Improves cross-modal retrieval by integrating unlabeled data.
problem Lack of semantic similarity constraints and unlabeled data in cross-modal retrieval.
method Integrates quadruplet ranking loss and semi-supervised contrastive loss in a multi-task learning architecture.
result Boosts cross-modal retrieval accuracy by exploiting unlabeled data.
Let { P 1 , P 2 , P 3 , P 4 } \{P_1, P_2, P_3, P_4\} { P 1 , P 2 , P 3 , P 4 } be a quadruplet of points in S 3 S^3 S 3 . We define a ``dual'' quadruplet of it in a conformal geometric way. We show that the dual of a dual quadruplet coincides with the original one. We also show that the cross ratio of the dual quadruplet is equal to the complex conjugate of that of the orig…
Framework learns item representations from text data for complementary and similar items.
problem Generating accurate complementary item recommendations from textual data.
method Quadruplet network learning framework for latent space representation of items.
result Items are placed closer together in latent space for similar and complementary items compared to non-complementary items.
Research characterizes learnability of multilabel ranking problems.
problem Learnability of multilabel ranking problems with relevance-score feedback.
method Characterizes learnability in batch and online settings for a large family of ranking losses.
result Characterizes two equivalence classes of ranking losses based on learnability.
Proposes a cross entropy loss for better ranking algorithms.
problem Improving the theoretical understanding and performance of ranking algorithms.
method Introduces a cross entropy-based loss function that is a convex bound on NDCG and consistent with NDCG.
result Empirically, the proposed method outperforms existing algorithms in quality and robustness.
Ranking is a key aspect of many applications, such as information retrieval, question answering, ad placement and recommender systems. Learning to rank has the goal of estimating a ranking model automatically from training data. In practical settings, the task often reduces to estimating a rank functional of an object …
Improved CNN for HCCR with new loss function and ranking method.
problem Loss of inter-class information in traditional CNN models for HCCR.
method Combining cross entropy with a new similarity ranking function (Average variance similarity) as loss function.
result New loss function (SoftMax cross entropy with Average variance similarity) achieves highest accuracy in HCCR.
Paper develops methods for non-quadratic loss low-rank matrix recovery.
problem Recovery of low-rank matrices with non-quadratic losses.
method Projected gradient method with a regularity projection oracle.
result Projected gradient method converges globally and linearly.
Framework learns sentence order from paragraphs using attention and transformer networks.
problem Learning to order sentences from a paragraph.
method Bidirectional sentence encoder and self-attention transformer network for ranking.
result Framework outperforms state-of-the-art methods on sentence ordering and discrimination tasks.
This paper evaluates various loss functions for Transformer models in stock ranking.
problem Evaluating loss functions for Transformer models in stock ranking.
method Systematic evaluation of advanced loss functions (pointwise, pairwise, listwise) on S&P 500 data.
result Different loss functions impact a model's ability to discern profitable relative orderings among assets.
A new method for forming learning objectives using the sum of ranked range.
problem Forming learning objectives from aggregated values.
method Sum of ranked range (SoRR) minimization with DCA.
result The proposed method effectively forms learning objectives and is applicable to binary and multi-label/multi-class classification.
HERA improves PLL by integrating heterogeneous loss and sparse-low-rank regularization.
problem Learning from data with partial labels.
method Combines heterogeneous loss and sparse-low-rank regularization.
result Achieves superior performance on artificial and real-world data.
This work analyzes two methods for combining multiple binary labels in bipartite ranking.
problem Combining multiple binary labels for optimal bipartite ranking.
method Loss aggregation vs. label aggregation approaches.
result Label aggregation is preferable to loss aggregation due to label dictatorship issues.
Paper solves vortex equations on complex surfaces, linking to Higgs bundle stability.
problem Existence of solutions to doubly-coupled vortex equations on Riemann surfaces.
method Introduced doubly-coupled vortex equations and used Higgs bundle theory.
result Existence of solutions to vortex equations is equivalent to Higgs bundle stability.
This paper improves multi-label ranking by reweighting univariate losses, enhancing consistency and performance.
problem Improving multi-label ranking performance while maintaining consistency.
method Systematic study of consistency and generalization error bounds for learning algorithms, proposing a reweighted univariate loss.
result Inconsistent pairwise losses can lead to better performance than consistent univariate losses in practice.
