A method for classification using pairwise similarities and unlabeled data.
problem Handling pairwise similarities and unlabeled data for classification.
method Empirical risk minimization approach to create an unbiased risk estimator.
result Derives an unbiased risk estimator for handling both similarities and unlabeled data.
New measure quantifies function similarity for optimization.
problem Measuring similarity between functions for optimization.
method Quantifies sub-optimality gaps and operation rules.
result Unified measure for various functional similarities.
SBIC learns patterns in imbalanced datasets using empirical similarity and synthetic data.
problem Classification failure in imbalanced datasets.
method SBIC uses an empirical similarity function and absent data to optimize weights and find minority class data points.
result SBIC outperforms other classification techniques for imbalanced datasets.
Defines a similarity measure for classification distributions.
problem Measuring similarity between classification distributions.
method Proposes task similarity, a novel measure quantifying performance of source distributions on target distributions.
result Empirical task similarity correlates with transfer efficiency and semantic similarity of source distributions.
Similar models predict similarly, reducing overfitting risk.
problem Excessive reuse of test data in machine learning.
method Proved model similarity mitigates overfitting and provided a generalization bound.
result Model similarity reduces the risk of overfitting, even when accuracy levels suggest otherwise.
Empirical study on hyperparameters affecting RL generalization.
problem Understanding how hyperparameters affect RL generalization across different domains.
method Empirical analysis of hyperparameters including architecture, regularization, and RL-dependent variables.
result Complex and interdependent relationships between hyperparameters and generalization performance.
Proposes a method to compare neural networks using feature and gradient vectors.
problem Understanding the behavior of neural networks trained on different datasets.
method Defines a similarity index using feature and gradient vectors, and employs sketching techniques for efficient comparison.
result Demonstrates superior performance in computing similarity of neural networks trained on different datasets.
The study improves the assessment of fairness in face recognition using ROC curves and statistical guarantees.
problem Improving the assessment of fairness in face recognition systems.
method Proves asymptotic guarantees for empirical ROC curves and fairness metrics, and introduces a recentering technique to avoid bootstrap pitfalls.
result Demonstrates the practical relevance of the methods for assessing fairness in face recognition systems.
The study examines how character and word-level representations improve sentence-level tasks.
problem Improving the quality of word and sentence representations using character-level information.
method Feature-wise sigmoid gating mechanism for combining character and word-level representations.
result Modeling characters improves final word and sentence representations, especially for less frequent words.
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.
Cryptocurrencies show similarities to traditional markets but also have unique characteristics.
problem Understanding the investment potential and characteristics of cryptocurrencies.
method Organized stylized facts and analyzed through empirical asset pricing.
result Cryptocurrencies exhibit similarities to traditional markets but also have distinct characteristics.
This paper tackles noise in raw datasets to improve representation learning efficiency.
problem Noise in real-world datasets degrades representation learning quality.
method Proposes denoising Cosine-Similarity (dCS) loss to learn robust representations.
result Empirical results show the dCS loss outperforms baseline objective functions.
Empirical analysis of the foreign exchange market is conducted based on methods to quantify similarities among multi-dimensional time series with spectral distances introduced in [A.-H. Sato, Physica A, 382 (2007) 258--270]. As a result it is found that the similarities among currency pairs fluctuate with the rotation …
Develops an ordinal-similarity framework for scalable and interpretable representation alignment.
problem Measuring representation similarity in large datasets.
method Triplet and Quadruplet Similarity Indices.
result Demonstrates inherent interpretability, robustness to outliers, and computational efficiency.
Study uses trajectory embedding to measure place function similarity at fine spatial granularity.
problem Measuring place function similarity at fine spatial granularity.
method Trajectory embedding to reduce dimensions and measure similarity of place functions.
result Embedding similarity can be a metric proxy for place functions at fine spatial granularity.
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…
Unified approach to private statistics from empirical to population data.
problem Divided focus on empirical vs population statistics in private statistics.
method Unified methods for both types of statistics.
result Methods for empirical statistics can be applied to population statistics.
