Two novel approaches improve prediction with Clusterwise Linear Regression.
problem Predicting cluster labels for unseen test points in CLR.
method Two novel approaches: Predictive CLR and Constrained CLR.
result Both approaches significantly improve over known CLR-based regression methods.
Paper tackles efficient exploration of unseen graph-structured environments.
problem Efficient exploration of unseen graph-structured environments.
method Learning to explore framework using reinforcement learning and graph-structured memory.
result Approach outperforms hand-engineered methods in software testing and real-world applications.
Develops robust learning methods for datasets with sub-populations.
problem Robust performance and generalization to unseen testing populations in datasets with sub-populations.
method Min-max-regret (MMR) formulation for distribution-free robust hierarchical model.
result Empirical MMR enjoys regret guarantees on training and unseen testing populations.
Enhances supervised visualization for unseen data using autoencoders and random forest.
problem Lack of generalization to unseen test sets in supervised dimensionality reduction.
method Combines autoencoder and random forest proximities for out-of-sample extension.
result 40% reduction in training time with 10% of training data, achieving consistent quality.
In this paper we consider a version of the zero-shot learning problem where seen class source and target domain data are provided. The goal during test-time is to accurately predict the class label of an unseen target domain instance based on revealed source domain side information (\eg attributes) for unseen classes. …
MADOD meta-learns invariant features for OOD detection across unseen domains.
problem Simultaneous covariate and semantic shifts in real-world machine learning applications.
method Meta-learning and G-invariance to learn robust, domain-invariant features.
result Superior performance in semantic OOD detection across unseen domains.
Study MAML's generalization in varying tasks, proving bounds on error.
problem Bounding MAML's generalization error across tasks.
method Characterizes MAML's generalization error from two perspectives: recurring and unseen tasks.
result MAML's generalization error depends on the number of tasks and samples per task.
New Siamese Capsule Networks improve face verification in limited data settings.
problem Few-shot face verification with limited labeled data.
method Siamese Capsule Networks with contrastive loss and normalized pose features.
result Siamese Capsule Networks outperform baselines in few-shot face verification.
A new ZSL algorithm uses shared sparse representations for unseen classes.
problem Classifying images from unseen classes using only semantic information.
method Coupled dictionary learning to represent visual and semantic features in an intermediate space.
result The proposed method outperforms state-of-the-art ZSL algorithms on benchmark datasets.
Paper develops a deep neural network for open set incremental learning of new authors.
problem Classifying unseen examples from previously unseen classes.
method Deep neural network clustering and retraining for new classes.
result Incremental learning model that continuously learns new classes.
Meta-learning framework improves zero-shot learning for unseen classes.
problem Handling unseen classes with strong bias towards seen classes.
method Meta-learning with Wasserstein GAN to handle class bias and unseen samples.
result Significant improvements in ZSL and GZSL settings.
A new ensemble method improves kNN performance by extending the neighborhood rule.
problem Traditional kNN's limitations when test points are outside the spherical region and ensemble's high errors.
method Determines neighbors in k steps, using bootstrap samples and optimal models selection.
result The proposed ensemble method outperforms state-of-the-art methods on 17 benchmark datasets.
The paper shows why AI safety doesn't generalize across tasks.
problem AI safety fails to generalize across unseen tasks.
method Theoretical analysis of linear-quadratic control with H∞-robustness, empirical demonstrations in simulated quadcopter navigation and CRM. result The mapping from task specification to an optimal controller has a higher Lipschitz constant with safety requirements than without, indicating inherent complexity of safety.
Meta-dropout learns to perturb training examples for better generalization.
problem Achieving good generalization in unseen test examples.
method Meta-learning a noise generator to perturb latent features of training examples.
result Meta-dropout significantly improves generalization performance on few-shot classification datasets.
This research uses activation entropy to improve speech recognition under unseen data conditions.
problem Performance degradation in speech recognition systems under unseen data conditions.
method Estimates network activation entropy to measure data variability and select data for unsupervised model adaptation.
result Improves speech recognition performance under unseen data conditions.
New algorithms improve multi-task learning across different environments.
problem Learning across multiple tasks and adapting to unseen environments.
method Learning common representations, dynamic feature weighting via attention mechanism.
result Significant performance improvement on new environments, 1.5x faster.
This paper studies how gradient descent in control systems can perform well on unseen data.
problem The extent of a learned controller's ability to extrapolate to unseen initial states.
method Theoretical study of policy gradient in Linear Quadratic Regulator (LQR) problems, focusing on the role of exploration.
result The performance of a learned controller on unseen initial states depends on the degree of exploration induced by the system.
