DMLreg uses expert knowledge to improve model performance in high-dimensional settings.
problem Improving model performance in high-dimensional prediction problems.
method Learning a Mahalanobis distance metric from expert comparisons and integrating it into a regularized linear model.
result DMLreg leads to improvements in model performance when expert knowledge is relevant.
Two methods factor out prior knowledge from low-dimensional embeddings.
problem Visualizing data without considering background knowledge.
method JEDI for tSNE and CONFETTI for any embedding.
result Embeddings reveal meaningful structure hidden by prior knowledge.
Paper tackles catastrophic forgetting in incremental learning with improved cosine distance and PEDCC-Loss.
problem Tackles catastrophic forgetting in incremental learning.
method Ensemble method based on cosine distance and PEDCC-Loss.
result Outperforms recent methods in preserving old knowledge while learning new classes.
Paper tackles transferring knowledge for learning distance metrics across different domains.
problem Mitigating label information deficiency in target distance metric learning.
method Develops a flexible HTDML framework that uses knowledge fragments from source domain to help target metric learning.
result Reduces generalization error in the target domain using the proposed transfer strategy.
New Gromov-Wasserstein metric controls rigidity and incorporates prior knowledge.
problem Inflexible Gromov-Wasserstein distance and lack of feature alignment.
method Augmented Gromov-Wasserstein distance with feature alignments and prior knowledge.
result Improved performance in single-cell multi-omic alignment and transfer learning.
Given a geodesic space (E, d), we show that full ordinal knowledge on the metric d-i.e. knowledge of the function D d : (w, x, y, z) → 1 d(w,x)≤d(y,z) , determines uniquely-up to a constant factor-the metric d. For a subspace En of n points of E, converging in Hausdorff distance to E, we construct a met…
New method clusters medical codes using multiple data sources.
problem Challenges in community detection with multiple data sources.
method Combines multiple data sources with prior distance knowledge.
result Yields insightful clustering structure for ICD9 codes.
Method learns relational features for Gaifman models from knowledge bases.
problem Structure learning for Gaifman models.
method Relational tree distances to learn relational features.
result Empirical evaluation shows superiority over classical rule-learning.
Expands HMRL to multi-target problem considering agent distances.
problem Learning state-action rules for multiple targets.
method Hierarchical Modular Reinforcement Learning with AT field and C4.5.
result Improved reinforcement learning for multi-agent scenarios.
New algorithm for clustering categorical data without using distance measures.
problem Challenges in clustering categorical data due to its unordered structure.
method Developed a Matching based clustering algorithm using a similarity matrix and feature importance criteria.
result The algorithm can serve as an alternative to existing clustering methods for categorical data.
New distance metric for neural architecture search reduces search space complexity.
problem Reducing the complexity of neural architecture search.
method Fisher task distance for measuring task similarity and online neural architecture search.
result Reduced search space complexity for task-specific architectures.
Proposes a method to incorporate prior domain knowledge into hierarchical clustering.
problem Hierarchical clustering results depend on similarity measures and algorithm choices.
method Uses ultrametric distance function to encode external ontological information and adds it as a penalty term to the original pairwise distance.
result Popular linkage-based algorithms can faithfully recover the encoded structure.
Proposes a neural network model for embedding knowledge bases and answering questions.
problem Handling uncertainty and conjunction in neural question answering.
method Gaussian attention model for neural memory access and scoring function.
result Demonstrates model's effectiveness on soccer player dataset for path and conjunctive queries.
This work learns a distance function for goal-conditioned RL without prior knowledge.
problem Learning a distance function for goal-conditioned reinforcement learning without prior knowledge.
method Self-supervised approach to learn distance function in terms of actions needed to reach a goal.
result Solves complex tasks in three scenarios without prior domain knowledge.
Improved model learns text and hierarchical relations for commonsense knowledge.
problem Predicting hierarchical relations and non-hierarchical knowledge in text data.
method Jointly learns ordering relations and non-hierarchical knowledge from text data. Exploits partial order structure for long-distance triplet constraints.
result Both free text and augmented training constraints improve model performance over baselines.
Paper proposes a new method for predicting drug interactions using adversarial autoencoders.
problem Predicting drug interactions to prevent adverse events.
method Introduces adversarial autoencoders based on Wasserstein distances and Gumbel-Softmax relaxation to generate high-quality negative samples.
result Significant improvements in link prediction and DDI classification tasks.
