This paper introduces CENIE to quantify environment novelty for better UED.
problem Challenges in measuring environment novelty for effective UED.
method CENIE framework using state-action space coverage and Gaussian Mixture Models.
result CENIE improves UED performance across multiple benchmarks.
This paper focuses on the valuation and hedging of gas storage facilities, using a spot-based valuation framework coupled with a financial hedging strategy implemented with futures contracts. The first novelty consist in proposing a model that unifies the dynamics of the futures curve and the spot price, which accounts…
New methods control false discoveries near the boundary in conformal novelty detection.
problem Over-optimistic assessments near the rejection threshold in conformal novelty detection.
method Support line (SL) correction and alternative procedures to control boundary false discovery rate (bFDR).
result New procedures control the boundary false discovery rate (bFDR) in the conformal setting.
This report provides an in-depth overview over the implications and novelty Generalized Variational Inference (GVI) (Knoblauch et al., 2019) brings to Deep Gaussian Processes (DGPs) (Damianou & Lawrence, 2013). Specifically, robustness to model misspecification as well as principled alternatives for uncertainty quantif…
Proposes a novel method for detecting novelty in multi-modal data.
problem Challenges in detecting novelty in high-dimensional, multi-modal data.
method Orthogonalized latent space for disentangling features and defining novelty score.
result Proposed method outperforms state-of-the-art algorithms in novelty detection.
This paper introduces the MCML approach for empirically studying the learnability of relational properties that can be expressed in the well-known software design language Alloy. A key novelty of MCML is quantification of the performance of and semantic differences among trained machine learning (ML) models, specifical…
Deep learning detects novel changes in time series data.
problem Detecting novel changes in time series with unknown probability structures.
method Causally extracts an innovations sequence for novelty detection.
result Minimax optimality established for the novelty detection method.
Novelty detection is the unsupervised problem of identifying anomalies in test data which significantly differ from the training set. Novelty detection is one of the classic challenges in Machine Learning and a core component of several research areas such as fraud detection, intrusion detection, medical diagnosis, dat…
This paper connects RND, deep ensembles, and Bayesian inference, providing a unified theoretical perspective.
problem Uncertainty quantification in deep learning models.
method Analysis of Random Network Distillation (RND) within the neural tangent kernel framework.
result The uncertainty signal from RND is equivalent to the predictive variance of a deep ensemble and can be made to mirror the centered posterior predictive distribution of Bayesian inference.
Since datasets with annotation for novelty at the document and/or word level are not easily available, we present a simulation framework that allows us to create different textual datasets in which we control the way novelty occurs. We also present a benchmark of existing methods for novelty detection in textual data s…
Generatability in metric spaces studied with novel novelty parameters.
problem Understanding generatability in metric spaces with asymmetric novelty parameters.
method Introducing (ε,ε′)-closure dimension to characterize uniform and non-uniform generatability. result Generatability is stable across novelty scales in doubling spaces but can be highly scale-sensitive in general metric spaces.
As more and more people shift their movie watching online, competition between movie viewing websites are getting more and more intense. Therefore, it has become incredibly important to accurately predict a given user's watching list to maximize the chances of keeping the user on the platform. Recent studies have sugge…
Gaussian Process Regression improves damage assessment in structural health monitoring.
problem Inaccurate damage quantification in varying conditions and environments.
method Gaussian Process Regression models with Damage Indices for probabilistic damage assessment.
result The method provides probability-based decision-making for structural health monitoring.
This work introduces a novel method to evaluate generative model novelty.
problem Evaluating the novelty of generative models compared to a reference model.
method Spectral approach to differential clustering and Kernel-based Entropic Novelty (KEN) score.
result The KEN score effectively detects novel modes and compares generative models.
This paper reviews information theory in open-world machine learning.
problem Lack of a unified theoretical foundation for open-world machine learning.
method Synthesis of information theoretic approaches.
result Established a pathway toward provable and trustworthy open world intelligence.