D2V learns domain-specific embeddings for domain generalization.
problem Learning decision functions across multiple related domains with limited labeled data.
method Proposes a neural network architecture, Domain2Vec (D2V), that learns domain-specific embeddings and uses them for generalization.
result D2V outperforms other algorithms in domain generalization tasks for image classification.
The paper proposes a method to estimate synthesis flow quality across different technologies and designs.
problem Challenges in developing high-quality synthesis flows for ICs and SoCs.
method Training a Long Short-Term Memory (LSTM) based RNN regressor to predict Quality-of-Result (QoR) for unseen synthesis flows.
result The approach achieves over 98% accuracy in predicting QoRs within one technology and over 96.3% accuracy across different technologies and designs.
Causal models offer stronger privacy guarantees and better generalization than associational models in machine learning.
problem Privacy attacks on machine learning models, especially membership inference attacks.
method Demonstrated the benefit of causal learning in machine learning models, showing better generalization and stronger privacy guarantees.
result Causal models provide stronger differential privacy guarantees and are more robust to membership inference attacks compared to associational models.
Paper introduces effect-invariance for better policy generalization.
problem Adapting policies to unseen environments efficiently.
method Introduces effect-invariance, a relaxation of full invariance, and develops testing procedures to test e-invariance directly from data.
result Effect-invariance enables zero-shot and few-shot policy generalization without assuming a causal graph.
Novel method improves load estimation in power grids using anomaly and change point detection.
problem Improving load estimation in power grid systems.
method Combining unsupervised anomaly and change point detection methods for automatic filtering.
result Automatic load estimation is accurate with 90% estimates within a 10% error margin.
MIRRAMS framework tackles robust tabular learning under unseen missingness shifts.
problem Challenges in achieving robust predictive performance due to shifts in missingness distribution between training and test inputs.
method Introduces MI robustness conditions and MIRRAMS framework to enforce these conditions without specific missingness assumptions.
result Consistently outperforms existing state-of-the-art baselines and maintains stable performance under diverse missingness conditions.
Self-supervised fine-tuning corrects SR CNNs for unseen models and artifacts.
problem SR CNNs' lack of robustness to unseen image formation models and generation of artifacts.
method Iterative fine-tuning using a data fidelity loss at test time.
result Successfully corrects SR solutions for unseen models and GAN artifacts.
Method infers domain-specific models without domain semantic descriptors.
problem Poor performance of standard supervised learning methods in unseen domains.
method Introduces latent domain vectors and neural networks for optimization.
result Inference of appropriate domain-specific models without semantic descriptors.
Paper develops a new method for open-set and imbalanced classification with valid prediction sets.
problem Tackles open-set and imbalanced classification with new prediction methods.
method Develops a new family of conformal p-values and a selective sample splitting algorithm.
result Valid prediction sets with valid coverage in open-set scenarios and informative predictions under extreme class imbalance.
This paper tackles G-ZSL by learning compositional spaces to classify unseen classes.
problem Classifying unseen classes in a test set.
method Space decomposition method to estimate and fine-tune decision boundaries between source and target classes.
result State-of-the-art performance on multiple G-ZSL benchmarks.
Paper shows regularization improves robustness in domain generalization.
problem Improving robustness in domain generalization.
method Derives novel theoretical analysis to control representation smoothness and proposes a regularization method.
result Regularization improves robustness in domain generalization.
A new method improves robustness in image translation by modeling uncertainty.
problem Performance degradation in image translation models due to lack of robustness to outliers and uncertainty.
method UGAC method based on Uncertainty-aware Generalized Adaptive Cycle Consistency, modeling per-pixel residual with generalized Gaussian distribution.
result Our method exhibits stronger robustness towards unseen perturbations in test data.
Detects data drift and outliers affecting ML model performance over time.
problem Detecting distribution changes between training and deployment datasets for machine learning models.
method Nonparametrically tests model prediction confidence distributions for changes using Change Point Models (CPMs). Also uses nonparametric outlier methods.
result Demonstrates robustness of the method under various levels of drift class contamination.
RTE enables extrapolation to new tasks by learning task transformations.
problem Learning systems struggle to generalize to unseen tasks.
method Relational Task Extrapolator (RTE) learns task transformations to enable extrapolation.
result RTE substantially outperforms existing approaches on extrapolation tasks.
The study uses machine learning to predict cryptocurrency market trends and design profitable trading strategies.
problem Predicting cryptocurrency market trends for profitable trading.
method Applied k-Nearest Neighbours, eXtreme Gradient Boosting, and Random Forest classifiers to detect trends.
result High profit factor of 1.60 for unseen data, showing promising results.
