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.
CoPhy-PGNN tackles competing PG losses in neural networks for solving eigenvalue problems.
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…
SSH-Net: A Deep Neural Network for Predicting Failure Time Distribution Functions under Competing Risks with GPU Data
SurvivalBoost improves prediction of event times in competing risks scenarios.
Reduces dynamic regret to static problem in RKHS.
Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields of statistical science. The dissimilarity between a probability forecast and an outcome is measured by a loss function (scoring rule). Popular example of scoring rule for continuous outcomes is the …
The paper explores IL and LVR in AMMs, identifying three regimes and the effect of fees.
SurvLatent ODE predicts VTE risk for cancer patients, outperforming current methods.
Contrastive learning properties studied, including feature suppression and hierarchical learning.
Adversarial online nonparametric regression achieves optimal rates with locally adaptive learning.
Latent factor models for Recommender Systems with implicit feedback typically treat unobserved user-item interactions (i.e. missing information) as negative feedback. This is frequently done either through negative sampling (point--wise loss) or with a ranking loss function (pair-- or list--wise estimation). Since a ze…
Study analyzes landscape complexity of empirical loss functions with correlated data.
Optimal algorithms for mixable losses in dynamic environments with reduced redundancy.
AuxiLearn combines auxiliary tasks into a single loss function.
Near-logarithmic regret per switch achieved for mixable/exp-concave losses.
Generalized algorithm for translation and scale-invariant prediction.
Estimating machine learning performance 'in the wild' is both an important and unsolved problem. In this paper, we seek to examine, understand, and predict the pointwise competence of classification models. Our contributions are twofold: First, we establish a statistically rigorous definition of competence that general…
A competing market model with a polyvariant profit function that assumes "zeitnot" stock behavior of clients is formulated within the banking portfolio medium and then analyzed from the perspective of devising optimal strategies. An associated Markov process method for finding an optimal choice strategy for monovariant…
SLT reveals how grokking occurs via basin selection in training.
The problem of estimating a high-dimensional sparse vector from an observation in i.i.d. Gaussian noise is considered. The performance is measured using squared-error loss. An empirical Bayes shrinkage estimator, derived using a Bernoulli-Gaussian prior, is analyzed and compared with the…
Protocol assesses better model among two opaque models with minimal interaction.
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…
Non-negative matrix factorization (NMF) minimizes the Euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated CauchyNMF loss that handle outliers by truncating large e…
We tackle the problem of Federated Learning in the non i.i.d. case, in which local models drift apart, inhibiting learning. Building on an analogy with Lifelong Learning, we adapt a solution for catastrophic forgetting to Federated Learning. We add a penalty term to the loss function, compelling all local models to con…
Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this limitation, self-supervised learning has emerged as a promising alternative for training models to perform monocular depth estimation. In this paper, we propose a set of improvements, which together result in both quantitatively and …
The paper explains how language models acquire complex skills through scaling laws and statistical analysis.
New bounds on minimax regret for sequential probability assignment using logarithmic loss.
This work includes a number of novel contributions for the multiple-source adaptation problem. We present new normalized solutions with strong theoretical guarantees for the cross-entropy loss and other similar losses. We also provide new guarantees that hold in the case where the conditional probabilities for the sour…
Study non-monotonic loss functions in CRC, achieving valid risk control with large calibration samples.
Comprisk simplifies competing-risks analysis in Python.
CIR method preserves relation for case-control studies.
HACSurv models dependencies between competing risks and censoring for improved survival analysis.
Improved incremental sequence classification with temporal consistency.
Develops regression trees for estimating cumulative incidence curves in competing risks.
A binary classifier capable of abstaining from making a label prediction has two goals in tension: minimizing errors, and avoiding abstaining unnecessarily often. In this work, we exactly characterize the best achievable tradeoff between these two goals in a general semi-supervised setting, given an ensemble of predict…
New algorithms achieve better regret bounds for online classification with relaxed benchmarks.
Due to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting. However, most methods focus on estimating the conditional mean or specific quantiles of the target quantity and do not provide the full conditional distri…
The goal of lifetime clustering is to develop an inductive model that maps subjects into clusters according to their underlying (unobserved) lifetime distribution. We introduce a neural-network based lifetime clustering model that can find cluster assignments by directly maximizing the divergence between the empiri…
A statistical functional, such as the mean or the median, is called elicitable if there is a scoring function or loss function such that the correct forecast of the functional is the unique minimizer of the expected score. Such scoring functions are called strictly consistent for the functional. The elicitability of a …
This paper addresses the problem of estimating, in the presence of random censoring as well as competing risks, the extreme value index of the (sub)-distribution function associated to one particular cause, in the heavy-tail case. Asymptotic normality of the proposed estimator (which has the form of an Aalen-Johansen i…
Develops new methods to estimate treatment effects in survival data with competing risks.
Develops RF-GLS for binary geospatial data.
Study shows gain-loss asymmetry in stock indices using a q-spin Potts model.
New algorithm handles bandit problems under translations and scales.
TSL learns separable models to avoid signal cancellation and off-support extrapolation.
Efficient human resource management needs accurate assessment and representation of available competences as well as effective mapping of required competences for specific jobs and positions. In this regard, appropriate definition and identification of competence gaps express differences between acquired and required c…
Robust feature-weighted jump models for time-dependent clustering
AdaSwarm optimizes deep learning models with swarm intelligence, outperforming Adam.