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.
Paper improves text-to-SQL models with schema-aware denoising.
A new method for uplift modeling using learning-to-rank techniques.
Symmetries of Poisson manifolds are in general quantized just to symmetries up to homotopy of the quantized algebra of functions. It is therefore interesting to study symmetries up to homotopy of Poisson manifolds. We notice that they are equivalent to Poisson principal bundles and describe their quantization to symmet…
This paper proposes learning to jump for generative modeling of sparse, skewed, heavy-tailed data.
Recurrent Neural Networks (RNNs) are powerful models for sequential data that have the potential to learn long-term dependencies. However, they are computationally expensive to train and difficult to parallelize. Recent work has shown that normalizing intermediate representations of neural networks can significantly im…
Deep RL methods improve resource allocation in uncertain environments.
This belongs to a series of papers devoted to the study of the cohomology of classifying spaces of Lie groupoids. Our aim here is to introduce and study the notion of representation up to homotopy of Lie groupoids, the resulting derived category, and to show that the adjoint representation is well defined as a represen…
Agent-based models, particularly those applied to financial markets, demonstrate the ability to produce realistic, simulated system dynamics, comparable to those observed in empirical investigations. Despite this, they remain fairly difficult to calibrate due to their tendency to be computationally expensive, even with…
This paper uses deep reinforcement learning to generate profitable trading signals in financial markets.
Improves text-to-SQL models by selecting the best SQL query from beam output.
Proposes isotonic recalibration for insurance pricing to ensure auto-calibration under low signal-to-noise ratio.
Recent research showed that deep neural networks are highly sensitive to so-called adversarial perturbations, which are tiny perturbations of the input data purposely designed to fool a machine learning classifier. Most classification models, including deep learning models, are highly vulnerable to adversarial attacks.…
Meta-learn causal structures based on adaptation speed to sparse distributional changes.
Proposes vMF distribution for skewed elliptical distributions.
Proposes a machine learning framework for more efficient economic dispatch.
BPE improves text-to-SQL generation by reducing training time and improving accuracy.
Proposes minimal interventions over counterfactual explanations for algorithmic recourse.
Model shows how banks' hidden-to-maturity accounting can mask run risk and lead to financial instability.
The paper finds obstructions to Lie algebroid representations up to homotopy.
Using neural networks in practical settings would benefit from the ability of the networks to learn new tasks throughout their lifetimes without forgetting the previous tasks. This ability is limited in the current deep neural networks by a problem called catastrophic forgetting, where training on new tasks tends to se…
Episodic memory is a psychology term which refers to the ability to recall specific events from the past. We suggest one advantage of this particular type of memory is the ability to easily assign credit to a specific state when remembered information is found to be useful. Inspired by this idea, and the increasing pop…
Ebay uses forecasting and simulation to decide when to disable a vendor.
The paper introduces invariants to describe period-doubling routes to chaos in dynamical systems.
End-to-end autonomous driving models get better uncertainty estimates.
Deep networks are able to learn highly predictive models of video data. Due to video length, a common strategy is to train them on small video snippets. We apply the deep Taylor / LRP technique to understand the deep network's classification decisions, and identify a "border effect": a tendency of the classifier to loo…
Noise injection improves inference privacy in DNN models.
Deep neural networks are susceptible to \emph{adversarial} attacks. In computer vision, well-crafted perturbations to images can cause neural networks to make mistakes such as confusing a cat with a computer. Previous adversarial attacks have been designed to degrade performance of models or cause machine learning mode…
Entropy regularization improves policy optimization in reinforcement learning.
Statistical analysis (SA) is a complex process to deduce population properties from analysis of data. It usually takes a well-trained analyst to successfully perform SA, and it becomes extremely challenging to apply SA to big data applications. We propose to use deep neural networks to automate the SA process. In parti…
Users can anticipate follower preferences by balancing feedback exploitation and exploration.
The goal of this article is to invite the reader to get to know and to get involved into higher Teichmüller theory by describing some of its many facets.
In order to reduce signalling, traders may resort to limiting access to dark venues and imposing limits on minimum fill sizes they are willing to trade. However, doing this also restricts the liquidity available to the trader since an ever increasing quantity of orders are traded by algos in clips. An alternative is to…
Study examines perceptions and attitudes about breast cancer on Twitter.
Homotopy theory applied to singular foliations leads to new results.
We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy. Incentivizing most players to truthfully report their data to the analyst constrains our design to mechanisms that provide a privacy guarantee to the participants; we use differential privacy to model in…
Study uses NLP to analyze emotions and challenges of young people with IDD.
End-to-end training of DBMs with improved gradient estimation.
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statisti…
We observe that any regular Lie groupoid G over an manifold M fits into an extension of a foliation groupoid E by a bundle of connected Lie groups K. If $\FF$ is the foliation on M given by the orbits of E and T is a complete transversal to $\FF$, this extension restricts to T, as an extension $K_{T}\to…
Paper explores MM strategies that can refuse to quote or provide single-sided quotes.
Given a compact Riemannian manifold, with positive Yamabe quotient, not conformally diffeomorphic to the standard sphere, we prove a priori estimates for solutions to the Yamabe problem. We restrict ourselves to the dimensions less than or equal to 7, where the Positive Mass Theorem is known to be true. We also show th…
We study the rates of convergence from empirical surrogate risk minimizers to the Bayes optimal classifier. Specifically, we introduce the notion of \emph{consistency intensity} to characterize a surrogate loss function and exploit this notion to obtain the rate of convergence from an empirical surrogate risk minimizer…
Proposes a method to quantify and explain deep learning model uncertainties.
SequenceR uses seq-to-seq learning to fix bugs in code.
This paper is split in three parts: first we use labelled trade data to exhibit how market participants accept or not transactions via limit orders as a function of liquidity imbalance; then we develop a theoretical stochastic control framework to provide details on how one can exploit his knowledge on liquidity imbala…
Bayesian optimisation generates saliency maps for black-box models.
Simulates DeLend Platform behavior to optimize operational parameters.
Study shows adversarial robustness and common perturbation robustness are independent.