DeepAPT uses neural networks to accurately attribute APTs to nation-states.
problem Attributing advanced persistent threats (APT) to specific nation-states is challenging due to multiple cyber units and evasion techniques.
method Used deep neural networks (DNN) trained on sandbox reports to learn feature abstractions of APTs.
result Achieved an accuracy rate of 94.6% on a dataset of 1,000 Chinese and Russian APTs.
Deep neural networks classify nation-state malware with high accuracy.
problem Classifying and attributing advanced persistent threats (APT) from nation-states.
method Used deep neural networks to analyze malware behavior in a sandbox, learning high-level feature abstractions.
result Achieved 98.6% accuracy in classifying 1000 Chinese and Russian APTs.
We generalize the Arbitrage Pricing Theory (APT) to include the contribution of virtual arbitrage opportunities. We model the arbitrage return by a stochastic process. The latter is incorporated in the APT framework to calculate the correction to the APT due to the virtual arbitrage opportunities. The resulting relatio…
Study on time-varying APT validity in Japanese stock market.
problem Validity of Arbitrage Pricing Theory (APT) in Japanese stock market over time.
method Rolling window method applied to Fama and MacBeth's two-step regression and Kamstra and Shi's generalized GRS test.
result APT validity is unstable over time in Japanese stock market, influenced by monetary policy and business cycle.
Deep learning detects APT attacks with high accuracy and low false positives.
problem Detecting Advanced Persistent Threat (APT) attacks in network traffic.
method Used C5.0 decision tree, Bayesian network, and 6-layer deep neural network models on NSL-KDD dataset.
result Deep learning model achieved 98.85% accuracy and 1.13 false positive rate.
APT improves likelihood-free inference by dynamically transforming posterior estimates.
problem Performing Bayesian inference on simulators with intractable likelihoods.
method Automatic posterior transformation (APT) using neural network-based density estimators.
result APT is more flexible, scalable, and efficient than previous methods.
APT-Gen generates tasks to help RL learn in hard problems.
problem Learning in hard exploration problems.
method APT-Gen uses a task generator to create tasks from a parameterized space, balancing performance and similarity to target tasks.
result APT-Gen outperforms baselines in grid world and robotic manipulation tasks.
We contrast Arbitrage Pricing Theory (APT), the theoretical basis for the development of financial instruments, with a dynamical picture of an interacting market, in a simple setting. The proliferation of financial instruments apparently provides more means for risk diversification, making the market more efficient and…
Paper proposes nested MLMC for SNPE with intractable likelihoods.
problem Estimating posterior distributions from intractable likelihoods.
method Nested MLMC for loss function and gradients, with convergence results.
result Effective methods for approximating complex multimodal posteriors.
Study uses APT and QR to identify risk factors affecting crude oil returns.
problem Determining the risk factors impacting crude oil returns.
method Employed Arbitrage Pricing Theory and Quantile Regression.
result Identified key risk factors: industrial production, inflation, energy prices, yield curve shape, and economic policy uncertainty.
We price financial models using optimization and probability theory.
problem Financial model pricing under risk-averse investors.
method Infinite dimensional optimization, probabilistic and functional analytic tools.
result Existence of optimal strategies and convergence of reservation prices.
Two proofs of Kalman Theorem using flows of vector fields.
problem Classical result of Control Theory (Kalman Theorem).
method Two proofs using flows of vector fields.
result New criteria for local controllability of non-linear systems.
This paper detects multi-stage Feint Attacks using Bi-RNN and few-shot learning.
problem Detecting multi-stage Feint Attacks due to lack of professional datasets and semantic relationships.
method Fuzzy clustering for attack chain mining, few-shot deep learning, Bi-RNN for feature extraction.
result Accurately detected Feint Attacks using Bi-RNN and few-shot learning.
Study proposes local effective dimension to measure model capacity and generalization error.
problem Capturing the generalization power of machine learning models.
method Proposes local effective dimension as a capacity measure.
result Local effective dimension bounds the generalization error and correlates well with it.
Investigates methods to regularize quantile regression for accurate predictions.
problem Improving accuracy and fairness in quantile regression predictions.
method Various regularization techniques including expected pinball loss, monotonicity constraints, and rate constraints.
result Deep lattice networks can maintain non-crossing quantiles and improve calibration and fairness.
