A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study shows the corrected Akaike criterion is inadmissible for estimating Kullback-Leibler discrepancy.
Paper proposes a method to improve deep neural networks' confidence estimates.
This paper evaluates various loss functions for Transformer models in stock ranking.
Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with such problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this pap…
Corrected misstatements about invariant rank in ECS manifold papers.
GrowNet uses shallow neural networks for gradient boosting, outperforming existing methods.
Max-rank improves multiple testing in conformal prediction.
New method corrects quantization errors in LLMs using low-rank matrices.
For the problems of low-rank matrix completion, the efficiency of the widely-used nuclear norm technique may be challenged under many circumstances, especially when certain basis coefficients are fixed, for example, the low-rank correlation matrix completion in various fields such as the financial market and the low-ra…
Paper analyzes convergence of PAM method for low-rank factorization models.
Research characterizes learnability of multilabel ranking problems.
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 …
In this paper we construct families of homology spheres which bound 4-manifolds with intersection forms isomorphic to . We show that these families have arbitrary large correction terms. This result says that among homology spheres, the difference of the maximal rank of minimal sub-lattice of definite filling and…
In deep neural network, the cross-entropy loss function is commonly used for classification. Minimizing cross-entropy is equivalent to maximizing likelihood under assumptions of uniform feature and class distributions. It belongs to generative training criteria which does not directly discriminate correct class from co…
New method improves model calibration by adjusting confidence based on prediction correctness.
Paper develops methods for non-quadratic loss low-rank matrix recovery.
Logit correction improves model performance by correcting spurious correlations.
We model and correct bias in sequential evaluation, improving ranking accuracy.
Boosted decision trees typically yield good accuracy, precision, and ROC area. However, because the outputs from boosting are not well calibrated posterior probabilities, boosting yields poor squared error and cross-entropy. We empirically demonstrate why AdaBoost predicts distorted probabilities and examine three cali…
A new method for forming learning objectives using the sum of ranked range.
Listwise learning-to-rank methods form a powerful class of ranking algorithms that are widely adopted in applications such as information retrieval. These algorithms learn to rank a set of items by optimizing a loss that is a function of the entire set -- as a surrogate to a typically non-differentiable ranking metric.…
This work analyzes two methods for combining multiple binary labels in bipartite ranking.
We present an attention-based ranking framework for learning to order sentences given a paragraph. Our framework is built on a bidirectional sentence encoder and a self-attention based transformer network to obtain an input order invariant representation of paragraphs. Moreover, it allows seamless training using a vari…
In this work, we addressed the issue of applying a stochastic classifier and a local, fuzzy confusion matrix under the framework of multi-label classification. We proposed a novel solution to the problem of correcting label pairwise ensembles. The main step of the correction procedure is to compute classifier- specific…
This paper improves multi-label ranking by reweighting univariate losses, enhancing consistency and performance.
A new correction term improves sample efficiency in deep reinforcement learning.
Convolution Neural Networks (CNN) have recently achieved state-of-the art performance on handwritten Chinese character recognition (HCCR). However, most of CNN models employ the SoftMax activation function and minimize cross entropy loss, which may cause loss of inter-class information. To cope with this problem, we pr…
New methods ensure feature importance rankings are correct with high probability.
A new method corrects bias in machine learning for trading by filtering out non-executable prices.
We propose a new learning to rank algorithm, named Weighted Margin-Rank Batch loss (WMRB), to extend the popular Weighted Approximate-Rank Pairwise loss (WARP). WMRB uses a new rank estimator and an efficient batch training algorithm. The approach allows more accurate item rank approximation and explicit utilization of…
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…
Develops a comprehensive theory of corruption in supervised learning.
New algorithm predicts ranked stock lists for long-short portfolios.
We correct for sampling bias in training models to improve real-world performance.
Improved unsupervised probing for ranking tasks using Contrast-Consistent Ranking.
A new method combines Laplace and Variational Bayes for scalable inference.
Paper corrects Max-Margin loss for multi-label tasks.
Deep learning has become the method of choice in many application domains of machine learning in recent years, especially for multi-class classification tasks. The most common loss function used in this context is the cross-entropy loss, which reduces to the log loss in the typical case when there is a single correct r…
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 of 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 , and the loss is the position (0th, 1st, 2nd...) of the item in the outputted r…
Owing to the advancement of deep learning, artificial systems are now rival to humans in several pattern recognition tasks, such as visual recognition of object categories. However, this is only the case with the tasks for which correct answers exist independent of human perception. There is another type of tasks for w…
Unified framework for binary responses using AUC loss and low-rank constraint.
The purpose of this erratum is to correct a mistake in the proof of Theorem 4.1 of our paper \cite{CF}.
Introduces SoRR for aggregating losses in supervised learning.
Contrastive UCB improves RL by learning feature representations efficiently.
Multi-label classification studies the task where each example belongs to multiple labels simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) aims to minimize the Ranking Loss and can also mitigate the negative influence of the class-imbalance issue. However, due to its stacking-style …
Label smoothing improves model performance even with noisy labels.