In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. Here we propose novelty detection methods based on training variational autoencoders…
CSI detects novelty by contrasting shifted instances, outperforming existing methods.
problem Detecting samples from outside the training distribution.
method Contrastive learning with distributionally shifted augmentations.
result CSI outperforms existing methods in various novelty detection scenarios.
BERT learns claim descriptions to identify patent novelty.
problem Identifying novel patent claims among existing documents.
method Training BERT on concatenated claims and descriptions, scoring BERT's output.
result BERT identifies relevant X documents for patent novelty.
Novelty search in low-dimensional space improves sample efficiency in exploration tasks.
problem Efficient exploration in complex environments with sparse rewards.
method Combines model-based and model-free objectives to learn a low-dimensional representation. Uses intrinsic novelty rewards based on nearest neighbor distances in this space.
result Our approach achieves more sample-efficient exploration compared to strong baselines on various tasks.
News novelty predicts negative stock market returns.
problem Negative stock market returns due to increased news novelty.
method Quantified news novelty using entropy measure from recurrent neural network applied to a large news corpus.
result Entropy exposure carries a negative risk premium, indicating that assets positively correlated with entropy hedge aggregate news risk.
This paper presents a computational model for conceptual shifts, based on a novelty metric applied to a vector representation generated through deep learning. This model is integrated into a co-creative design system, which enables a partnership between an AI agent and a human designer interacting through a sketching c…
Paper proposes AdaDetect for FDR-controlled novelty detection.
problem Semi-supervised novelty detection with probabilistic classification.
method Data-adaptive learning of transformation to control FDR.
result Control of false discovery rate on detected novelties.
Flow-based deep generative models learn data distributions by transforming a simple base distribution into a complex distribution via a set of invertible transformations. Due to the invertibility, such models can score unseen data samples by computing their exact likelihood under the learned distribution. This makes fl…
Proposes a context-aware approach to deep autoencoder novelty detection.
problem Challenges of static distribution in novelty detection.
method Semi-supervised network architecture with auxiliary labels for contextual information.
result Single model achieves performance of individually trained models on various contexts.
Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection, appropriate statistical models for point pattern data have not received much attention. This paper proposes the modelling of point pattern…
Proposes UICR to improve novelty in recommendation systems without sacrificing relevance.
problem Balancing relevance and novelty in recommendation systems is challenging, especially for long-tail items.
method Introduces uncertainty modeling in the matching stage and multi-task modeling of model and index uncertainty.
result Improves novelty without sacrificing relevance, as shown by experimental results and online A/B tests.
Study on robustness of learning-based novelty detection methods under adversarial attacks.
problem Understanding how learning-based novelty detection methods perform under adversarial perturbations.
method Formulated an oracle attack setup and evaluated vulnerability using black-box adversarial algorithms.
result Adversarial perturbations can significantly increase FDR while maintaining high detection power.
OCmst detects anomalies using CNN features and MSTs.
problem Novelty detection in data with no outliers.
method Uses CNN for feature extraction and MSTs for graph-based modeling.
result Achieved state-of-the-art results on CIFAR10 dataset.
The paper develops methods for novelty detection on path space using signature-based statistics.
problem Novelty detection on path space as a hypothesis testing problem.
method Signature-based test statistics, transportation-cost inequalities, CVaR, one-class SVM algorithms.
result Established lower bounds on type-II error and general power bounds. A new method for student-initiated action advice using novelty detection.
problem Exploration and sample inefficiency in RL, especially with teacher absence.
method Random Network Distillation (RND) to measure advice novelty, updates only for advised states.
result Significant performance improvement over state-of-the-art methods, especially in challenging scenarios.
Decentralized detection avoids sharing data, controls false discoveries.
problem Global false discovery rate control in decentralized novelty detection.
method Quantized surrogate models for low-precision sharing, preserving exchangeability.
result Quantized composite scores maintain competitive statistical power with reduced communication.
