Enhances clustering performance by integrating tensor similarity.
problem Noise contamination and imbalance in samples or features hinder accurate clustering.
method Proposes a high-order similarity matrix from tensor similarity, which captures spatial information and complements pairwise similarity.
result The proposed IPS2 method significantly outperforms previous similarity-based methods on real-world datasets.
Estimates set overlap and similarity using random samples.
problem Estimating set overlap and similarity with limited data.
method Binomial model for predicting set overlap, comparing to previous methods.
result Binomial model provides better estimates with small sample sizes.
New method learns local metrics for k-NN classification using sample similarity.
problem Improving k-NN classification accuracy through better distance metrics.
method Local distance metric learning based on sample similarity, using conical combinations of metric weight matrices.
result New metrics yield smaller distances for similar samples and larger distances for dissimilar ones.
We introduce a new discrepancy score between two distributions that gives an indication on their similarity. While much research has been done to determine if two samples come from exactly the same distribution, much less research considered the problem of determining if two finite samples come from similar distributio…
Neural networks auto-denoise similar inputs, enabling new statistical analysis.
problem Estimating similarity of inputs for neural networks.
method Define and quantify similarity from neural network perspective, using parameter variation impact on outputs.
result Estimate sample density and quantify denoising effect without true labels.
Method quantifies relation similarity using entity pair distributions.
problem Measuring similarity between relations in knowledge bases.
method Simple neural network parameterizes conditional probability distributions over entity pairs. Sampling-based approximation for similarity computation.
result Approximation correlates with human judgments and detects redundant relations.
Improved similarity search in embeddings using InfoNCE loss.
problem Improving similarity search in embedding models trained by contrastive learning.
method Introduced a new continuity bound for InfoNCE loss via Gâteaux differentiation, preserving the averaging effect of negative samples.
result Demonstrated that the averaging effect of k negative samples in InfoNCE loss carries over to stabilisation of generalisation error as k grows. SBSS uses similarity to split data for better classifier training.
problem Training better classifiers with realistic performance estimation.
method SBSS uses both input and output space information to split data using similarity functions.
result SBSS outperformed ordinary stratified 10-fold cross-validation in 75% of scenarios.
ICE proposes a new loss function for deep metric learning.
problem Deep metric learning from instance-level matching distribution.
method Instance Cross Entropy (ICE) loss function.
result ICE outperforms existing methods on real-world benchmarks.
A new test assesses text similarity between two groups of documents.
problem Comparing similarity between two groups of documents.
method Neural network-based language models estimate entropy, and a test statistic derived from an estimation-and-inference framework is used.
result The proposed test maintains the nominal Type one error rate while offering greater power compared to existing methods.
In this paper, we study the missing sample recovery problem using methods based on sparse approximation. In this regard, we investigate the algorithms used for solving the inverse problem associated with the restoration of missed samples of image signal. This problem is also known as inpainting in the context of image …
This paper proposes asal, a new GAN based active learning method that generates high entropy samples. Instead of directly annotating the synthetic samples, ASAL searches similar samples from the pool and includes them for training. Hence, the quality of new samples is high and annotations are reliable. To the best of o…
We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling. We propose a new class of smooth graph signals, called approximately bandlimited, which generalizes the bandlimited class and is similar to the globally smooth class. We then propose two recovery s…
New algorithms improve multi-task bandit performance by transferring reward samples.
problem Sequential multi-task bandit problems with adjacent similar tasks.
method Two UCB-based algorithms that transfer reward samples between tasks.
result Transfer of reward samples reduces overall regret compared to no transfer.
Study shows how leveraging hierarchical similarity graphs improves matrix completion in recommender systems.
problem Improving matrix completion in recommender systems using hierarchical similarity graphs.
method Characterizes the optimal sample complexity using hierarchical stochastic block models and low-rank rating matrices.
result Exploiting hierarchical structure of social graphs significantly reduces the number of observed entries needed for accurate matrix completion.