HiDe learns hierarchical control for complex tasks by separating planning and control.
problem Solving long horizon control tasks with generalization to unseen scenarios.
method Functional decomposition of state-action spaces, RL-based planner, modular transfer of policy layers.
result Generalizes across unseen test environments and scales to longer horizons.
Cross-validation estimates model performance on unseen data, not training data.
problem Understanding how cross-validation estimates prediction error and its limitations.
method Analyzing linear models and popular prediction error estimates, introducing nested cross-validation.
result Cross-validation estimates the average prediction error of models fit on other unseen training sets, not the model at hand.
A new inference network learns undirected models from data.
problem Learning undirected models with hidden variables is hard.
method Train a single stochastic inference network from data, using a cost function over queries.
result The network can perform inference tasks like an undirected model without partition function.
Adaptive method for prediction sets under changing data distributions.
problem Forming prediction sets in an online setting with varying data distributions.
method Adaptive conformal inference that re-estimates the distribution shift parameter over time.
result Adaptive method achieves desired coverage frequency over long-time intervals.
A neural network model predicts the critical point of the Ising phase transition.
problem Predicting the critical point of the Ising phase transition using supervised learning.
method Proposed a minimal one-free-parameter neural network model to describe the supervised learning problem for the Ising model.
result Just one free parameter is enough to describe the universal finite-size-scaling function in the network output.
Transformers can predict pseudo-random sequences from LCGs with unseen parameters and moduli.
problem Learning pseudo-random number sequences from linear congruential generators with unknown parameters and moduli.
method Investigated the ability of Transformers to learn LCG sequences with varying complexity and moduli. Analyzed embedding layers and attention patterns.
result Transformers can predict pseudo-random sequences from LCGs with unseen parameters and moduli, up to mexttest=216, using a two-step strategy. Proposes DFDG for robust domain generalization without source domain labels.
problem Robustness of deep learning models in real-world applications where train and test distributions differ.
method Model-agnostic, class-aware alignment of class relationships through saliency maps.
result Competitive performance on time series sensor and image classification datasets.
Transferable Boltzmann Generators learn to sample unseen molecules efficiently.
problem Generating equilibrium samples of molecular systems.
method Boltzmann Generators using normalizing flows to learn transformations.
result Transferable Boltzmann Generators can predict zero-shot Boltzmann distributions for unseen molecules.
Mix and match networks enable image translation without paired data.
problem Image translation between domains or modalities with no direct paired data.
method Multiple encoders and decoders aligned for test-time composition.
result Model outperforms baselines in cross-modal image translation.
Proposes AMS-SFE to improve zero-shot learning by aligning semantic feature spaces.
problem Domain shift problem in zero-shot learning due to disjoint seen and unseen data.
method Expands semantic features using an autoencoder and aligns them with visual feature manifold.
result Remarkable performance improvement over existing methods.
Neural network accuracy improves with denser training samples.
problem Improving neural network accuracy on unseen test samples.
method Bounding empirical training error smoothed across activation regions and using it to discard high-risk test samples.
result Discarding high-risk test samples based on error bounds improves prediction accuracy by up to 20%.
CNNs improve generalization to unseen audio devices with increased width, not depth.
problem CNNs are sensitive to specific audio recording devices in acoustic scene classification.
method Investigated the relationship between over-parameterization and generalization in CNNs for audio classification.
result Increasing width improves generalization to unseen devices without increasing the number of parameters.
Paper proposes a method to reduce annotation time for 3D object detection.
problem Effort and time required for generating 3D object annotations.
method Combines human supervision with pretrained neural networks for 3D point cloud segmentation and bounding box generation.
result Reduces human annotation time by 30x.
Improves deep RL agents' generalization to unseen environments.
problem Deep RL agents struggle to adapt to new environments.
method Information Bottleneck regularization and annealing-based optimization.
result Agents can generalize to test parameters more than 10 standard deviations away from training.
CM algorithm improves MMI classifications for unseen instances.
problem Improving classification accuracy for unseen instances using MMI criterion.
method Introduces CM algorithm for MMI classifications, combining semantic and Shannon channels for matching.
result Achieves high mutual information (99%) with minimal iterations in low-dimensional feature spaces.
Generative model creates unseen class examples for zero-shot learning.
problem Training and test classes overlap, leading to biased predictions.
method Variational autoencoder with feedback-driven generator and discriminator.
result Model generates and uses unseen class examples to improve classification.