A new method for quantized matrix completion using Huber loss.
problem Quantized Matrix Completion with robustness to quantization errors.
method Rank minimization with Huber loss regularization, Smooth Rank Approximation.
result Our method achieves better accuracy and efficiency than state-of-the-art methods.
We consider the problem of rank loss minimization in the setting of multilabel classification, which is usually tackled by means of convex surrogate losses defined on pairs of labels. Very recently, this approach was put into question by a negative result showing that commonly used pairwise surrogate losses, such as ex…
WMRB improves ranking accuracy and efficiency in scalable batch training.
problem Improving ranking accuracy and efficiency in large-scale recommendation systems.
method WMRB uses a new rank estimator and an efficient batch training algorithm.
result WMRB consistently outperforms WARP and other baselines in three item recommendation tasks.
New algorithm predicts ranked stock lists for long-short portfolios.
problem Constructing effective long-short stock portfolios using machine learning.
method Proposes a new listwise learn-to-rank loss function to emphasize top and bottom of a rank list.
result Demonstrates superior performance in constructing long-short portfolios with a 38% annual return.
Improved unsupervised probing for ranking tasks using Contrast-Consistent Ranking.
problem Improving self-consistency in language model rankings.
method Adapting Contrast-Consistent Search (CCS) to Contrast-Consistent Ranking (CCR) for ranking tasks.
result CCR probing outperforms prompting techniques across different models and datasets.
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.
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object. We present a general kernel framework for learning conditional rankings from various types of relational da…
The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. …
Given a set V V V of n n n objects, an online ranking system outputs at each time step a full ranking of the set, observes a feedback of some form and suffers a loss. We study the setting in which the (adversarial) feedback is an element in V V V , and the loss is the position (0th, 1st, 2nd...) of the item in the outputted r…
Unified framework for binary responses using AUC loss and low-rank constraint.
problem Statistical inefficiency and shared structure in fitting multiple binary responses.
method Pairwise AUC loss aggregation with low-rank constraint, scalable projected gradient descent.
result Unified framework outperforms likelihood-based approaches in challenging settings.
Model predicts hit songs in pop music using Siamese CNN with ranking loss.
problem Predicting hit songs in the pop music industry.
method A multi-objective Siamese CNN model trained with Euclidean and ranking losses, using A/B sampling and neural attention.
result The model with A/B sampling achieves higher accuracy in hit song prediction.
Introduces SoRR for aggregating losses in supervised learning.
problem Aggregating individual losses into a single output for machine learning models.
method Sum of ranked range (SoRR) minimization using DCA.
result Demonstrates effectiveness of AoRR and TKML in improving robustness of multi-label learning.
A new multi-label classification model combining SVM and BR with low-rank learning.
problem Class imbalance and label correlation issues in multi-label classification.
method Joint Ranking SVM and Binary Relevance with robust Low-rank learning (RBRL).
result RBRL outperforms state-of-the-art methods in multi-label classification.
New method optimizes AUC without pairwise comparisons, improving efficiency.
problem Difficulty in directly optimizing AUC due to slow running time and storage requirements.
method Developed a new surrogate loss that avoids pairwise comparisons and ranks predictions.
result The new learning objective has linear complexity in time and storage.
Paper tackles low-rank matrix recovery with KL property and DC reformulation.
problem Low-rank matrix recovery with coarse rank estimation.
method Adds ℓ 2 , 0 \ell_{2,0} ℓ 2 , 0 -norm and balanced terms to factorized loss function; establishes KL property and DC reformulations. result Establishes KL property of exponent 1 / 2 1/2 1/2 for the composite function and its global minimizers. Unified framework for non-Euclidean CPD under scalable stochastic mirror descent.
problem Handling non-Euclidean losses in tensor decomposition.
method Tensor fiber sampling strategy-based stochastic mirror descent.
result Global convergence to a stationary point under reasonable conditions.
TripleSurv improves survival analysis by ranking samples with time-adaptive adjustments.
problem Modeling censored time-to-event data with high accuracy and robustness.
method Introduces a time-adaptive coordinate loss function to rank samples and calibrate robustness.
result TripleSurv outperforms state-of-the-art methods on various survival datasets.
Unified algorithm for tensor decomposition supports multiple loss functions and models.
problem Efficient tensor decomposition for various models and loss functions.
method Hierarchical combination of ADMM and MM for optimization.
result Wide-range applications can be solved by the proposed algorithm.