Financial market dynamics is rigorously studied via the exact generalized Langevin equation. Assuming market Brownian self-similarity, the market return rate memory and autocorrelation functions are derived, which exhibit an oscillatory-decaying behavior with a long-time tail, similar to empirical observations. Individ…
STRAPSim measures ETF portfolio similarity better than existing methods.
problem Measuring portfolio similarity for ETFs and portfolios.
method Semantic, two-level, residual-aware portfolio similarity computation.
result STRAPSim outperforms existing methods in predictive accuracy and ranking alignment.
MRS optimizes for minimum simple regret in Bayesian optimization.
problem Optimizing for the best recommendation in multi-task settings.
method Minimum Regret Search (MRS) aims to minimize expected simple regret.
result MRS produces fewer high simple regret recommendations than entropy search.
Framework for assessing fairness across similar predictive models.
problem Fairness in predictive models across different groups.
method Develops a framework for characterizing fairness over the set of good models under selective labels.
result Framework can replace or audit models for better fairness properties.
Study improves material similarity measures considering distinctiveness.
problem Improving similarity measures for materials science applications.
method Used machine learning techniques with specific descriptors and kernels.
result Minimizing loss of distinctiveness improves prediction accuracy.
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.
ContraSim learns financial headline similarities for market forecasting.
problem Financial market forecasting accuracy improvement.
method ContraSim framework with Weighted Headline Augmentation and WSSCL.
result Improves financial forecasting accuracy by 7%.
Graph kernels assess graph similarity for various applications.
problem Assessing similarity between graphs for predictions.
method Review and comparison of existing graph kernels.
result State-of-the-art graph kernels reviewed and compared.
Improved A/B testing by leveraging system similarities.
problem Traditional A/B testing ignores potential system similarities.
method Off-policy estimation to exploit system propensities.
result Improved A/B testing estimators achieve better accuracy.
The paper proposes tree-based methods for automatically learning similarity measures.
problem Automatically learning similarity measures in feature spaces.
method Formulates similarity learning as a pairwise bipartite ranking problem and uses recursive tree-based ROC optimization.
result Validates iterative partitioning procedures for similarity learning and proposes efficient algorithms.
The paper tackles learning from similar but not identical linear representations, improving performance over single-task learning.
problem Understanding how to learn from tasks with similar but not exactly the same linear representations, especially when dealing with outlier tasks.
method Proposes adaptive and robust penalized empirical risk minimization and spectral methods.
result Both methods outperform single-task learning when representations are similar and perform at least as well otherwise, with minimax optimality demonstrated.
Paper explains contrastive learning using cosine similarity and proposes mitigations for batch size effects.
problem Understanding and improving contrastive learning through batch size effects.
method Unified framework of cosine similarity, theoretical insights, and auxiliary loss.
result Performance improvement in small-batch settings through proposed auxiliary loss.
Extends convex clustering to graph-structured data.
problem Handling graph-structured data with convex clustering.
method Formulates a convex objective and uses a proximal dual algorithm for efficient recovery.
result Demonstrates the effectiveness of the method on real-life datasets.
Graph neural networks improve with edge similarity constraints in RNA structure analysis.
problem Lack of edge similarity constraints in graph neural networks.
method Introduced a graph neural network layer that leverages prior information about edge similarities.
result Edge similarity constraints do not enhance performance in graph neural networks.
Proposes rpf-kernel for clustering via random projection forests.
problem Clustering similar data points while distinguishing them from dissimilar ones.
method Random projection forests to learn a similarity kernel.
result rpf-kernel effectively clusters data with competitive performance.
Pairwise quantile regression tackles similarity scoring in biometric systems.
problem Analyzing errors in similarity scoring for facial recognition.
method Established theoretical guarantees for pairwise quantile regression solutions, leveraging sharp concentration results for U-processes. result Proved generalization bounds and identified conditions for fast learning rates.
Enhances CLIP's similarity computation using PMI's linear structure.
problem CLIP's similarity computation misses the optimal linear structure of PMI.
method KME-CLIP, utilizing inner product in a reproducing kernel Hilbert space.
result KME-CLIP approximates PMI with arbitrary accuracy and outperforms CLIP.
