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.
rMCL improves on MCL by preserving diversity in predictions for regression problems.
This paper introduces resilient constrained learning to adapt learning constraints while solving machine learning tasks.
We show that wealth processes in the block-shaped order book model of Obizhaeva/Wang converge to their counterparts in the reduced-form model proposed by Almgren/Chriss, as the resilience of the order book tends to infinity. As an application of this limit theorem, we explain how to reduce portfolio choice in highly-re…
Machine learning has begun to play a central role in many applications. A multitude of these applications typically also involve datasets that are distributed across multiple computing devices/machines due to either design constraints (e.g., multiagent systems) or computational/privacy reasons (e.g., learning on smartp…
Machine Learning (ML) and Deep Learning (DL) models have achieved state-of-the-art performance on multiple learning tasks, from vision to natural language modelling. With the growing adoption of ML and DL to many areas of computer science, recent research has also started focusing on the security properties of these mo…
We consider optimal execution strategies for block market orders placed in a limit order book (LOB). We build on the resilience model proposed by Obizhaeva and Wang (2005) but allow for a general shape of the LOB defined via a given density function. Thus, we can allow for empirically observed LOB shapes and obtain a n…
Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as autonomous vehicles, demands a reliable and efficient execution on hardware. Optimized dedicated hardware accelerators are being developed to achieve this. However, the design of efficient and reliable hardware has become increasingly d…
We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlated or non-predictive features learned by the classifier during training and design the adversarial noise to utilize these features. Therefore…
Machine Learning (ML) solutions are nowadays distributed and are prone to various types of component failures, which can be encompassed in so-called Byzantine behavior. This paper introduces LiuBei, a Byzantine-resilient ML algorithm that does not trust any individual component in the network (neither workers nor serve…
New method uses Multiple Choice Learning for speech separation.
Applications in machine learning, optimization, and control require the sequential selection of a few system elements, such as sensors, data, or actuators, to optimize the system performance across multiple time steps. However, in failure-prone and adversarial environments, sensors get attacked, data get deleted, and a…
Paper tackles Byzantine resilience in distributed multi-task learning.
CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.
Neuromorphic-style inference only works well if limited hardware resources are maximized properly, e.g. accuracy continues to scale with parameters and complexity in the face of potential disturbance. In this work, we use realistic crossbar simulations to highlight that compact implementations of deep neural networks a…
The intrinsic error tolerance of neural network (NN) makes approximate computing a promising technique to improve the energy efficiency of NN inference. Conventional approximate computing focuses on balancing the efficiency-accuracy trade-off for existing pre-trained networks, which can lead to suboptimal solutions. In…
Paper introduces a new index to measure financial and workplace resilience of firms.
This paper investigates the resilience and robustness of Deep Reinforcement Learning (DRL) policies to adversarial perturbations in the state space. We first present an approach for the disentanglement of vulnerabilities caused by representation learning of DRL agents from those that stem from the sensitivity of the DR…
Improved deep neural network generalization through noise resilience.
A new federated learning method speeds up training by selecting faster nodes first.
Noise-resilient method improves Hurst exponent estimation accuracy in noisy data.
Measures financial resilience using BSDEs and their properties.
We solve the superhedging problem for European options in an illiquid extension of the Black-Scholes model, in which transactions have transient price impact and the costs and the strategies for hedging are affected by physical or cash settlement requirements at maturity. Our analysis is based on a convenient choice of…
This paper defines resilience in knowledge graph embeddings and surveys existing works.
Secure federated learning framework resists adversarial users.
Python tool assesses European agricultural production resilience.
Predictive Q-learning algorithm for IoT networks with human operators.
Decentralized ranking consensus via gossip for robust and scalable systems.
This paper measures financial market resilience in China and identifies key uncertainties.
Federated Learning aims to train distributed deep models without sharing the raw data with the centralized server. Similarly, in distributed inference of neural networks, by partitioning the network and distributing it across several physical nodes, activations and gradients are exchanged between physical nodes, rather…
Ensemble methods are arguably the most trustworthy techniques for boosting the performance of machine learning models. Popular independent ensembles (IE) relying on naive averaging/voting scheme have been of typical choice for most applications involving deep neural networks, but they do not consider advanced collabora…
The resilience of low-degree Rademacher chaos is studied, providing probabilistic lower bounds.
Deviation-based learning improves recommender systems by abstaining from recommending choices users might follow.
TensorFI injects faults in TensorFlow programs to assess their reliability.
New model shows negative resilience can improve trading efficiency.
aMCL uses annealing to improve hypothesis diversity in ambiguous tasks.
LoRA-MCL improves language models by generating diverse sentence continuations.
We study a multiplicative transient price impact model for an illiquid financial market, where trading causes price impact which is multiplicative in relation to the current price, transient over time with finite rate of resilience, and non-linear in the order size. We construct explicit solutions for the optimal contr…
Proposes resilience metrics for large blackout costs with logarithmic resilience.
Stocks of more resilient firms outperformed during the pandemic, reflecting disaster risk.
RESIST improves decentralized learning resilience against MITM attacks.
With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to mi…
Financial markets can be seen as complex systems that are constantly evolving and sensitive to external disturbance, such as systemic risks and economic instabilities. Analysis of resilient market performance, therefore, becomes useful for investors. From a systems perspective, this paper proposes a novel function-base…
Novel algorithm resists Byzantine attacks in federated learning for PCA and LRCS.
New method quantifies resilience of electric distribution systems from historical data.
Byzantine-resilient federated learning with local iterations and robust mean estimation.
This paper develops a stochastic learning-optimization model for resilient automotive supply chains.
Study uses machine learning to optimize stock trading strategies.
There has been a surge of interest in using machine learning (ML) to automatically detect malware through their dynamic behaviors. These approaches have achieved significant improvement in detection rates and lower false positive rates at large scale compared with traditional malware analysis methods. ML in threat dete…