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A System for Automated Open-Source Threat Intelligence Gathering and Management
Sophisticated cyber attacks have plagued many high-profile businesses. T...
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A System for Efficiently Hunting for Cyber Threats in Computer Systems Using Threat Intelligence
Log-based cyber threat hunting has emerged as an important solution to c...
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Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
As machine learning systems grow in scale, so do their training data req...
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Extracting Training Data from Large Language Models
It has become common to publish large (billion parameter) language model...
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PrivFramework: A System for Configurable and Automated Privacy Policy Compliance
Today's massive scale of data collection coupled with recent surges of c...
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Towards Defending Multiple Adversarial Perturbations via Gated Batch Normalization
There is now extensive evidence demonstrating that deep neural networks ...
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Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams
Adversarial examples are a widely studied phenomenon in machine learning...
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Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence
Log-based cyber threat hunting has emerged as an important solution to c...
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Language Models are Open Knowledge Graphs
This paper shows how to construct knowledge graphs (KGs) from pre-traine...
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F2ED-Learning: Good Fences Make Good Neighbors
In this paper, we present F2ED-Learning, the first federated learning pr...
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Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer
Federated learning enables multiple parties to collaboratively learn a m...
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A Principled Approach to Data Valuation for Federated Learning
Federated learning (FL) is a popular technique to train machine learning...
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Measuring Massive Multitask Language Understanding
We propose a new test to measure a text model's multitask accuracy. The ...
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Compositional Generalization via Neural-Symbolic Stack Machines
Despite achieving tremendous success, existing deep learning models have...
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Aligning AI With Shared Human Values
We show how to assess a language model's knowledge of basic concepts of ...
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Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis
The use of deep learning techniques has achieved significant progress fo...
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BeeTrace: A Unified Platform for Secure Contact Tracing that Breaks Data Silos
Contact tracing is an important method to control the spread of an infec...
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The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
We introduce three new robustness benchmarks consisting of naturally occ...
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Towards practical differentially private causal graph discovery
Causal graph discovery refers to the process of discovering causal relat...
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Imitation Attacks and Defenses for Black-box Machine Translation Systems
We consider an adversary looking to steal or attack a black-box machine ...
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Epione: Lightweight Contact Tracing with Strong Privacy
Contact tracing is an essential tool in containing infectious diseases s...
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Pretrained Transformers Improve Out-of-Distribution Robustness
Although pretrained Transformers such as BERT achieve high accuracy on i...
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Anomalous Instance Detection in Deep Learning: A Survey
Deep Learning (DL) is vulnerable to out-of-distribution and adversarial ...
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Proceedings of the AAAI-20 Workshop on Intelligent Process Automation (IPA-20)
This is the Proceedings of the AAAI-20 Workshop on Intelligent Process A...
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Hierarchical Variational Imitation Learning of Control Programs
Autonomous agents can learn by imitating teacher demonstrations of the i...
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Synthetic Datasets for Neural Program Synthesis
The goal of program synthesis is to automatically generate programs in a...
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Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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A Benchmark for Anomaly Segmentation
Detecting out-of-distribution examples is important for safety-critical ...
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REFIT: a Unified Watermark Removal Framework for Deep Learning Systems with Limited Data
Deep neural networks (DNNs) have achieved tremendous success in various ...
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The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
This paper studies model-inversion attacks, in which the access to a mod...
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An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms
This paper focuses on valuating training data for supervised learning ta...
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Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy
Outlier detection and novelty detection are two important topics for ano...
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Duet: An Expressive Higher-order Language and Linear Type System for Statically Enforcing Differential Privacy
During the past decade, differential privacy has become the gold standar...
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Data Capsule: A New Paradigm for Automatic Compliance with Data Privacy Regulations
The increasing pace of data collection has led to increasing awareness o...
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Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms
Given a data set D containing millions of data points and a data consume...
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TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems
A trojan backdoor is a hidden pattern typically implanted in a deep neur...
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Keystone: An Open Framework for Architecting TEEs
Trusted execution environments (TEEs) are being used in all the devices ...
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Keystone: A Framework for Architecting TEEs
Trusted execution environments (TEEs) are becoming a requirement across ...
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Characterizing Attacks on Deep Reinforcement Learning
Deep reinforcement learning (DRL) has achieved great success in various ...
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Natural Adversarial Examples
We introduce natural adversarial examples -- real-world, unmodified, and...
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Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Self-supervision provides effective representations for downstream tasks...
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Impossibility of Full Decentralization in Permissionless Blockchains
Bitcoin uses blockchain technology and proof-of-work (PoW) mechanism whe...
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How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning
Machine learning has been widely applied to various applications, some o...
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SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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Towards Efficient Data Valuation Based on the Shapley Value
"How much is my data worth?" is an increasingly common question posed by...
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Sanctorum: A lightweight security monitor for secure enclaves
Enclaves have emerged as a particularly compelling primitive to implemen...
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Data Poisoning Attack against Unsupervised Node Embedding Methods
Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec)...
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Assessing Generalization in Deep Reinforcement Learning
Deep reinforcement learning (RL) has achieved breakthrough results on ma...
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Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation
Deep Neural Networks (DNNs) have been widely applied in various recognit...
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Characterizing Audio Adversarial Examples Using Temporal Dependency
Recent studies have highlighted adversarial examples as a ubiquitous thr...
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