Tile2Vec learns spatially meaningful representations without labels.
problem Lack of unsupervised methods for geospatial data.
method Unsupervised representation learning using the distributional hypothesis.
result Tile2Vec improves performance in spatial classification tasks.
Paper tackles clustering with ordinal comparisons, achieving near-optimal results.
problem Clustering with ordinal comparisons when similarity measures are not available.
method Two-step procedure: estimate similarity matrix from comparisons, then apply SDP clustering.
result Near-optimal recovery of planted clustering using near-optimal number of comparisons.
We discuss a weighted estimation of correlation and covariance matrices from historical financial data. To this end, we introduce a weighting scheme that accounts for similarity of previous market conditions to the present one. The resulting estimators are less biased and show lower variance than either unweighted or e…
Extends graph similarity theory to improve MPNNs' generalization abilities.
problem Understanding MPNNs' generalization beyond training data.
method Extends graph similarity theory, assesses graph structure, aggregation, and loss functions.
result Improves understanding of MPNNs' generalization properties.
LS-SVR and Bayesian RBF networks are shown to be theoretically similar.
problem Improving LS-SVR performance through Bayesian methods.
method Formal demonstration of theoretical similarities between LS-SVR and Bayesian RBF networks.
result LS-SVR and Bayesian RBF networks have equivalent theoretical expressions.
Proposes a deep learning framework for evaluating patient similarities from EHRs.
problem Evaluating clinical similarities between patients for various healthcare applications.
method A deep learning framework with medical concept embedding, preserving temporal information.
result Significant improvement in patient similarity evaluation over baselines.
This study investigates self-supervised learning with Wasserstein distance on tree structures.
problem Improving self-supervised learning methods using Wasserstein distance.
method Utilized Tree-Wasserstein distance (TWD) and Jeffrey divergence regularization for training.
result A simple combination of softmax function and Tree-Wasserstein distance outperforms cosine similarity-based methods.
The paper proposes a new method to evaluate LLM agent responses using ECDF clustering.
problem The standard evaluation of LLM agent responses via majority voting obscures response quality and distribution.
method The paper introduces a novel evaluation framework based on ECDF of cosine similarities and clustering of ECDFs using distances and k-medoids algorithm. result ECDF clustering reveals interpretable group structures in LLM responses, offering insights into agent settings.
The paper analyzes the generalization of deep neural networks for metric and similarity learning.
problem Lack of rigorous understanding of generalization performance in metric and similarity learning.
method Derive explicit form of true metric, construct structured deep ReLU neural network, establish excess risk bounds.
result Explicit excess risk bounds for metric and similarity learning are derived.
Geometrically transforms word embeddings into a common space for better comparison.
problem Comparing embeddings from different sources is challenging.
method Applies orthogonal rotations and Mahalanobis scaling to transform embeddings into a shared latent space.
result The method improves word similarity and analogy tasks.
Improved kernel methods enhance data similarity estimation.
problem Efficiently estimating the RBF kernel's similarity.
method Normalized Random Fourier Features (NRFF) and Generalized Min-Max (GMM) kernel.
result GCWS typically requires fewer samples than NRFF for comparable accuracy.
The paper optimizes similarity learning for better machine learning performance.
problem Improving machine learning performance through better similarity measures.
method Probabilistic framework for pairwise bipartite ranking, focusing on pointwise ROC optimization.
result Universal and faster learning rates derived for the optimization problem.
Proposes a method to learn optimal neighbors and projection matrix in low-dimensional space.
problem Difficulty in precisely measuring similarity and selecting optimal neighbors in high-dimensional space.
method Models similarity and neighbors as variables, optimizing a unified objective function with nonnegative and sum-to-one constraints.
result Optimal similarity and projection matrix learned simultaneously, with adaptive regularization parameter.
Two new algorithms improve federated optimization under second-order similarity.
problem Federated learning under communication constraints and second-order similarity.
method SVRP and Catalyzed SVRP algorithms combining proximal point evaluations, client sampling, and variance reduction.
result Achieves superior performance and uniformly improves upon existing algorithms for federated optimization under second-order similarity and strong convexity.