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.
NOTMAD estimates context-specific Bayesian networks without breaking datasets.
Investigates physical properties on surfaces of rotation using Clairaut's theorem.
Enhances demonstrations with safety specifications using LTL.
In this paper we formulate a probabilistic model for class-specific discriminant subspace learning. The proposed model can naturally incorporate the multi-modal structure of the negative class, which is neglected by existing class-specific methods. Moreover, it can be directly used to define a class-specific probabilis…
Introduces CStrees for modeling context-specific causal models from observational and interventional data.
In this note we study whether specific elements in the second homology of specific simply connected closed -manifolds can be represented by smooth or topologically flat embedded spheres.
Statistical model checking for PCTL on MDPs using reinforcement learning.
Prior work on neural network verification has focused on specifications that are linear functions of the output of the network, e.g., invariance of the classifier output under adversarial perturbations of the input. In this paper, we extend verification algorithms to be able to certify richer properties of neural netwo…
New bounds on specific torsion lengths for periodic mapping classes.
New model identifies patient-specific disease root causes.
Formulates approach for guiding explanation types based on user specifications.
Safe neural networks for input-output specifications.
A site-specific Gordian distance between two spatial embeddings of an abstract graph is the minimal number of crossing changes from one to another where each crossing change is performed between two previously specified abstract edges of the graph. It is infinite in some cases. We determine the site-specific Gordian di…
IFGAN uses feature-specific GANs for missing value imputation.
A novel validation method improves feature importance analysis in subject-specific ML models.
We address the problem of tuning word embeddings for specific use cases and domains. We propose a new method that automatically combines multiple domain-specific embeddings, selected from a wide range of pre-trained domain-specific embeddings, to improve their combined expressive power. Our approach relies on two key c…
Site-specific recombination is an enzymatic process where two sites of precise sequence and orientation along a circle come together, are cleaved, and the ends are recombined. Site-specific recombination on a knotted substrate produces another knot or a two-component link depending on the relative orientation of the si…
Region-specific linear models are widely used in practical applications because of their non-linear but highly interpretable model representations. One of the key challenges in their use is non-convexity in simultaneous optimization of regions and region-specific models. This paper proposes novel convex region-specific…
Extends specific relative entropy to multidimensional continuous martingales.
Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
Scalable method learns context-specific models for hundreds of variables.
This work defines observation-specific explanations for black-box models.
Research in deep learning for multi-speaker source separation has received a boost in the last years. However, most studies are restricted to mixtures of a specific number of speakers, called a specific scenario. While some works included experiments for different scenarios, research towards combining data of different…
Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed choices can be a very challenging and time-consuming task. This …
Efficiently fine-tunes patient-independent seizure detection models with tensor kernel machine.
Site-specific recombination on supercoiled circular DNA molecules can yield a variety of knots and catenanes. Twist knots are some of the most common conformations of these products and they can act as substrates for further rounds of site-specific recombination. They are also one of the simplest families of knots and …
CSD learns a common component for domain generalization, outperforming existing methods.
Study evaluates if LLMs have company-specific biases in financial sentiment analysis.
QuantNet learns global market trends to improve trading strategies.
cube2net efficiently constructs query-specific networks using data cube technology.
In this paper we propose a boosting based multiview learning algorithm, referred to as PB-MVBoost, which iteratively learns i) weights over view-specific voters capturing view-specific information; and ii) weights over views by optimizing a PAC-Bayes multiview C-Bound that takes into account the accuracy of view-specif…
Low-rank framework for task-specific LLM ranking from sparse comparisons.
This paper aims to optimize incident-specific cyber insurance design.
CosML combines domain-specific meta-learners for cross-domain few-shot classification.
Interventional domain adaptation improves feature transferability by removing spurious correlations.
Computer algorithms are written with the intent that when run they perform a useful function. Typically any information obtained is unknown until the algorithm is run. However, if the behavior of an algorithm can be fully described by precomputing just once how this algorithm will respond when executed on any input, th…
New definition of patient-specific root causes of disease using counterfactuals.
Study shows specific states produce Khovanov homology torsion.
In this paper, we propose a deep multimodal fusion network to fuse multiple modalities (face, iris, and fingerprint) for person identification. The proposed deep multimodal fusion algorithm consists of multiple streams of modality-specific Convolutional Neural Networks (CNNs), which are jointly optimized at multiple fe…
Study introduces KorFinMTEB for Korean financial texts, revealing model limitations.
Identifies patient-specific root causes of disease using structural equation models.
Recent advancements in language representation models such as BERT have led to a rapid improvement in numerous natural language processing tasks. However, language models usually consist of a few hundred million trainable parameters with embedding space distributed across multiple layers, thus making them challenging t…
Study proposes a machine learning method to predict stock price crashes based on investor sentiment.
Many real-world problems, including multi-speaker text-to-speech synthesis, can greatly benefit from the ability to meta-learn large models with only a few task-specific components. Updating only these task-specific modules then allows the model to be adapted to low-data tasks for as many steps as necessary without ris…
Log-linear models are the popular workhorses of analyzing contingency tables. A log-linear parameterization of an interaction model can be more expressive than a direct parameterization based on probabilities, leading to a powerful way of defining restrictions derived from marginal, conditional and context-specific ind…
Graph data widely exist in many high-impact applications. Inspired by the success of deep learning in grid-structured data, graph neural network models have been proposed to learn powerful node-level or graph-level representation. However, most of the existing graph neural networks suffer from the following limitations…
The paper presents a novel approach to direct covariance function learning for Bayesian optimisation, with particular emphasis on experimental design problems where an existing corpus of condensed knowledge is present. The method presented borrows techniques from reproducing kernel Banach space theory (specifically m-k…
Configuration spaces for computer systems can be challenging for traditional and automatic tuning strategies. Injecting task-specific knowledge into the tuner for a task may allow for more efficient exploration of candidate configurations. We apply this idea to the task of index set selection to accelerate database wor…