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.
This paper reviews forecast combinations over 50 years, highlighting their evolution and utility.
In this work, we addressed the issue of combining linear classifiers using their score functions. The value of the scoring function depends on the distance from the decision boundary. Two score functions have been tested and four different combination strategies were investigated. During the experimental study, the pro…
New Klein-Maskit theorems for Anosov subgroups.
Unified framework combines dependent microbiome tests.
Generative model designs drug combinations for improved efficacy and reduced side effects.
We prove a combination theorem for trees of (strongly) relatively hyperbolic spaces and finite graphs of (strongly) relatively hyperbolic groups. This gives a geometric extension of Bestvina and Feighn's Combination Theorem for hyperbolic groups and answers a question of Swarup. We also prove a converse to the main Com…
MAC combines models without locking them, improving ensemble performance.
Survey of combination theorems in geometry and dynamics.
Combines VaR and ES forecasts from a large pool of methods.
RCAM-based ensemble combines binary classifiers using similarity and vote scheme.
Drug resistance is still a major challenge in cancer therapy. Drug combination is expected to overcome drug resistance. However, the number of possible drug combinations is enormous, and thus it is infeasible to experimentally screen all effective drug combinations considering the limited resources. Therefore, computat…
The paper proposes a method to improve forecast combination accuracy using portfolio theory.
A new method forecasts financial tail risks by combining and weighting quantiles.
New method combines score lists using joint CDFs, improving computation.
Synthetic Combinations learns unit-specific causal outcomes for combinatorial interventions.
Combines human and model predictions for improved accuracy.
Probabilistic programs with dynamic computation graphs can define measures over sample spaces with unbounded dimensionality, which constitute programmatic analogues to Bayesian nonparametrics. Owing to the generality of this model class, inference relies on `black-box' Monte Carlo methods that are often not able to tak…
Combines VaR and ES forecasts for cryptocurrency market risk management.
NCoRE learns counterfactual representations for combined treatments.
Classifier ensembles are pattern recognition structures composed of a set of classification algorithms (members), organized in a parallel way, and a combination method with the aim of increasing the classification accuracy of a classification system. In this study, we investigate the application of a generalized mixtur…
Develops inference combinators for probabilistic programs using neural networks.
We show in this short note that if a rational linear combination of Pontrjagin numbers vanishes on all simply-connected -dimensional closed connected and oriented spin manifolds admitting a Riemannian metric whose Ricci curvature is nonnegative and nonzero at any point, then this linear combination must be a multip…
Combination theorem for PGF groups helps in constructing new examples and understanding their geometry.
In this paper, we introduce a novel combined reward cum penalty loss function to handle the regression problem. The proposed combined reward cum penalty loss function penalizes the data points which lie outside the -tube of the regressor and also assigns reward for the data points which lie inside of the -tube of…
New methods combine model predictions to avoid linear mixtures' limitations.
In this work, we propose a generalized product of experts (gPoE) framework for combining the predictions of multiple probabilistic models. We identify four desirable properties that are important for scalability, expressiveness and robustness, when learning and inferring with a combination of multiple models. Through a…
Combines datasets to improve model fitting with small sample sizes.
Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are distributed across the nodes of the network. We propose a general approach for distributed learning based on combining local estimators defined by pseudo-likelihood components, encompassing a num…
Recommendation systems and computing advertisements have gradually entered the field of academic research from the field of commercial applications. Click-through rate prediction is one of the core research issues because the prediction accuracy affects the user experience and the revenue of merchants and platforms. Fe…
This project combines recent advances in experience replay techniques, namely, Combined Experience Replay (CER), Prioritized Experience Replay (PER), and Hindsight Experience Replay (HER). We show the results of combinations of these techniques with DDPG and DQN methods. CER always adds the most recent experience to th…
In unsupervised outlier ensembles, the absence of ground truth makes the combination of base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles lack a reliable way of selecting competent base detectors, affecting accuracy and stability, during model combination. In this paper, we pr…
Combines linear and spectral estimators for signal recovery in generalized linear models.
Combining forecasts of 16 ED causes improves accuracy and stability.
One of the promising methods for the treatment of complex diseases such as cancer is combinational therapy. Due to the combinatorial complexity, machine learning models can be useful in this field, where significant improvements have recently been achieved in determination of synergistic combinations. In this study, we…
LOL method simplifies forming linear combinations of latent variables.
It is typical for a machine learning system to have numerous hyperparameters that affect its learning rate and prediction quality. Finding a good combination of the hyperparameters is, however, a challenging job. This is mainly because evaluation of each combination is extremely expensive computationally; indeed, train…
Background: The problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered in this manuscript. Methods: Novel kernels over drug combinations of arbitrary orders are developed within support vector machines for the prediction. Graph matching methods are …
Constructs correspondences on hyperelliptic surfaces combining orbifold groups and Blaschke products.
ITCA optimizes label combination for ambiguous outcomes in multi-class classification.
Google's multilingual speech recognition system combines low-level acoustic signals with language-specific recognizer signals to better predict the language of an utterance. This paper presents our experience with different signal combination methods to improve overall language identification accuracy. We compare the p…
For a linear combination of random variables, fix some confidence level and consider the quantile of the combination at this level. We are interested in the partial derivatives of the quantile with respect to the weights of the random variables in the combination. It turns out that under suitable conditions on the join…
This paper improves combine harvester performance using ANN-PSO hybrid model.
Combining Bayesian deep learning and split conformal prediction affects out-of-distribution coverage.
New model generates unseen attribute combinations from limited data.
Study continuous paths in discrete subgroups of hyperbolic space, proving combination and decomposition theorems.
This paper explores methods for combining predictions in multilabel classification.
Combines expert models using Kullback-Leibler divergence to create a combined model.
AI agents improve forecast combination in empirical economics.