TML uses knowledge from related domains to improve metric learning in target domain.
problem Insufficient label information in real-world applications.
method Leveraging knowledge from related domains to improve metric learning in target domain.
result TML can improve metric learning performance in target domain.
Exact inference method for Wasserstein distance with finite-sample coverage.
problem Asymptotic approximation methods for Wasserstein distance lack finite-sample validity.
method Selective Inference inspired approach for exact inference.
result Valid confidence interval for Wasserstein distance with finite-sample coverage.
Valid inference method for DTW distance for abnormal time-series detection.
problem Statistical inference on DTW distance under uncertain conditions.
method Conditional selective inference framework to derive valid p-values.
result First method to provide valid p-values for DTW distance.
This guide explains statistical distances for evaluating generative models.
problem Evaluating the quality of samples from generative models.
method Four statistical distances: SW, C2ST, MMD, FID.
result Different distances can yield varying results on similar data.
Efficiently represents large geodesic distance matrices for MDS analysis.
problem Quadratic growth of geodesic distance matrices for large point sets.
method Sparse biharmonic interpolation to learn a subset of points for efficient approximation.
result 2x faster and 20x less memory usage than current methods, enabling analyses of large point sets.
New algorithms for efficient matrix profile computation using various Euclidean distances.
problem Efficiently computing matrix profile for all-pairs-similarity search on time series.
method Proposed AAMP, ACAMP, and extended algorithms for p-norm distance.
result AAMP and ACAMP algorithms outperform existing methods for specific Euclidean distances.
A graph traversal algorithm for cold-start news recommendation using named entities.
problem Cold-start news recommendation for articles without user-specific information.
method Graph traversal algorithm and novel weighting scheme for named entities over a knowledge graph.
result Our method produces stronger Pearson correlation to human similarity scores than other cold-start methods.
We address noisy Euclidean distances in high dimensions, estimating noise levels and correcting distances.
problem Distorted pairwise Euclidean distances due to heteroskedastic noise.
method Developed a hyperparameter-free approach to jointly estimate noise magnitudes and correct distances.
result Our method provides accurate noise magnitude estimates and corrected distances in high-dimensional settings.
Transportation distances have been used for more than a decade now in machine learning to compare histograms of features. They have one parameter: the ground metric, which can be any metric between the features themselves. As is the case for all parameterized distances, transportation distances can only prove useful in…
Estimates distances between latent points in random geometric graphs.
problem Estimating distances between latent points in random geometric graphs.
method Spectral estimator of pairwise distances.
result Rate of convergence is the same as nonparametric estimation on the sphere, up to a logarithmic factor.
Proposes WDGRL for better domain adaptation.
problem Improving performance on target domain via source domain knowledge.
method WDGRL uses Wasserstein distance to learn domain invariant feature representations.
result WDGRL outperforms state-of-the-art approaches in domain adaptation.
Method recovers particle orientations from cryo-EM projections.
problem Unknown orientations in cryo-EM images.
method Two-step process: estimating distances and recovering orientations.
result Accurate orientation recovery from noisy projections.
Paper tackles NNS under uncertainty with improved algorithms.
problem Efficient nearest neighbor search with noisy distance estimates.
method Combines cover trees and multi-armed bandits for optimal performance.
result Optimal dependence on dataset size and unknown geometry achieved.
Rigidity theorem for discrete metric spaces embedded in Riemannian surfaces.
problem Understanding the rigidity of discrete metric spaces embedded in Riemannian surfaces.
method Proving that certain discrete metric spaces are rigidly embedded in the Euclidean plane or other Riemannian surfaces.
result Riemannian embeddings of certain discrete metric spaces are rigid, meaning they cannot be deformed without changing distances.
SE-KGE embeds spatial data into KGs for better spatial reasoning.
problem Spatially explicit KG embeddings for geographic tasks.
method Location-aware KG embedding model SE-KGE.
result SE-KGE outperforms baselines on DBGeo dataset.
The paper provides a theoretical framework for learning task similarity in multitask learning.
problem Understanding and leveraging task similarities for improving multitask learning performance.
method The paper derives theoretical bounds on multitask learning generalization error, and proposes a new training algorithm to learn task relation coefficients and neural network parameters.
result The proposed algorithm outperforms baselines and reaffirms the benefits of theoretical insight in algorithm design.
