In a model extraction attack, an adversary steals a copy of a remotely deployed machine learning model, given oracle prediction access. We taxonomize model extraction attacks around two objectives: *accuracy*, i.e., performing well on the underlying learning task, and *fidelity*, i.e., matching the predictions of the r…
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48 results for “model extraction”
Machine learning is being increasingly used by individuals, research institutions, and corporations. This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and resources to learn the model, and (b) a user-friendly query interface to access the model. However, suc…
Unified framework for unsupervised concept extraction simplifies guarantees.
problem Establishing guarantees for unsupervised concept extraction.
method Unified theoretical framework, meta-theorem for identifiability.
result Simplifies the task of proving guarantees for concept extraction.
Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack using Public Datacs.LG
Marich extracts high-fidelity models from public data with minimal queries.
problem Creating an accurate replica of a target ML model using few queries.
method Sequentially selects informative queries to maximize entropy and reduce model mismatch.
result Extracted models achieve 60-95% of target model's accuracy with 1,000-8,500 queries.
Fed-FEARE model extracts rules from multiple agencies' data securely.
problem Data privacy and secure rule extraction across multiple agencies.
method Federated F-score based ensemble tree model.
result Model performance measures significantly improved with federated learning.
Paper proposes a method to extract style features from unlabeled data.
problem Extracting fine-grained features like styles from unlabeled data.
method Contrastive conditioned variational autoencoders with mutual information constraints.
result The method efficiently extracts style features from real-world natural image datasets.
Although information extraction and coreference resolution appear together in many applications, most current systems perform them as ndependent steps. This paper describes an approach to integrated inference for extraction and coreference based on conditionally-trained undirected graphical models. We discuss the advan…
CURE extracts relations without supervision by clustering similar entity pairs.
problem Extracting relations unsupervised without considering sentence correlations.
method CURE uses Encoder-Decoder architecture for self-supervised learning and clustering similar relations.
result CURE outperforms state-of-the-art models on NYT and UNPC datasets.
AxCell automates results extraction from machine learning papers.
problem Difficulty in tracking progress in machine learning due to many papers.
method AxCell uses table segmentation and learns structural knowledge to extract results.
result AxCell significantly improves results extraction compared to existing methods.
VarDetect monitors API queries to detect model extraction attacks.
problem Attackers can extract ML models through API queries.
method VarDetect uses a modified variational autoencoder to track and detect attacker samples.
result VarDetect successfully detects and raises alarms for model extraction attacks.
Drug-Drug Interactions (DDIs) Extraction refers to the efforts to generate hand-made or automatic tools to extract embedded information from text and literature in the biomedical domain. Because of restrictions in hand-made efforts and their lower speed, Machine-Learning, or Deep-Learning approaches have become more po…
GenIE extracts structured text with fewer errors and more entities.
problem Closed information extraction with errors and limited entities.
method End-to-end autoregressive generation with bi-level constraints.
result State-of-the-art performance on closed information extraction.
ECC Analyzer uses LLMs to predict stock volatility from ECCs.
problem Leveraging unstructured ECC data for stock volatility prediction.
method Uses large language models to extract and fuse textual and audio features from ECCs.
result ECC Analyzer outperforms traditional benchmarks in volatility prediction.
Detecting PE malware files is now commonly approached using statistical and machine learning models. While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware. We propose an innovative machine learning approach to extra…
In this paper, we proposed a deep learning-based end-to-end method on the domain specified automatic term extraction (ATE), it considers possible term spans within a fixed length in the sentence and predicts them whether they can be conceptual terms. In comparison with current ATE methods, the model supports nested ter…
TXtract extracts structured knowledge from thousands of product categories.
problem Extracting structured knowledge from diverse product categories in e-commerce.
method TXtract uses a taxonomy-aware model with category conditional self-attention and multi-task learning.
result TXtract outperforms state-of-the-art approaches by up to 10% in F1 and 15% in coverage across all categories.