A new model for supervised learning to rank using gradient estimation.
problem Improving supervised learning to rank methods.
method Proposes a new model with a gradient estimator for categorical variables.
result Achieves better or comparable results compared to existing methods.
A new model for data with zeros or missing values.
problem Data with excess zeros or missing values.
method Composite loss framework for low-rank modeling, combining generalized low-rank and hurdle methods.
result Demonstrated on a manufacturing data set and applied to missing value imputation.
New method proves asymptotic normality for matrix sensing problems.
problem Proving asymptotic normality for matrix sensing under general convex losses.
method Riemannian geometry to handle degeneracy of the Hessian due to rotational symmetry.
result Proves n ( φ 0 − φ ∗ ) → D N ( 0 , ( H ∗ ) − 1 ) \sqrt{n}(φ^0-φ^*)\xrightarrow{D}N(0,(H^*)^{-1}) n ( φ 0 − φ ∗ ) D N ( 0 , ( H ∗ ) − 1 ) as n o ∞ n o\infty n o ∞ . A structured prediction method for ranking labels.
problem Solving label ranking problems as structured output regression.
method Two-step approach: regression in feature space followed by pre-image solving.
result Efficiency on real-world datasets for partial and complete rankings.
Study on deep matrix factorization with Bures-Wasserstein loss, focusing on critical points and convergence.
problem Analyzing critical points and convergence of generative deep linear networks trained with Bures-Wasserstein loss.
method Characterization of critical points and minimizers of Bures-Wasserstein distance, analysis of Hessian at low-rank matrices, convergence results for gradient flow and descent.
result Established convergence results for gradient flow and finite step size gradient descent under certain assumptions.
Online boosting algorithms improve multi-label ranking accuracy.
problem Improving multi-label ranking accuracy through online boosting.
method Design and analysis of online boosting algorithms with provable loss bounds.
result Our adaptive algorithm achieves comparable performance to existing batch boosting methods without requiring knowledge of weak learner edges.
Paper tackles ambiguity in relevance labels for neural list-wise learning to rank.
problem Ambiguity in relevance labels in learning to rank data.
method Proposes a novel sampling technique for computing a list-wise loss.
result Significantly outperforms other methods on validation and test sets.
Data compression speeds up machine learning loss calculations.
problem Computational demand in calculating mean squared error for large datasets.
method Use rank-1 lattices to compress data, assigning weights based on original data and responses.
result Our QMC data compression algorithms can lead to arbitrary high convergence rates for smooth functions.
In many real-world applications of machine learning classifiers, it is essential to predict the probability of an example belonging to a particular class. This paper proposes a simple technique for predicting probabilities based on optimizing a ranking loss, followed by isotonic regression. This semi-parametric techniq…
The paper explores how the depth of neural networks affects their ability to represent data accurately.
problem Understanding the implicit bias and rank of neural networks with large depth.
method Analyzing the convergence of representation cost to a notion of rank as network depth increases, and investigating conditions for recovering the true rank of data.
result There is a range of network depths where the true rank of data is recovered, and this affects the topology of class boundaries.
Online boosting for multilabel ranking with limited feedback.
problem Multilabel ranking with top-k feedback.
method Surrogate loss function and unbiased estimator for weak learners.
result Adapted full information multilabel ranking algorithms to top-k feedback setting with theoretical and experimental support.
Perceptron is a classic online algorithm for learning a classification function. In this paper, we provide a novel extension of the perceptron algorithm to the learning to rank problem in information retrieval. We consider popular listwise performance measures such as Normalized Discounted Cumulative Gain (NDCG) and Av…
The paper introduces a method for multi-label classification that allows partial predictions.
problem Handling multi-label classification with the option to abstain from predictions.
method Formalized MLC with abstention as a generalized loss minimization problem.
result Initial results for Hamming loss, rank loss, and F-measure.
Deep ReLU networks with extra parameters have mostly good loss landscapes.
problem Finding good local minima in the loss landscape of deep neural networks.
method Analyzing shallow and deep ReLU networks with extra parameters on a generic dataset.
result Most activation patterns correspond to regions with no bad local minima.
FedLoRU improves FL efficiency by using low-rank updates.
problem Communication inefficiency and performance reduction in Federated Learning.
method Proposes FedLoRU, a low-rank update framework for FL, which reduces communication costs while maintaining performance.
result FedLoRU achieves convergence rates similar to FedAvg and is robust to heterogeneous and large numbers of clients.