A deep clustering method for hyperspectral images improves clustering performance by constraining intra-class distances.
problem Clustering performance degradation in hyperspectral images due to high dimensionality.
method Intra-class distance constrained deep clustering algorithm using auto-encoder network.
result The proposed algorithm outperforms state-of-the-art methods in clustering hyperspectral images.
Hybrid controller combines model-based and policy-based reinforcement learning.
problem Combining model-based and policy-based reinforcement learning for stability and robustness.
method Designs a hybrid controller that interpolates a model-based linear controller and a differentiable policy.
result Proven to maintain stability and universal approximation properties.
Proposes MDSMM for matrix classification with multi-distance.
problem Matrix data with structural information.
method Introduces multi-distance to capture matrix data correlation.
result MDSMM achieves faster learning rate than traditional classifiers.
FedGTEA learns new tasks in federated learning with task embeddings and alignment.
problem Federated class-incremental learning with task-specific knowledge and model uncertainty.
method Cardinality-Agnostic Task Encoder (CATE) for Gaussian task embeddings, 2-Wasserstein distance for inter-task alignment.
result FedGTEA achieves superior classification performance and mitigates forgetting.
Paper tackles continual reinforcement learning challenges with diversity exploration and adversarial self-correction.
problem Challenges in learning new tasks sequentially due to catastrophic forgetting.
method Develops an end-to-end framework (CDAN) combining unsupervised diversity exploration and adversarial self-correction.
result Final result outperforms baseline by 18.35% in NSD and 0.61 in average reward.
Robust Bayesian inference improves model performance on discrete data.
problem Misspecification of discrete-valued models leads to poor inference and prediction.
method Total Variation Distance (TVD) for discrepancy, efficient estimator and inference method.
result Our approach significantly improves predictive performance on various data.
New algorithm combines Cramér distance with function approximation for distributional reinforcement learning.
problem Limited theoretical understanding of practical distributional reinforcement learning methods.
method Adapts Cramér distance to arbitrary vectors, derives new distributional algorithm combining Cramér-based and function approximation.
result First proof of convergence for a distributional algorithm combined with function approximation.
The paper analyzes how well classes are separated in neural network feature space.
problem Understanding class separability in neural network feature space.
method Theoretical analysis of intra-class and inter-class distances in feature space.
result A lower bound for the probability of inter-class distance being greater than intra-class distance as a function of loss value.
MetFA aligns source and target domains for cross-device image classification.
problem Learning discriminative class boundaries across different domains.
method Distance metric guided feature alignment (MetFA) for domain-invariant and discriminative feature extraction.
result MetFA outperforms state-of-the-art methods in cross-device image classification.
For a compact Riemannian manifold with boundary, we want to find the metric structure from knowledge of distances between boundary points. This is called the "boundary rigidity problem". If the boundary is not concave, which means locally not all shortest paths lie entirely in the boundary, then we are able to find the…
Random forest can be adapted for open-set recognition with improved performance.
problem Handling unknown classes in real-world classification tasks.
method Incorporating distance metric learning and distance-based open-set recognition into random forest.
result The proposed method outperforms state-of-the-art open-set recognition methods.
Semantic embeddings improve safety-critical classifier performance.
problem Improving interpretability and error detection in safety-critical neural networks.
method Created embeddings from symbolic domain knowledge, used for misprediction interpretation and error detection, introduced semantic distance for confidence measurement.
result Semantic distance achieves near state-of-the-art performance in a traffic sign classifier, faster than other methods.
New method optimizes black-box functions using generative models and Wasserstein distance.
problem Optimizing black-box functions with stochastic responses in high dimensions.
method Deep generative surrogate models and Wasserstein distance for uncertainty estimation.
result Method outperforms state-of-the-art methods in robustness to function shape and stochasticity.
A new method for unlearning trained models without needing the original data.
problem Lack of access to original training data for privacy-preserving unlearning.
method Uses a surrogate dataset to approximate statistical properties and calibrates noise based on statistical distance.
result Effective unlearning of trained models with strong privacy guarantees, even without access to the original data.
A new method optimizes projection directions for sliced Wasserstein distances.
problem Finding informative projecting directions for sliced Wasserstein distances is computationally expensive.
method Amortized projection optimization to predict directions efficiently.
result Proposed amortized models improve generative modeling performance.
Private method measures nonlinear correlations between data hosted across two entities.
problem Measuring nonlinear correlations between sensitive data hosted across multiple parties while preserving privacy.
method Differentially private estimator of distance correlation.
result First private estimator of nonlinear correlations in a multi-party setup.