A new method for precise user targeting in advertising using hyperbolic manifold learning.
problem Improving user satisfaction in targeted advertising by overcoming skepticism and spam perception.
method Proposes a Multi-Manifold Learning framework to learn hierarchical user and ad representations in the hyperbolic space.
result Demonstrates improved performance in user targeting and prediction accuracy on both public datasets and a large-scale commercial system.
Improves CTR prediction by considering spatial and temporal auxiliary ads.
problem Improving CTR prediction in online advertising systems.
method Deep Spatio-Temporal Neural Networks (DSTNs) for CTR prediction.
result DSTNs outperform state-of-the-art methods in CTR prediction.
New algorithm improves ad targeting for personalized online services.
problem Personalizing online services for improved user experience and revenue.
method Label ranking approach for non-linear, large-scale prediction of user interests.
result The proposed algorithm outperforms existing solutions in rank loss and top-K retrieval.
Enhances model's ability to distinguish target domain by adding a new class.
problem Improving unsupervised domain adaptation models' discriminative power.
method Training model on data from a new class generated by GAN, repositioning current class data.
result Achieves state-of-the-art performance in various unsupervised domain adaptation scenarios.
Interprets feature interactions in ad-click prediction models.
problem Improving interpretability of black-box recommender systems.
method Interprets feature interactions from a source model and encodes them in a target model.
result Interpretations significantly outperform existing recommender models.
Deformed sigma-models with complex structures have zero-curvature representations.
problem Deformation of sigma-models with complex structures.
method B-field proportional to Kähler form, zero-curvature representation.
result Equations of motion admit zero-curvature representations.
Efficient ANN search for sparse embeddings in ads targeting.
problem Efficiently searching near neighbors in sparse data for applications like ads targeting.
method Graph-based ANN algorithms (HNSW, chi-square two-tower model, Sign Cauchy Projections).
result Sparse embeddings and ANN algorithms improve efficiency in EBR applications.
A new method optimizes anomaly scoring from score distribution to improve AD performance.
problem Vulnerability to anomaly contamination and lack of adaptability in existing AD methods.
method Optimizes anomaly scoring function from score distribution perspective, using Overlap loss.
result Overlap loss-based AD models significantly outperform state-of-the-art methods.
STAND-DA improves AD in DA target domains with limited data.
problem Statistical validity of AD after DA with limited data.
method Selective Inference framework for GPU-accelerated p-value computation. result Valid p-values and controlled false positive rate. Minimal surfaces in AdS(4) correspond to elliptic solutions of the cosh-Gordon equation.
problem Minimal surfaces in AdS(4) and their relation to entanglement entropy.
method Inverting Pohlmeyer reduction to construct static minimal surfaces in AdS(4) that correspond to elliptic solutions of the reduced system.
result A two-parameter family of static minimal surfaces in AdS(4) that include helicoids and catenoids as special limits.
Unified framework for various adversarial attacks on deep networks.
problem Vulnerability of deep neural networks to adversarial attacks.
method ADMM (Alternating Direction Method of Multipliers) for generating adversarial examples.
result ADMM-based attacks achieve highest success rates and minimal distortion.
A Q-learning approach optimizes RTB ad campaigns for mobile app installs.
problem Optimizing RTB ad campaigns for mobile app installs with delayed rewards.
method State space based policy trained via Q-learning algorithm to handle delayed install notifications.
result Significant increase in profit and number of efficient campaigns.
Bayesian neural networks predict AD severity from EEG data.
problem Developing low-cost, non-invasive biomarkers for AD diagnosis and progression.
method Bayesian deep neural networks using QEEG markers.
result Bayesian approach provides uncertainty bounds for AD severity prediction.
Wide-AdGraph detects ads and trackers using a graph of resource requests.
problem Detecting and blocking ad trackers to protect user privacy.
method Combining a large-scale graph of resource requests from multiple websites to train a machine learning algorithm.
result High accuracy (96.1% biased, 90.9% unbiased) in detecting ads and trackers.
Researchers create holographic super-embeddings for M5 and M2 branes.
problem No concrete examples of super-embeddings for M5 and M2 branes existed.
method Constructed explicit holographic super-embeddings of probe M5 and M2 branes into their super-AdS backgrounds.
result Explicit holographic super-embeddings of M5 and M2 branes were successfully constructed.
Proposes a method to recover sparse tensors with covariate info.
problem Sparse tensor with high missing entries and many zeros.
method Covariate-assisted Sparse Tensor Completion (COSTCO) using latent components.
result 23% accuracy improvement over baseline in advertisement dataset.
