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.
Quantum computing promises faster finance algorithms.
Quantum computing offers a quadratic speedup for estimating non-linear functionals.
With the increase in the amount of data and the expansion of model scale, distributed parallel training becomes an important and successful technique to address the optimization challenges. Nevertheless, although distributed stochastic gradient descent (SGD) algorithms can achieve a linear iteration speedup, they are l…
A central task in the field of quantum computing is to find applications where quantum computer could provide exponential speedup over any classical computer. Machine learning represents an important field with broad applications where quantum computer may offer significant speedup. Several quantum algorithms for discr…
Quantum algorithm speeds up nested expectation estimation by nearly quadratically.
ML-EM method speeds up diffusion model sampling.
Speeds up deep neural networks training by 10x using GPU concurrency.
Chemical transport models (CTMs), which simulate air pollution transport, transformation, and removal, are computationally expensive, largely because of the computational intensity of the chemical mechanisms: systems of coupled differential equations representing atmospheric chemistry. Here we investigate the potential…
ECD algorithm speeds up non-convex optimization, offering quantum and stochastic enhancements.
The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typically assume {\it all the data are already in one place}, and divide the data and conquer on multiple machines. However, it is increasingly ofte…
System learns optimizer hyperparameters to generalize across tasks.
SliceOut speeds up deep learning training without sacrificing accuracy.
New GPU kernels boost deep learning speed and memory efficiency.
Machine Learning as a Service (MLaaS) is enabling a wide range of smart applications on end devices. However, such convenience comes with a cost of privacy because users have to upload their private data to the cloud. This research aims to provide effective and efficient MLaaS such that the cloud server learns nothing …
We develop parallel and distributed Frank-Wolfe algorithms; the former on shared memory machines with mini-batching, and the latter in a delayed update framework. Whenever possible, we perform computations asynchronously, which helps attain speedups on multicore machines as well as in distributed environments. Moreover…
Improved neural framework for scaling entropic MOT with significant computational gains.
High demand for computation resources severely hinders deployment of large-scale Deep Neural Networks (DNN) in resource constrained devices. In this work, we propose a Structured Sparsity Learning (SSL) method to regularize the structures (i.e., filters, channels, filter shapes, and layer depth) of DNNs. SSL can: (1) l…
Quantum algorithm speeds up MIP solving by a near-quadratic factor.
DASA speeds up SA with delayed agents, achieving N-fold speedup.
The realized stochastic volatility (RSV) model that utilizes the realized volatility as additional information has been proposed to infer volatility of financial time series. We consider the Bayesian inference of the RSV model by the Hybrid Monte Carlo (HMC) algorithm. The HMC algorithm can be parallelized and thus per…
Quantum algorithm speeds up learning from big data exponentially.
Quantum algorithms speed up derivative pricing beyond Black-Scholes models.
Quantum computing speeds up linear regression training.
A new scheme reduces global search cost by a square root factor.
Efficiently approximates Sparse PCA with significant speedups and minor error.
Quantum algorithm estimates mean with sub-Gaussian error.
COPML framework securely trains models across multiple data owners without revealing individual data.
Photonic chip speeds up option pricing with GAN for financial efficiency.
The key cryptographic protocols used to secure the internet and financial transactions of today are all susceptible to attack by the development of a sufficiently large quantum computer. One particular area at risk are cryptocurrencies, a market currently worth over 150 billion USD. We investigate the risk of Bitcoin, …
New method speeds up model selection for complex scientific tasks.
FastKCI speeds up KCI tests for causal inference on large datasets.
Neural networks offer high-accuracy solutions to a range of problems, but are costly to run in production systems because of computational and memory requirements during a forward pass. Given a trained network, we propose a techique called Deep Learning Approximation to build a faster network in a tiny fraction of the …
Improved K-Means++ and K-Means with faster run-time.
ProxSkip achieves linear speedup in distributed non-convex optimization.
In this work we show that randomized (block) coordinate descent methods can be accelerated by parallelization when applied to the problem of minimizing the sum of a partially separable smooth convex function and a simple separable convex function. The theoretical speedup, as compared to the serial method, and referring…
Convolutional Neural Network (CNN) based Deep Learning (DL) has achieved great progress in many real-life applications. Meanwhile, due to the complex model structures against strict latency and memory restriction, the implementation of CNN models on the resource-limited platforms is becoming more challenging. This work…
Fast estimates of model uncertainty are required for many robust robotics applications. Deep Ensembles provides state of the art uncertainty without requiring Bayesian methods, but still it is computationally expensive. In this paper we propose deep sub-ensembles, an approximation to deep ensembles where the core idea …
Safe screening rule reduces computational costs for Group OWL models.
Asynchronous parallel implementations of stochastic gradient (SG) have been broadly used in solving deep neural network and received many successes in practice recently. However, existing theories cannot explain their convergence and speedup properties, mainly due to the nonconvexity of most deep learning formulations …
HybridSGD improves SGD performance by balancing computation and communication.
Computations for the softmax function are significantly expensive when the number of output classes is large. In this paper, we present a novel softmax inference speedup method, Doubly Sparse Softmax (DS-Softmax), that leverages sparse mixture of sparse experts to efficiently retrieve top-k classes. Different from most…
We present a parallel algorithm that computes the ask and bid prices of an American option when proportional transaction costs apply to the trading of the underlying asset. The algorithm computes the prices on recombining binomial trees, and is designed for modern multi-core processors. Although parallel option pricing…
A fast method for Lasso and Logistic Lasso problems.
MIMONets speed up neural network inference by processing multiple inputs in parallel.
The Graph Convolutional Network (GCN) model and its variants are powerful graph embedding tools for facilitating classification and clustering on graphs. However, a major challenge is to reduce the complexity of layered GCNs and make them parallelizable and scalable on very large graphs -- state-of the art techniques a…
New insights explain speedup saturation in distributed learning with large batches and delays.
SparseRT accelerates sparse computations on GPUs for deep learning inference.
Novel algorithm speeds up log-determinant estimation for large matrices.