The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection. This article provides an experimental comparison of the novelty detection problem applied to discrete sequences. The objective of this study is to …
DCAE learns compact latent representations for one-class novelty detection.
problem Learning compact latent representations for one-class novelty detection.
method DCAE learns compact and collapse-free latent representations through internal discriminative layers of GANs, reconstructing in-class data finely and exclusively.
result DCAE achieves state-of-the-art performance on novelty and adversarial example detection.
In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion fea…
RECODE uses clustering and embedding to track state visitation counts in RL.
problem Efficient novelty-based exploration in nonstationary RL environments.
method Non-parametric clustering, online density estimation, inverse dynamics loss.
result RECODE achieves state-of-the-art performance in challenging RL tasks.
AutoSciDACT detects scientific anomalies in noisy data.
problem Detecting anomalies in large, noisy scientific datasets.
method Contrastive pre-training for low-dimensional data representations, two-sample test using NPLM.
result Strong sensitivity to small anomalies across various scientific domains.
One-Shot Neural architecture search (NAS) attracts broad attention recently due to its capacity to reduce the computational hours through weight sharing. However, extensive experiments on several recent works show that there is no positive correlation between the validation accuracy with inherited weights from the supe…
Combines Gaussian processes and polynomial chaos for stochastic control.
problem Uncertainties in dynamic models lead to performance issues in predictive control.
method Combines Gaussian processes with polynomial chaos expansions to estimate probability distributions of nonlinear functions.
result Demonstrates accurate approximation and closed-loop performance in stochastic nonlinear model predictive control.
BEACON optimizes discovery by efficiently finding novel behaviors.
problem Discovering diverse system behaviors without a scalar objective.
method Bayesian optimization inspired strategy using multi-output Gaussian processes.
result BEACON discovers broader sets of distinct behaviors than competing methods.
Paper tackles novelty detection in text classification.
problem Traditional text classification assumes known classes in testing, but often encounters unexpected instances.
method Converts problem to pair-wise matching, uses CNN with embedding matrices.
result Proposed method outperforms state-of-the-art baselines.
SAGE improves memory efficiency by selectively adding, merging, or ignoring new facts.
problem Efficiently managing new facts in agentic LLMs to avoid costly write-time reasoning.
method SAGE uses a von Mises-Fisher-based density estimator to score and route candidate facts.
result SAGE achieves the best average token-F1 on LoCoMo and reduces add-phase API cost by 3.4x on GPT-4o-mini.
New deep probabilistic model handles missing data in time series forecasting.
problem Handling missing data in time series forecasting.
method Combination of deep learning and probabilistic methods.
result Advantage in forecasting and novelty detection with missing data.
Robust VAE detects anomalies in corrupted data.
problem Detect anomalies in data with high corruption.
method Robust Variational Autoencoder (VAE) with four modifications.
result Establishes robustness to outliers and suitability to low-rank modeling.
Plan2Explore learns new tasks efficiently through self-supervised planning.
problem Challenges in reinforcement learning, especially task-specific learning and sample efficiency.
method Self-supervised exploration and fast adaptation to new tasks through efficient planning.
result Plan2Explore outperforms prior methods in learning new tasks without supervision.
Novel loss function improves few-shot classification performance.
problem Few-shot classification struggles with embedding space for new classes.
method Proposes self-compacting softmax loss (SSL) to prevent embedding space fullness.
result SSL leads to significant improvement in state-of-the-art performance.
(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de S…
Generative Kernel PCA explores latent spaces for data interpretation and novelty detection.
problem Exploring latent spaces of datasets for better data interpretation.
method Generative Kernel PCA using hidden and visible units similar to Restricted Boltzmann Machines.
result Gradually moving in the latent space allows for interpretation of components and detection of novel patterns.
HistNetQ improves quantification tasks by optimizing loss functions and eliminating label requirements.
problem Quantification of class prevalence in bags of examples.
method Permutation-invariant Histograms and deep neural networks.
result HistNetQ outperforms other quantification methods and optimizes custom loss functions.