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.
The paper predicts run times for Gaussian chemistry code.
Clusters of withdrawals emerge in banks due to latent fragility.
New compression technique reduces RNN size by 2-4x without sacrificing accuracy.
New method reduces neural image compression run-time by 50%.
Machine learning improves accuracy of running gait event detection from tibial acceleration.
Study optimal control of diffusion processes with infimum or supremum costs.
Improved SVRG for quadratic functions achieves better performance and running times.
Paper analyzes Hit-and-Run's convergence rates and applies similar methods to randomized Kaczmarz.
ATSDLN adapts to time series data for anomaly detection.
What predicts the evolution over time of subjective well-being? We correlate the trends of subjective well-being with the trends of social capital and/or GDP. We find that in the long and medium run social capital largely predicts the trends of subjective wellbeing in our sample of countries. In the short-term this rel…
AITuning uses reinforcement learning to optimize communication libraries.
We study -GenEV, the problem of finding the top generalized eigenvectors, and -CCA, the problem of finding the top vectors in canonical-correlation analysis. We propose algorithms and to solve the two problems with running times linearly dependent on the input size and…
New algorithms improve agnostic learning for triangles and polygons, reducing time complexity.
This paper develops coding techniques to reduce the running time of distributed learning tasks. It characterizes the fundamental tradeoff to compute gradients (and more generally vector summations) in terms of three parameters: computation load, straggler tolerance and communication cost. It further gives an explicit c…
Efficiently learns complex Boolean functions under Gaussian distributions.
Study uses artificial counterfactuals to show lockdowns reduced US case and death counts.
We propose a real-time context-aware learning system along with the architecture that runs on the mobile devices, provide services to the user and manage the IoT devices. In this system, an application running on mobile devices collected data from the sensors, learned about the user-defined context, made predictions in…
New algorithms speed up American option pricing significantly.
Drawdowns measuring the decline in value from the historical running maxima over a given period of time, are considered as extremal events from the standpoint of risk management. To date, research on the topic has mainly focus on the side of severity by studying the first drawdown over certain pre-specified size. In th…
This paper concentrates on the time series momentum or contrarian effects in the Chinese stock market. We evaluate the performance of the time series momentum strategy applied to major stock indices in mainland China and explore the relation between the performance of time series momentum strategies and some firm-speci…
New algorithms solve dense linear systems with low-rank structure efficiently.
Improved K-Means++ and K-Means with faster run-time.
We study the portfolio selection problem of a long-run investor who is maximising the asymptotic growth rate of her expected utility. We show that, somewhat surprisingly, it is essentially not affected by introduction of a floor constraint which requires the wealth process to dominate a given benchmark at all times. We…
We consider the problem of configuring general-purpose solvers to run efficiently on problem instances drawn from an unknown distribution. The goal of the configurator is to find a configuration that runs fast on average on most instances, and do so with the least amount of total work. It can run a chosen solver on a r…
Randomly chosen support makes sparse linear regression easy.
We propose a novel coding theoretic framework for mitigating stragglers in distributed learning. We show how carefully replicating data blocks and coding across gradients can provide tolerance to failures and stragglers for Synchronous Gradient Descent. We implement our schemes in python (using MPI) to run on Amazon EC…
Belief propagation and its variants are popular methods for approximate inference, but their running time and even their convergence depend greatly on the schedule used to send the messages. Recently, dynamic update schedules have been shown to converge much faster on hard networks than static schedules, namely the res…
Long short-term memory (LSTM) has been widely used for sequential data modeling. Researchers have increased LSTM depth by stacking LSTM cells to improve performance. This incurs model redundancy, increases run-time delay, and makes the LSTMs more prone to overfitting. To address these problems, we propose a hidden-laye…
This paper presents quantum and classical algorithms for approximate submodular function minimization.
Extended model ensures long-term survival of traders in limited stock market participation.
Faster algorithms estimate robust covariance in high dimensions.
Algorithm classifies surface homeomorphisms with polynomial time complexity.
We study the problem of learning an unknown mixture of rankings over elements, given access to noisy samples drawn from the unknown mixture. We consider a range of different noise models, including natural variants of the "heat kernel" noise framework and the Mallows model. For each of these noise models we giv…
Bayesian optimization for long-term outcomes using fast and slow experiments.
Quantum version of C5.0 algorithm improves decision tree construction time.
Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in advance the trend of running process instances would allow business managers to react in time, in order to prevent delays or undesirable situ…
Paper studies long-run risk optimization with dyadic impulses for unbounded processes.
In-Run Data Shapley offers efficient data attribution for large-scale models.
Given a finite honest time, we first show that the associated Azéma optional supermartingale can be expressed as the drawdown and the relative drawdown of some local optional supermartingales with continuous running supremum. The relative drawdown representation then allows us to provide a characterisation of finite ho…
Efficient algorithms improve learning of large-margin halfspaces.
New algorithm speeds up polynomial kernel approximations.
We test for the long-run relationship between stock prices, inflation and its uncertainty for different U.S. sector stock indexes, over the period 2002M7 to 2015M10. For this purpose we use a cointegration analysis with one structural break to capture the crisis effect, and we assess the inflation uncertainty based on …
Edge language models show bias over time, especially on resource-constrained devices.
We study solvency of insurers in a comprehensive model where various economic factors affect the capital developments of the companies. The main interest is in the impact of real growth to ruin probabilities. The volume of the business is allowed to increase or decrease. In the latter case, the study is focused on run-…
Detects and mitigates rare subclasses in deep neural networks.
We give the first algorithm for Matrix Completion whose running time and sample complexity is polynomial in the rank of the unknown target matrix, linear in the dimension of the matrix, and logarithmic in the condition number of the matrix. To the best of our knowledge, all previous algorithms either incurred a quadrat…
Advances in sensing technologies and the growth of the internet have resulted in an explosion in the size of modern datasets, while storage and processing power continue to lag behind. This motivates the need for algorithms that are efficient, both in terms of the number of measurements needed and running time. To comb…
One approach to improving the running time of kernel-based machine learning methods is to build a small sketch of the input and use it in lieu of the full kernel matrix in the machine learning task of interest. Here, we describe a version of this approach that comes with running time guarantees as well as improved guar…