Supervised linear feature extraction can be achieved by fitting a reduced rank multivariate model. This paper studies rank penalized and rank constrained vector generalized linear models. From the perspective of thresholding rules, we build a framework for fitting singular value penalized models and use it for feature …
Along with the advent of deep neural networks came various methods of exploitation, such as fooling the classifier or contaminating its training data. Another such attack is known as model extraction, where provided API access to some black box neural network, the adversary extracts the underlying model. This is done b…
When creating an outfit, style is a criterion in selecting each fashion item. This means that style can be regarded as a feature of the overall outfit. However, in various previous studies on outfit generation, there have been few methods focusing on global information obtained from an outfit. To address this deficienc…
Neural networks in many varieties are touted as very powerful machine learning tools because of their ability to distill large amounts of information from different forms of data, extracting complex features and enabling powerful classification abilities. In this study, we use neural networks to extract features from b…
Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectivesstat.ML
We propose a method to extract interpretable rules from tree ensembles.
problem Tree ensembles are accurate but hard to interpret.
method Propose an estimator to extract compact sets of decision rules from tree ensembles.
result Our estimator improves accuracy and reveals useful relationships in the data.
Training machine learning (ML) models is expensive in terms of computational power, amounts of labeled data and human expertise. Thus, ML models constitute intellectual property (IP) and business value for their owners. Embedding digital watermarks during model training allows a model owner to later identify their mode…
Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling. In this work we present a machine learning based feature extraction scheme based on inception models for pixel classification tasks. We extract features under multi-scale and multi-layer sche…
CAI automates extraction and validation of corporate GHG emission metrics.
problem Manual extraction of corporate GHG emission metrics is labor-intensive and error-prone.
method CAI uses LLMs to automate extraction and validation of metrics from corporate disclosures.
result CAI improves data collection efficiency and accuracy by automating the process.
Paper presents first model extraction attack against DRL models.
problem Stealing DRL models from interaction with environment.
method Imitation learning approach to recover DRL models from predicted actions.
result Successfully recovers DRL models with high fidelity and accuracy.
FIRE extracts interpretable rules from tree ensembles.
problem Building sparse and interpretable rule sets from tree ensembles.
method Optimization-based framework with fusion regularization and sparsity-inducing penalties.
result FIRE outperforms state-of-the-art rule ensemble algorithms.
Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge. In particular, one may infer from them relations between scientific concepts, such as synonyms and hyponyms. Artificial neural networks have been recently explored for relation extraction. In this work, we continue…
Extracting relevant information from medical conversations and providing it to doctors and patients might help in addressing doctor burnout and patient forgetfulness. In this paper, we focus on extracting the Medication Regimen (dosage and frequency for medications) discussed in a medical conversation. We frame the pro…
Relation extraction aims to extract relational facts from sentences. Previous models mainly rely on manually labeled datasets, seed instances or human-crafted patterns, and distant supervision. However, the human annotation is expensive, while human-crafted patterns suffer from semantic drift and distant supervision sa…
We propose a novel perspective to understand deep neural networks in an interpretable disentanglement form. For each semantic class, we extract a class-specific functional subnetwork from the original full model, with compressed structure while maintaining comparable prediction performance. The structure representation…
The ability to interpret machine learning models has become increasingly important now that machine learning is used to inform consequential decisions. We propose an approach called model extraction for interpreting complex, blackbox models. Our approach approximates the complex model using a much more interpretable mo…
Machine learning models trained on confidential datasets are increasingly being deployed for profit. Machine Learning as a Service (MLaaS) has made such models easily accessible to end-users. Prior work has developed model extraction attacks, in which an adversary extracts an approximation of MLaaS models by making bla…
AverageTime uses simple averaging to enhance long-term time series forecasting.
problem Long-term time series forecasting with improved intra-sequence and cross-channel dependencies.
method Proposes AverageTime, a simple, efficient, and scalable forecasting model that reframes channel extraction as a stackable architecture.
result AverageTime surpasses state-of-the-art models in forecasting performance with near-linear complexity.