This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation. The semantic attribute modulation includes various document attributes, such as titles, authors, and document categories. We consider two types of attributes, (title attributes and category attributes), and a flexible a…
AttGAN edits facial attributes by changing only what you want, preserving details.
problem Facial attribute editing with preservation of details.
method Encoder-decoder architecture with attribute classification and reconstruction learning.
result Outperforms state-of-the-arts on realistic attribute editing with preserved details.
MTRGL learns temporal correlations from multi-modal data for improved pair trading.
problem Discerning temporal correlations among financial entities.
method Combines time series data and discrete features into a temporal graph, using a memory-based temporal graph neural network.
result MTRGL outperforms traditional methods in temporal graph link prediction and pair trading.
Proposes a Taylor framework to unify and analyze attribution methods.
problem Lack of a unified guideline for feature contribution assignment in machine learning models.
method Introduces a Taylor attribution framework to model the attribution problem and reformulates fourteen mainstream methods.
result Empirically validates the Taylor reformulations and reveals a positive correlation between performance and principles followed.
Unified framework for analyzing machine learning model attributions.
problem Lack of a general and theoretical framework for understanding attribution methods.
method Proposes a Taylor attribution framework to unify and analyze seven mainstream attribution methods.
result Established three principles for good attribution and empirically validated the Taylor reformulations.
Improved deep learning models using new attribution priors and expected gradients.
problem Improving interpretability and performance of deep learning models.
method Introducing new attribution priors and expected gradients method that satisfies interpretability axioms.
result Improves model performance across various real-world tasks.
Wavelet Attribution Method (WAM) improves feature attribution for deep models.
problem Inability of pixel-based heatmaps to capture data structure and variability in feature attribution.
method Wavelet domain for feature attribution, leveraging spatial and scale-localized properties of wavelet coefficients.
result WAM provides quantitatively superior explanations across audio, image, and volume modalities.
Paper tackles attribute pattern learning in high-dimensional SLAMs.
problem Learning significant attribute patterns from high-dimensional SLAMs.
method Proposes a penalized likelihood method for selecting attribute patterns.
result Establishes selection consistency in overfitted SLAMs.
Proposes VCLANC for attributed network clustering using node and attribute embeddings.
problem Lack of mutual affinity exploitation between nodes and attributes in graph convolution.
method Dual variational auto-encoders for node and attribute embeddings, Gaussian mixture model priors, mutual distance and clustering assignment hardening losses.
result Demonstrates effectiveness on real-world attributed network datasets.
Generates missing node attributes for better graph-based tasks.
problem Missing or incomplete node attributes degrade graph-based algorithms' performance.
method Deep adversarial learning-based method (NANG) to generate node attributes.
result Generated node attributes improve node classification and link prediction.
A new approach combines attributes and sequences for better item recommendations.
problem Difficulty in leveraging attribute information due to heterogeneity and sparseness.
method Heterogeneous Attribute Recurrent Neural Networks (HA-RNN) that incorporates heterogeneous attributes and captures sequential dependencies.
result Significant improvements over state-of-the-art models in item recommendation.
Proposes a method to predict node attributes using network topology.
problem Predicting node attributes in graphs for various applications.
method Creates a feature map using all attributes of neighbors to predict attributes values for a node.
result Significantly improves prediction accuracy compared to baseline approaches.
Investors seek to attribute performance to various features using Shapley value method.
problem Attributing performance to different features in an investment process.
method Use Shapley value method for attribution, either exactly or approximately.
result Shapley value method provides a preferred attribution approach.
Unified analysis of removal-based feature attributions robustness.
problem Robustness of removal-based feature attributions is not well understood.
method Theoretical analysis and upper bounds derivation for removal-based feature attributions under input and model perturbations.
result Upper bounds for the difference between intact and perturbed attributions derived under various perturbation settings.
A new method for disentangled latent spaces in VAEs that can manipulate attributes.
problem Disentangled representation of attributes in latent spaces of VAEs.
method Attribute-based regularization loss to enforce monotonic relationships between attributes and latent codes.
result Manipulation of attributes in latent spaces post-training.
VarNet learns and manipulates high-level attributes from inputs.
problem Manipulating high-level attributes of inputs.
method Generative model that learns attributes from data and can handle predefined attributes.
result VarNet can learn and manipulate relevant attributes from datasets.
