorithmic and theoretical foundations to address key reliability issues arising from distributional uncertainty and unknown classes.<n>A core contribution is the development of an unknown-aware learning framework that enables models to recognize and handle novel inputs without labeled OOD data.<n>The thesis also extends reliable learning to foundation models, including large language models (LLMs)<n>Overall, these contributions promote unknown-aware learning as a new paradigm, and we hope it can advance the reliability of AI systems with minimal human efforts.
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Data Collaboration Analysis Over Matrix Manifolds [0.0]
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Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
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Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN [77.0623472106488]
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We present the first tight analysis of the error incurred by any algorithm ensuring robustness against a fraction of adversarial machines. Our analysis exhibits a fundamental trade-off between privacy, robustness, and utility.
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We introduce a deep reinforcement learning framework for safe decision making in uncertain environments. We provide robustness guarantees for this framework by showing it is equivalent to a specific class of distributionally robust safe reinforcement learning problems. In experiments on continuous control tasks with safety constraints, we demonstrate that our framework produces robust performance and safety at deployment time across a range of perturbed test environments.
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We present an algorithm for training self-destructing models leveraging techniques from meta-learning and adversarial learning. In a small-scale experiment, we show MLAC can largely prevent a BERT-style model from being re-purposed to perform gender identification.
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Holistic Adversarial Robustness of Deep Learning Models [91.34155889052786]
Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability. This paper provides a comprehensive overview of research topics and foundational principles of research methods for adversarial robustness of deep learning models.
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Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System [78.60415450507706]
Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty.
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Bounding Information Leakage in Machine Learning [26.64770573405079]
This paper investigates fundamental bounds on information leakage. We identify and bound the success rate of the worst-case membership inference attack. We derive bounds on the mutual information between the sensitive attributes and model parameters.
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Adversarial Robustness under Long-Tailed Distribution [93.50792075460336]
Adversarial robustness has attracted extensive studies recently by revealing the vulnerability and intrinsic characteristics of deep networks. In this work we investigate the adversarial vulnerability as well as defense under long-tailed distributions. We propose a clean yet effective framework, RoBal, which consists of two dedicated modules, a scale-invariant and data re-balancing.
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Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
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SPEED: Secure, PrivatE, and Efficient Deep learning [2.283665431721732]
We introduce a deep learning framework able to deal with strong privacy constraints. Based on collaborative learning, differential privacy and homomorphic encryption, the proposed approach advances state-of-the-art.
arXiv Detail & Related papers (2020-06-16T19:31:52Z)
Hidden Cost of Randomized Smoothing [72.93630656906599]
In this paper, we point out the side effects of current randomized smoothing. Specifically, we articulate and prove two major points: 1) the decision boundaries of smoothed classifiers will shrink, resulting in disparity in class-wise accuracy; 2) applying noise augmentation in the training process does not necessarily resolve the shrinking issue due to the inconsistent learning objectives.
arXiv Detail & Related papers (2020-03-02T23:37:42Z)

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