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.
Algorithm finds novel policies for tasks using novelty reward.
problem Finding distinct policies for a given task.
method Creates a second reward function for novelty, updates policies to balance task and novelty rewards.
result Collection of distinct policies for a task.
New method uses reward prediction error for efficient exploration.
problem Efficient exploration in reinforcement learning, especially in complex environments.
method Reward prediction error (RPE) as an intrinsic motivation for exploration, combined with a deep reinforcement learning method (QXplore).
result QXplore outperforms state-novelty methods in diverse tasks, especially when state novelty is not correlated with improved reward.
CRL uses clustering to enhance exploration in RL, improving performance in diverse tasks.
problem Challenges in RL with large state spaces and sparse rewards.
method CRL employs clustering to guide exploration based on novelty and quality in neighboring areas.
result CRL outperforms other methods in various RL tasks.
SEMI uses multisensory incongruity to self-supervise exploration in reinforcement learning.
problem Efficient exploration in reinforcement learning with sparse or missing rewards.
method SEMI incentivizes exploration by maximizing multisensory incongruity, measured in perception and action incongruity.
result SEMI improves sample efficiency and learns skills without external rewards.
Novel methods generate diverse policies in reinforcement learning.
problem Generating diverse policies in reinforcement learning.
method Constrained optimization perspective, introducing new metrics, and novel policy generation methods.
result Improved novelty and performance of generated policies.
This paper improves neural architecture search by focusing on novelty-driven sampling.
problem Lack of positive correlation between validation accuracy and test accuracy in One-Shot NAS.
method Single-path supernet with optimized weights and novelty search for architecture sampling.
result Novelty search method achieves state-of-the-art test error rate on CIFAR-10.
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.
New algorithms improve best-arm identification with varying rewards.
problem Identifying the best arm with varying reward variances in fixed budget.
method Proposed two algorithms: SHVar for known variances, SHAdaVar for unknown variances; uses non-uniform budget allocation.
result Bounding misidentification probabilities for both algorithms.
New algorithm finds optimal policies without knowing reward functions.
problem Reward-agnostic exploration in reinforcement learning.
method Designs an algorithm that explores without reward information, achieving minimax optimality.
result Achieves provable minimax optimality in finding optimal policies for multiple reward functions.
Reinforcement learning algorithms struggle when the reward signal is very sparse. In these cases, naive random exploration methods essentially rely on a random walk to stumble onto a rewarding state. Recent works utilize intrinsic motivation to guide the exploration via generative models, predictive forward models, or …
Rewards are sparse in the real world and most of today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more suitable for learning. In particular, inspired by curious behaviour in animals, obs…
New algorithm for average reward learning with bounded hitting time assumption.
problem Minimizing regret in average reward reinforcement learning with bounded hitting time.
method Optimistic Q-learning with a novel L ‾ \overline{L} L operator for bounded hitting time. result Regret bound of i l d e O ( H 5 S A T ) ilde{O}(H^5 S\sqrt{AT}) i l d e O ( H 5 S A T ) for average reward learning. 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.
We address the problem of Bayesian reinforcement learning using efficient model-based online planning. We propose an optimism-free Bayes-adaptive algorithm to induce deeper and sparser exploration with a theoretical bound on its performance relative to the Bayes optimal policy, with a lower computational complexity. Th…
A method for self-supervised representation learning in partially observable environments.
problem Sparse rewards and stochasticity in partially observable environments.
method World model in latent space for estimating missing information.
result Significant improvement in exploration compared to prior work.
Study compares methods for detecting novelty in textual data streams.
problem Lack of annotated datasets for novelty detection.
method Simulation framework to create controlled datasets and benchmark methods.
result Evaluation of state-of-the-art methods on different types of novelty.
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…
A model predicts user movie preferences based on novelty-seeking traits.
problem Accurately predicting user movie preferences for competitive websites.
method DFNSM model uses demographic, genre, and novelty-seeking data.
result DFNSM outperforms previous models in movie recommendation accuracy.
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.
Framework improves agent's ability to learn from noisy images.
problem Agents tend to focus on distracting regions in unsupervised image-based goal exploration.
method Proposes a novel framework combining absolute Learning Progress with unsupervised image-based goal exploration.
result Agents successfully identify and ignore distracting regions, improving overall performance.
New method uses hindsight to make exploration robust in stochastic environments.
problem Exploration in sparse-reward or reward-free environments, especially in stochastic settings.
method Learn representations of the future that capture unpredictable aspects, using them to predict and reward only the predictable parts of the world.
result Improves exploration in Atari games and Montezuma's Revenge, robust to stochasticity.
Generatability in metric spaces studied with novel novelty parameters.
problem Understanding generatability in metric spaces with asymmetric novelty parameters.
method Introducing ( ε , ε ′ ) (\varepsilon,\varepsilon') ( 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.
Enhances financial market reinforcement learning with new state generation.
problem Improving reinforcement learning models for financial markets.
method Lipschitz extensions and artificial state generation.
result Improved investment strategy through enriched state space.
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.
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…
Deep learning model for AI co-creativity boosts design creativity.
problem Design creativity enhancement through AI collaboration.
method Novelty metric applied to deep learning-generated vector representations for conceptual shifts in a co-creative design system.
result Increasing novelty in AI contributions correlates with higher creative outcomes in design.
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.
SUPE combines unlabeled data with RL to efficiently explore tasks.
problem Efficient exploration in reinforcement learning with sparse rewards.
method Extract low-level skills using VAE, pseudo-label unlabeled data, and use as off-policy data for online RL.
result SUPE outperforms prior methods across 42 long-horizon tasks.
Efficient exploration is an unsolved problem in Reinforcement Learning which is usually addressed by reactively rewarding the agent for fortuitously encountering novel situations. This paper introduces an efficient active exploration algorithm, Model-Based Active eXploration (MAX), which uses an ensemble of forward mod…
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.
Flow-based models detect anomalies in industrial time series data.
problem Novelty detection in industrial time series data.
method Normalizing flows, specifically Masked Autoregressive Flows and Free-form Jacobian of Reversible Dynamics.
result Flow-based models outperform traditional methods in novelty detection of industrial time series data.
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.
Agent learns directed exploration policies to improve performance in hard games.
problem Improving exploration in complex games.
method Episodic memory-based intrinsic reward, self-supervised inverse dynamics, UVFA framework.
result Doubles performance in hard exploration games, achieves non-zero rewards in Pitfall!.
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…
Curious hierarchical reinforcement learning improves learning performance.
problem Combining hierarchical abstraction and curiosity-driven exploration in reinforcement learning.
method Developed a method that combines hierarchical reinforcement learning with curiosity.
result Curiosity can more than double learning performance and success rates.
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 shows how to motivate AI agents like humans through learning from demonstrations.
problem Transfer human-like intrinsic motivation to AI agents in RL.
method Inverse RL approach to learn an exploration bonus from demonstrations.
result AI agents can learn and use complex exploration behaviors from demonstrations.
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.
FICM uses motion features to encourage exploration in DRL environments.
problem Exploiting novelty in environments with moving objects.
method Flow-based intrinsic curiosity module (FICM) leveraging motion features between consecutive frames.
result FICM encourages exploration in environments featuring moving objects.
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- I I \mathrm{II} 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.
Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) are popular risk measures from academic, industrial and regulatory perspectives. The problem of minimizing CVaR is theoretically known to be of Neyman-Pearson type binary solution. We add a constraint on expected return to investigate the Mean-CVaR portfolio sele…