AVA combines feature attribution methods for better model explanations.
problem Improving feature attribution methods for machine learning models.
method AVA: Aggregate Valuation of Antecedents, fusing antecedent event influence and value attribution.
result AVA provides better local and global model explanations.
Develops framework to evaluate feature attribution methods.
problem Lack of ground truth for evaluating feature attribution methods.
method Proposes a framework including a dataset and metrics.
result Certain methods produce false positive explanations.
Paper tackles embedding attributed sequences in unsupervised learning.
problem Mining tasks over attributed sequences with dependencies between sequences and attributes.
method Proposes a deep multimodal learning framework, NAS, for unsupervised learning of attributed sequences.
result NAS produces task-independent embeddings for various mining tasks on real-world datasets.
We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that satisfies the given attributes. To address this problem, we condition the …
AnomalyDAE detects anomalies in networks by learning cross-modality interactions.
problem Detecting anomalies in attributed networks where structure and attributes interact.
method Dual autoencoder framework with attention mechanism for joint learning of structure and attribute embeddings.
result AnomalyDAE effectively detects anomalies by reconstructing node attributes and structures.
New framework for attributing online marketing touchpoints.
problem Fine-grained attribution of individual touchpoints' effects.
method Graphical point process framework for studying conversion effects.
result Proposed methods allocate proper credit to touchpoints for each customer's path.
Adapts IG for better feature attributions and robustness.
problem Reliability concerns in feature attributions for deep learning models.
method Adaptation of path-based feature attribution to Riemannian geometry of data manifolds.
result IG along geodesics generates more intuitive and robust explanations.
Study reduces dataset size by 3 attributes to 73% classification accuracy.
problem Reducing dataset size for gender classification in TSI Life Satisfaction Survey.
method Attribute selection using Chi2 algorithm, followed by classification tests with Naive Bayes and Random Forest.
result Best classification rate achieved with 3 attributes selected by Chi2 algorithm.
New method separates graph structure from node attributes to recover lost signal.
problem Standard representation learning on attributed graphs merges incompatible metric spaces, leading to geometrically flawed alignment.
method Custom variational autoencoder that separates manifold learning from structural alignment.
result Transforms geometric conflict into interpretable structural descriptor, uncovering connectivity patterns and anomalies.
New method builds complex networks from attribute interactions without normalization.
problem Improving high-level classification algorithms by capturing hidden attribute interactions.
method Proposes a new complex network building methodology based on attribute-attribute interactions, avoiding normalization.
result Demonstrates improved performance in high-level classification techniques.
Secure methods learn fair models without revealing sensitive attributes.
problem Training fair machine learning models without exposing sensitive data.
method Secure multi-party computation to encrypt sensitive attributes.
result Outcome-based fair models can be learned, checked, or verified without revealing sensitive attributes.
ASTRID method interprets classifier interactions in datasets.
problem Understanding attribute interactions in classification models.
method ASTRID method for investigating attribute interactions.
result ASTRID reveals associations between attributes for better model interpretability.
RoSHAP stabilizes feature attribution in machine learning models.
problem Stochastic variation in feature attribution measures.
method Modeling feature attribution score distribution and estimating it through bootstrap resampling and kernel density estimation.
result RoSHAP provides stable feature rankings and improves model performance.
Method generates time-series attribution maps with identifiability guarantees.
problem Lack of identifiability guarantees in gradient-based attribution methods.
method Regularized contrastive learning algorithm trained on time-series data.
result Empirically shows robust approximation of zero vs. non-zero entries in the ground-truth attribution map.
Framework learns dynamic graph attributes and links co-evolution.
problem Forecasting change of node attributes and link formation in dynamic graphs.
method CoEvoGNN framework with temporal self-attention and joint optimization.
result Framework outperforms baselines on predicting unseen graph snapshots.
Paper tackles product categorization with structured and unstructured attributes for large-scale eCommerce.
problem Challenges in categorizing products with thousands of classes and millions of products.
method Compares hierarchical and flat models, uses Deep Learning for feature extraction, combines structured and unstructured attributes.
result Flat models perform better in specific cases, and the proposed approach handles faulty attribute names and values.
A new framework quantifies how model explanations influence each other.
problem Understanding how different model explanations interact and influence each other.
method Introducing the metagame, a conceptual framework for measuring second-order interaction effects of model explanations using Shapley values.
result Meta-attributions provide directional insights into how feature interactions influence model explanations.