Optimized OCPC strategy improves Taobao's ad traffic allocation efficiency.
problem Inefficient bid matching between advertisers and traffic quality.
method Proposed OCPC strategy to automatically adjust bids for finer matching.
result Substantially better results compared to fixed bid methods in production tests.
New method improves robustness of neural network-based debiasing.
problem Improving robustness of neural network-based debiasing.
method Moment-constrained learning for neural networks.
result Improved performance compared to state-of-the-art benchmarks.
Effective multilingual search with instance-based transfer learning.
problem Search in multilingual setting, especially next-sentence prediction and inverse cloze.
method Instance-based transfer learning, analyzing vocabulary overlaps and transitive overlaps.
result Positive transfer on all 35 target languages and two tasks, even with no vocabulary overlap.
This paper quantifies how hard it is to identify specific data points in machine learning models.
problem Quantifying the difficulty of identifying specific data points in machine learning models.
method Characterizing optimal attacks and privacy defences, deriving impacts of noise and misspecification, and proposing a new covariance attack.
result The Mahalanobis distance explains the hardness of fixed-target membership inference attacks.
DeepMCP improves CTR prediction by learning better feature representations.
problem Data sparsity in CTR prediction models.
method DeepMCP models user-ad, ad-ad, and feature-CTR relationships through three subnets.
result DeepMCP outperforms state-of-the-art models in CTR prediction.
We identify a condition for regularity of optimal transport maps that requires only three derivatives of the cost function, for measures given by densities that are only bounded above and below. This new condition is equivalent to the weak Ma-Trudinger-Wang condition when the cost is C4. Moreover, we only require (n…
This paper analyzes how periodic and soft target updates stabilize linear Q-learning.
problem Theoretical explanation of stabilization mechanisms for linear Q-learning.
method Exact analysis using switched linear system dynamics and the joint spectral radius.
result Periodic and soft target updates can guarantee convergence to the exact projected Q-Bellman solution under specific conditions.
Proves existence of optimal shallow neural networks with ReLU activation.
problem Proving the existence of optimal shallow feedforward networks with ReLU activation.
method Proves existence of global minima in the loss landscape for continuous target functions using shallow feedforward neural networks with ReLU activation.
result Existence of global minima in the loss landscape for shallow feedforward networks with ReLU activation.
Improves unsupervised domain adaptation by mixing source and target domains.
problem Improves unsupervised domain adaptation by mixing source and target domains.
method Enforces training constraints across domains using mixup formulation and feature-level consistency regularizer.
result Significantly improves state-of-the-art performance on image classification and human activity recognition tasks.
Adaptive algorithm for online evaluation of targeted audiences in advertising.
problem Determining the right match between advertising creatives and target audiences.
method Contextual bandit approach to address audience overlap and learn optimal display policies.
result The proposed method is more efficient than traditional split-testing methods.
New method uses limited labeled data and multiple starts to adapt models across domains.
problem Accurate predictions in target domain with few labeled data.
method Fine-tuning from multiple adaptive starts, extending UDA methods.
result Minimax-optimal target performance with limited labeled target data.
The paper tackles the issue of preferential attachment in targeted display advertising by developing domain-adaptation approaches.
problem Skewed distribution of data leads to preferential attachment towards high-budget partners.
method Develops domain-adaptation approaches to predict interested users for low-budget partners.
result Proposed approaches outperform other domain-adaptation methods across different points of campaigns.
GNMT uses latent variables to improve machine translation, especially with missing words.
problem Improving machine translation, especially with missing words.
method GNMT adds a latent variable to an encoder-decoder model to capture sentence semantics.
result GNMT achieves competitive BLEU scores and is superior when source sentences are missing words.
Detects backdoors in trained classifiers without access to training data.
problem Post-training detection of backdoor attacks in DNN image classifiers.
method Purely unsupervised anomaly detection (AD) approach.
result Detects whether a classifier has been backdoor-attacked and infers source and target classes.
Improved conversion prediction for B2B products using collective online activity trails.
problem Limited information from single user activity trails for B2B ad targeting.
method Introduced relevant users concept and used distributed activity representations to build seed lists.
result Improved conversion prediction AUC by 8.8% using collective activity trails.
A new method transfers data between domains using a common space and adaptive functions.
problem Domain transfer learning between different datasets.
method Mapping data to a common space, learning a classifier there, and adapting it for each domain.
result The method outperforms existing transfer learning methods on benchmark datasets.