Deep neural networks are a powerful tool for feature learning and extraction given their ability to model high-level abstractions in highly complex data. One area worth exploring in feature learning and extraction using deep neural networks is efficient neural connectivity formation for faster feature learning and extr…
Patient summarization is essential for clinicians to provide coordinated care and practice effective communication. Automated summarization has the potential to save time, standardize notes, aid clinical decision making, and reduce medical errors. Here we provide an upper bound on extractive summarization of discharge …
The Impact of Annotation Guidelines and Annotated Data on Extracting App Features from App Reviewscs.IR
Annotation guidelines used to guide the annotation of training and evaluation datasets can have a considerable impact on the quality of machine learning models. In this study, we explore the effects of annotation guidelines on the quality of app feature extraction models. As a main result, we propose several changes to…
Requirements elicitation can be very challenging in projects that require deep domain knowledge about the system at hand. As analysts have the full control over the elicitation process, their lack of knowledge about the system under study inhibits them from asking related questions and reduces the accuracy of requireme…
Optimal query allocation improves extractive QA efficiency with LLMs.
problem Efficiency and reliability in extractive question answering with LLMs.
method Learning-to-Defer framework that allocates queries to specialized models.
result Enhanced answer reliability with reduced computational overhead.
Paper proposes a novel model to improve n-ary cross-sentence relation extraction by addressing noisy data and non-consecutive sentences.
problem Noisy labeled data and non-consecutive sentences in n-ary cross-sentence relation extraction.
method Two-level agent reinforcement learning model and hybrid attention mechanism/PCNN approach.
result The model reduces the impact of noisy data and achieves better performance.
Traditional convolutional layers extract features from patches of data by applying a non-linearity on an affine function of the input. We propose a model that enhances this feature extraction process for the case of sequential data, by feeding patches of the data into a recurrent neural network and using the outputs or…
We present a novel framework to deal with relation extraction tasks in cases where there is complete lack of supervision, either in the form of gold annotations, or relations from a knowledge base. Our approach leverages syntactic parsing and pre-trained word embeddings to extract few but precise relations,which are th…
Pitch or fundamental frequency (f0) extraction is a fundamental problem studied extensively for its potential applications in speech and clinical applications. In literature, explicit mode specific (modal speech or singing voice or emotional/ expressive speech or noisy speech) signal processing and deep learning f0 ext…
Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on …
New method uses LLMs to extract financial insights from Q&A sections of reports.
problem Scalability and accuracy issues in extracting valuable insights from financial report Q&A sections.
method Combines retrieval-augmented generation technique with metadata.
result Empirically demonstrates superior performance of the proposed method.
Recently, machine learning (ML) has introduced advanced solutions to many domains. Since ML models provide business advantage to model owners, protecting intellectual property of ML models has emerged as an important consideration. Confidentiality of ML models can be protected by exposing them to clients only via predi…
From Arbitrage Removal to Density Extraction: A Model-Free Framework for Short-Dated Optionsq-fin.CP
A model-free framework extracts risk-neutral densities from short-dated options.
problem Arbitrage and bid-ask spread issues in short-dated options.
method Develops ARIES for filtering static arbitrage and SEDEx for density extraction.
result Robust density extraction across various market conditions and volatility smiles construction.
New method compares feature importance and rule extraction for text data interpretability.
problem Unexpected differences in explanations from feature importance and rule extraction methods.
method Proposes a new approach to compare explanations from different methods.
result Different methods can lead to unexpected explanations, even for simple models.
A price-maker company extracts an exhaustible commodity from a reservoir, and sells it instantaneously in the spot market. In absence of any actions of the company, the commodity's spot price evolves either as a drifted Brownian motion or as an Ornstein-Uhlenbeck process. While extracting, the company affects the marke…