arXiv research

New asynchronous algorithms improve speed in decentralized optimization networks.

Event-driven features improve forex price prediction accuracy.

REST framework predicts stock trends by considering stock-specific and related-stock events.

Most automation in machine learning focuses on model selection and hyper parameter tuning, and many overlook the challenge of automatically defining predictive tasks. We still heavily rely on human experts to define prediction tasks, and generate labels by aggregating raw data. In this paper, we tackle the challenge of…

Paper uncovers causal structures in Hawkes processes with latent subprocesses.

PredictionMarketBench benchmarks trading agents on prediction markets.

Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space. However, existing NRL methods ignore the impact of properties of relations on the object relevance in heterogeneous information networks (HINs). To tackl…

This paper describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT). POAP is an event-driven framework for building and combining asynchronous optimization strategies, designed for global optimization of expensive functions where concurrent function …

Paper proposes a new trading strategy using corporate event detection from news articles.

Proposes a neural LOB model for market-making.

The paper challenges the assumption of a unique global time in financial markets, highlighting market incompleteness.

This paper introduces a new learning rule for probabilistic SNNs that improves log-likelihood, accuracy, and calibration.

Summarizes financial news for better investment decisions.

Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, visual odometry, etc. The main flavors of neural networks which are used commonly are convolutional (CNN) and recurrent (RNN). In spite of rap…

The event-driven and elastic nature of serverless runtimes makes them a very efficient and cost-effective alternative for scaling up computations. So far, they have mostly been used for stateless, data parallel and ephemeral computations. In this work, we propose using serverless runtimes to solve generic, large-scale …

Optimizes search times by resetting agents when a threshold is reached.

The team predicts foreign exchange rates using clustering and attention models.

Exact simulation method for market impact estimation under various execution strategies.

This work optimizes DNN inference for energy-harvesting devices by compressing and selectively executing neural network exits.

We revisit and demonstrate the Epps effect using two well-known non-parametric covariance estimators; the Malliavin and Mancino (MM), and Hayashi and Yoshida (HY) estimators. We show the existence of the Epps effect in the top 10 stocks from the Johannesburg Stock Exchange (JSE) by various methods of aggregating Trade …

The study uses financial events to predict stock market movements.

Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the even…

Framework detects tipping points in complex systems using ML.

Spiking Neural Networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be levera…

This paper models short rates with jumps using PDEs.

We propose reinforcement learning on simple networks consisting of random connections of spiking neurons (both recurrent and feed-forward) that can learn complex tasks with very little trainable parameters. Such sparse and randomly interconnected recurrent spiking networks exhibit highly non-linear dynamics that transf…

Spiking neural networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be levera…

Paper proposes efficient weight updates for edge nodes with minimal communication.

Fan tokens surged before World Cup matches, but declined during them, revealing cognitive biases.

A new update rule for deep reinforcement learning reduces learning variance and variance in reference signals.

We build an agent-based model to study how the interplay between low- and high-frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates flash crashes. In the model, low-frequency agents adopt trading rules based on chronol…

Paper proves Jeffrey's update rule minimizes relative entropy.

AMUSE uses reinforcement learning to predict optimal model updates.

We shed new insights on the two commonly used updates for the online $k$-PCA problem, namely, Krasulina's and Oja's updates. We show that Krasulina's update corresponds to a projected gradient descent step on the Stiefel manifold of the orthonormal $k$-frames, while Oja's update amounts to a gradient descent step using…

Study quantifies systemic risk in DeFi using network analysis.

Efficiently updates posterior tree distributions over meta-trees.

Recently, the technique of local updates is a powerful tool in centralized settings to improve communication efficiency via periodical communication. For decentralized settings, it is still unclear how to efficiently combine local updates and decentralized communication. In this work, we propose an algorithm named as L…

The paper examines how updates to probabilistic models influence behavior based on evidence.

Paper improves policy updates in reinforcement learning to speed up learning.

In this paper, we study the randomized distributed coordinate descent algorithm with quantized updates. In the literature, the iteration complexity of the randomized distributed coordinate descent algorithm has been characterized under the assumption that machines can exchange updates with an infinite precision. We con…

Proposes a new method for nonlinear Bayesian updates using ensemble kernel regression.

This paper analyzes how periodic and soft target updates stabilize linear Q-learning.

In this letter, we generalize the convolutional NMF by taking the $β$-divergence as the contrast function and present the correct multiplicative updates for its factors in closed form. The new updates unify the $β$-NMF and the convolutional NMF. We state why almost all of the existing updates are inexact and approximat…

EnKF's update is shown to be similar to Matheron's method in Gaussian process regression.

Improved HGF networks avoid negative precision errors in volatility updates.

Federated learning is a distributed learning method to train a shared model by aggregating the locally-computed gradient updates. In federated learning, bandwidth and privacy are two main concerns of gradient updates transmission. This paper proposes an end-to-end encrypted neural network for gradient updates transmiss…

Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications. As we try to solve more advanced problems, increasing demands for computing and power resources has become inevitable. Spiking neural networks (SNNs) have attracted widespread interest as the third-g…

Algorithm estimates bounds of updated classifier coefficients efficiently.