Develops a new bidding system to maximize advertiser profit.
problem Inaccurate prediction of ad lift-effect due to biased log data.
method Unbiased Lift-based Bidding System that predicts lift-effect from biased log data.
result Demonstrates superior and practical high-performing lift-based bidding strategy.
Generative models create indistinguishable adversarial objects for object detection.
problem Creating unrestricted adversarial examples for object detection.
method Search over latent space of GAN for adversarial objects.
result Generated adversarial objects are indistinguishable from non-adversarial objects and transferable.
TPA-AD detects axle-box bearing anomalies using pseudo anomalies near normal boundaries.
problem Detecting axle-box bearing anomalies with only normal training data.
method Two-stage approach: pseudo anomalies, contrastive learning, KNN.
result Improves anomaly detection separability and sensitivity to degradation.
Paper shows attacks on transfer learning models without target-specific info.
problem Security vulnerability in transfer learning models.
method Brute force attack on pre-trained models without target-specific data.
result Demonstrates effective and efficient attacks on transfer learning models.
Predicts user age and gender on Tumblr using rich content.
problem Challenges in targeting specific demographic groups on Tumblr.
method Graph based and deep learning models, including network embedding, label propagation, CNN, and MLP.
result Significantly improved accuracy for age and gender predictions.
Personalized model improves ADAS-Cog13 cognitive score forecasting.
problem Improving accuracy in predicting cognitive changes in Alzheimer's Disease.
method Meta-Weighted Gaussian Process Experts (pGPE) model for personalized forecasting.
result Meta-weighting of expert models leads to significant improvements in forecasting accuracy.
Hierarchical randomized smoothing improves model robustness for complex data.
problem Certifying robustness on complex data (e.g. images, graphs) is challenging.
method Add random noise to a randomly selected subset of entities in a hierarchical manner.
result Hierarchical randomized smoothing yields stronger robustness guarantees with high accuracy.
New attacks manipulate knowledge graph embeddings by adding or deleting facts.
problem Lack of robustness of KGE to adversarial attacks.
method Data poisoning attack strategies to manipulate knowledge graph embeddings.
result Demonstrated effectiveness and efficiency of proposed attack strategies.
End-to-end learnable Gaussian mixture priors improve diffusion models' exploration and expressiveness.
problem Challenges in diffusion models when priors differ from target distributions.
method End-to-end learnable Gaussian mixture priors (GMPs) with iterative refinement.
result Significant performance improvements across various benchmark problems.
Rewiring edges subtly improves graph neural networks' robustness.
problem Vulnerability of graph neural networks to subtle structural perturbations.
method Proposes a graph rewiring operation and uses reinforcement learning to learn attack strategies.
result Demonstrates the effectiveness of the proposed framework on real-world graphs.
MAGIC uncovers disease heterogeneity across brain scales.
problem Understanding distinct subtypes of brain diseases at different spatial scales.
method Multi-scale Heterogeneity Analysis and Clustering (MAGIC) using semi-supervised clustering.
result Two main subtypes of AD identified with distinct atrophy patterns.
Study optimal bidding strategies for digital ads targeting purchases and health campaigns.
problem Optimizing advertising strategies in digital channels.
method Continuous-time models encoding user behavior and auction mechanisms, semi-explicit formulas for optimal bidding.
result Semi-explicit formulas for optimal value and bidding policy for different types of advertising.
Adversarial attacks can fool algorithmic trading systems.
problem Adversarial perturbations can manipulate algorithmic trading models.
method Real-time adversarial attacks on trading algorithms using universal perturbations.
result Perturbations can fool trading algorithms at unseen data points.
Auxiliary Tuning adapts pre-trained models for novel tasks efficiently.
problem Adapting pre-trained models for new tasks efficiently.
method Supplementing pre-trained model with an auxiliary model that shifts output distribution.
result Achieved similar results to training from scratch with fewer resources.
The paper evaluates different vector space models for text similarity.
problem Measuring semantic text similarity in natural language processing.
method Comparison of TFIDF, topic models, and neural models for patent-to-patent similarity.
result TFIDF performs well for longer, technical texts or finer distinctions.
Gaussian processes with differential privacy protect both inputs and outputs.
problem Previous DP methods only protected model outputs, not inputs.
method Sparse GP with private variational approximation, adjusting covariance for DP noise.
result Accurate models can be produced under strong privacy protection with sufficient data.