Explicitly or implicitly, most of dimensionality reduction methods need to determine which samples are neighbors and the similarity between the neighbors in the original highdimensional space. The projection matrix is then learned on the assumption that the neighborhood information (e.g., the similarity) is known and f…
This paper investigates the problem of recovering missing samples using methods based on sparse representation adapted especially for image signals. Instead of l2-norm or Mean Square Error (MSE), a new perceptual quality measure is used as the similarity criterion between the original and the reconstructed images. T…
EB-RANSAC uses energy-based model for robust estimation without complex sampling.
problem Robust estimation of parameters in noisy data.
method EB-RANSAC combines RANSAC's sampling scheme with an energy-based model, simplifying the process and reducing hyperparameter requirements.
result EB-RANSAC effectively solves linear regression and maximum likelihood estimation problems.
We introduce TzK (pronounced "task"), a conditional probability flow-based model that exploits attributes (e.g., style, class membership, or other side information) in order to learn tight conditional prior around manifolds of the target observations. The model is trained via approximated ML, and offers efficient appro…
Balls-and-Bins sampling improves DP-SGD privacy and utility.
problem Improving privacy and utility in DP-SGD implementations.
method Introducing Balls-and-Bins sampling as an alternative to shuffling in DP-SGD.
result Balls-and-Bins sampling achieves utility comparable to shuffling while offering better privacy amplification.
This thesis studies domain adaptation under minimal distribution similarity assumptions using moments.
problem Learning from samples with distributions different from training samples.
method Uses minimal similarity assumptions modeled by moments.
result Establishes learning bounds and algorithms for domain adaptation.
Wavelet scattering spectra model non-Gaussian time-series, proving scale invariance for self-similar processes.
problem Modeling non-Gaussian time-series with stationary increments.
method Complex wavelet transform for scale variations, joint correlation matrix for scale dependencies, second wavelet transform for diagonalization, maximum entropy models conditioned by scattering spectra coefficients.
result Scattering spectra of self-similar processes are scale invariant, allowing statistical testing and generation of new time-series.
PSDR improves robustness against noisy labels by penalizing KL divergence between similar inputs.
problem Robust training of DNNs in datasets with noisy labels.
method Introduces PSDR, a manifold regularizer that penalizes KL divergence between similar inputs.
result Significantly improves robustness against noisy labels on benchmark datasets.
New approach uses contrastive learning for better wireless power control.
problem Improving power control in wireless networks.
method A multi-layer perceptron with a contrastive learning backbone and head.
result Significant gains in sum-throughput and sample efficiency over supervised learning.
New method tags malware for better understanding and identification.
problem Lack of detailed descriptions and relationships between malware samples.
method Deep learning model generates human-interpretable descriptions and similarity index.
result Correctly identifies more than 95% of eleven tag descriptions with a low false positive rate.
Under the framework of spectral clustering, the key of subspace clustering is building a similarity graph which describes the neighborhood relations among data points. Some recent works build the graph using sparse, low-rank, and ℓ2-norm-based representation, and have achieved state-of-the-art performance. Howeve…
The paper analyzes unsupervised learning using contrastive methods and introduces a theoretical framework.
problem Learning useful feature representations from unlabeled data.
method Introduces latent classes and contrasts similar vs. non-similar data points.
result Proves guarantees on the performance of learned representations on downstream tasks.
New method approximates Individual Fairness using human judgments.
problem Enforcing fairness in classification tasks.
method Approximates a metric for Individual Fairness based on human queries.
result Constructs hypotheses for metric approximations that generalize.
Few-shot visual reasoning model learns analogical relationships from small data.
problem Training deep models on few samples for visual reasoning tasks.
method Meta-analogical contrastive learning to enforce structural similarity between training and test samples.
result Method outperforms state-of-the-art on RAVEN dataset with scarce training data.
Study evaluates synthetic data augmentation for small datasets, highlighting inconsistencies in traditional metrics.
problem Inconsistent validation of synthetic data generated for small sample sizes.
method Proposes a normalized Bottleneck distance metric to evaluate synthetic tabular data.
result Common metrics like propensity scoring and MMD fail for small datasets, showing instability and high variability.
Proposes a robust similarity measure for sparse time series data.
problem Sparse time course data in biological settings.
method Gaussian processes (GP) similarity measure based on log-likelihood ratio.
result Enhanced robustness to noise compared to Euclidean distance.
