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.
In this paper, we propose a novel asymmetric -insensitive pinball loss function for quantile estimation. There exists some pinball loss functions which attempt to incorporate the -insensitive zone approach in it but, they fail to extend the -insensitive approach for quantile estimation in true sense. The propo…
The paper introduces a new FOR framework using Huber and ε-insensitive losses.
New SVM model balances sparsity and robustness in noisy data.
A new method for support vector regression using a data-driven insensitive parameter.
Study improves -consistency bounds for regression analysis.
Diffusion models accurately recover mixture weights from generated samples despite score function insensitivity.
RHPSVM improves SVM performance with robust loss function.
Proposes a fair pricing framework insensitive to protected covariates.
A new KF handles outliers without MSE loss.
We present surrogate regret bounds for arbitrary surrogate losses in the context of binary classification with label-dependent costs. Such bounds relate a classifier's risk, assessed with respect to a surrogate loss, to its cost-sensitive classification risk. Two approaches to surrogate regret bounds are developed. The…
New methods connect low-loss points on neural network surfaces.
We analyze dropout in deep networks with rectified linear units and the quadratic loss. Our results expose surprising differences between the behavior of dropout and more traditional regularizers like weight decay. For example, on some simple data sets dropout training produces negative weights even though the output i…
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…
This research analyzes the consistency of convex and nonconvex surrogate losses for adversarially robust classification.
SRHM explains deep learning's hierarchy and insensitivity to transformations.
Operator-Valued Kernels (OVKs) and associated vector-valued Reproducing Kernel Hilbert Spaces provide an elegant way to extend scalar kernel methods when the output space is a Hilbert space. Although primarily used in finite dimension for problems like multi-task regression, the ability of this framework to deal with i…
Symmetrizes loss functions to improve neural network robustness against noisy labels.
New algorithm reduces dimensionality in stochastic optimization.
A new model for complex cells accounts for insensitivity to image shifts.
Develops a new method for neural network significance testing without strict constraints.
Paper relaxes differential privacy for correlated features, improving privacy-utility trade-off.
A new R package for high-dimensional regression and precision matrix estimation.
A new method recovers latent potentials from graph flows, preserving ordering and stability.
This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR). In addition to this parameter, the moment function can also depend on a nuisance function, such as the propensity score or the conditional choice probability, which we es…
For many tasks and data types, there are natural transformations to which the data should be invariant or insensitive. For instance, in visual recognition, natural images should be insensitive to rotation and translation. This requirement and its implications have been important in many machine learning applications, a…
Recent literature on deep neural networks for tagging of highly energetic jets resulting from top quark decays has focused on image based techniques or multivariate approaches using high-level jet substructure variables. Here, a sequential approach to this task is taken by using an ordered sequence of jet constituents …
Deep models can fit noisy labels, but robustness and reliability are still issues.
Support vector machines (SVMs) are special kernel based methods and belong to the most successful learning methods since more than a decade. SVMs can informally be described as a kind of regularized M-estimators for functions and have demonstrated their usefulness in many complicated real-life problems. During the last…
New fairness criterion for risk-sensitive decisions in regulated industries.
We propose regularizing the empirical loss for semi-supervised learning by acting on both the input (data) space, and the weight (parameter) space. We show that the two are not equivalent, and in fact are complementary, one affecting the minimality of the resulting representation, the other insensitivity to nuisance va…
Deep learning dynamics exhibit anomalous superdiffusion initially, aiding escape from local minima.
This paper proposes a novel '-support vector quantile regression' (-SVQR) model for the quantile estimation. It can facilitate the automatic control over accuracy by creating a suitable asymmetric -insensitive zone according to the variance present in data. The proposed -SVQR model uses the fraction of …
PANDA improves linear discriminant analysis in high dimensions with minimal tuning.
Machine Learning improves macroeconomic forecasting by capturing nonlinearities.
A new method for decision-focused learning reduces computational cost.
Equity default-swaps pay the holder a fixed amount of money when the underlying spot level touches a (far-down) barrier during the life of the instrument. While most pricing models give reasonable results when the barrier lies within the range of liquidly traded strikes of plain-vanilla option prices, the situation is …
Simplifies neural regression by combining two sub-networks for predictions and uncertainties.
In this paper we study a model-based approach to calculating approximately optimal policies in Markovian Decision Processes. In particular, we derive novel bounds on the loss of using a policy derived from a factored linear model, a class of models which generalize numerous previous models out of those that come with s…
In decision-making systems, it is important to have classifiers that have calibrated uncertainties, with an optimisation objective that can be used for automated model selection and training. Gaussian processes (GPs) provide uncertainty estimates and a marginal likelihood objective, but their weak inductive biases lead…
A new method improves Bayesian inference for multimodal posteriors.
In this short communication we introduce the quick clustering algorithm (QUIST), an efficient hierarchical clustering algorithm based on sorting. QUIST is a poly-logarithmic divisive clustering algorithm that does not assume the number of clusters, and/or the cluster size to be known ahead of time. It is also insensiti…
Despite its success and popularity, machine learning is now recognized as vulnerable to evasion attacks, i.e., carefully crafted perturbations of test inputs designed to force prediction errors. In this paper we focus on evasion attacks against decision tree ensembles, which are among the most successful predictive mod…
Most density-based clustering methods largely rely on how well the underlying density is estimated. However, density estimation itself is also a challenging problem, especially the determination of the kernel bandwidth. A large bandwidth could lead to the over-smoothed density estimation in which the number of density …
This paper presents a neural network-based end-to-end clustering framework. We design a novel strategy to utilize the contrastive criteria for pushing data-forming clusters directly from raw data, in addition to learning a feature embedding suitable for such clustering. The network is trained with weak labels, specific…
No methods currently exist for making arbitrary neural networks fair. In this work we introduce GRAD, a new and simplified method to producing fair neural networks that can be used for auto-encoding fair representations or directly with predictive networks. It is easy to implement and add to existing architectures, has…
New damping technique improves deep learning models by reducing noise in flat directions.
Tr-LinUCB reduces regret in stochastic linear bandits by truncating exploration.
A global agreement on how to reduce and cap human footprint, especially their GHG emissions, is very unlikely in near future. At the same time, bilateral agreements would be inefficient because of their neural and balanced nature. Therefore, unilateral actions would have attracted attention as a practical option. Howev…