Develops a method for manifold learning with small sample size datasets.
problem Improving manifold learning performance for multiple tasks with limited samples.
method Uses instance and model transfer to integrate manifold models from similar tasks.
result Successfully estimates manifolds with tiny sample sizes across multiple tasks.
Proposes Group Loss for deep metric learning to improve clustering and image retrieval.
problem Improving deep metric learning for better clustering and image retrieval.
method Group Loss based on label-propagation method enforcing embedding similarity across all samples of a group.
result Shows state-of-the-art results on clustering and image retrieval on several datasets.
A novel unsupervised feature selection method using subspace clustering and self-expressive model.
problem Feature selection for large datasets with minimal labeling effort.
method Subspace clustering with adaptive representation learning and regularized regression.
result The method effectively captures sample similarities and discriminative information.
The problem of feature disentanglement has been explored in the literature, for the purpose of image and video processing and text analysis. State-of-the-art methods for disentangling feature representations rely on the presence of many labeled samples. In this work, we present a novel method for disentangling factors …
Algorithm estimates tensors from sparse observations with robust error bounds.
problem Estimating tensors from sparse noisy observations.
method Similarity-based collaborative filtering algorithm for tensor estimation.
result Achieves sample complexity nearly matching conjectured lower bound.
Time series are ubiquitous, and a measure to assess their similarity is a core part of many computational systems. In particular, the similarity measure is the most essential ingredient of time series clustering and classification systems. Because of this importance, countless approaches to estimate time series similar…
Meta-CVs leverage task similarity to reduce variance with limited data.
problem Reducing variance in Monte Carlo estimators with few samples.
method Meta-learning control variates for related tasks.
result Meta-CVs lead to significant variance reduction in settings with limited data.
Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks. In this paper, we propose the random sample partition (RSP) data model to represent a big data set as a set of non-overlapping data subsets, called RSP data blocks,…
Paper tackles multi-label learning by improving SVR for positive semidefinite metrics.
problem Learning positive semidefinite metrics for multi-label and label distribution learning.
method Proposes two methods to overcome SVR's limitation in learning positive semidefinite metrics.
result Demonstrates new methods achieve favorable performance in multi-label and label distribution learning.
A growing number of threats to Android phones creates challenges for malware detection. Manually labeling the samples into benign or different malicious families requires tremendous human efforts, while it is comparably easy and cheap to obtain a large amount of unlabeled APKs from various sources. Moreover, the fast-p…
Proposes a method to compare noisy high-dimensional datasets with low-dimensional manifolds.
problem Comparing distributions on manifolds in noisy high-dimensional datasets.
method Linking low-rank structure to manifold geometry, developing a scale-invariant distance measure.
result Superior robustness and statistical power compared to existing methods.
Graphs from features improve classification accuracy in tasks.
problem Traditional classification tasks can be improved by incorporating relational information.
method Construct geometric graphs from features and use them in Graph Convolutional Networks.
result Graphs derived from features increase classification accuracy and improve class separation.
Recently, metric learning and similarity learning have attracted a large amount of interest. Many models and optimisation algorithms have been proposed. However, there is relatively little work on the generalization analysis of such methods. In this paper, we derive novel generalization bounds of metric and similarity …
Most network-based speech recognition methods are based on the assumption that the labels of two adjacent speech samples in the network are likely to be the same. However, assuming the pairwise relationship between speech samples is not complete. The information a group of speech samples that show very similar patterns…
Learn dynamics of a system using auxiliary data from similar systems.
problem Learning dynamics of a linear system with limited data.
method Weighted least squares approach, incorporating auxiliary data.
result Auxiliary data can help reduce intrinsic error due to noise.
Deep metric learning aims to learn a deep embedding that can capture the semantic similarity of data points. Given the availability of massive training samples, deep metric learning is known to suffer from slow convergence due to a large fraction of trivial samples. Therefore, most existing methods generally resort to …
Paper develops robust k-NN algorithm for few samples.
problem Learning robust classifier from limited samples.
method Distributionally robust formulation of weighted k-NN. result Robust classifier improves generalization with smaller Lipschitz norm.