This study examines the interaction between CDS and stock indices, revealing significant short and long-term impacts.
problem Understanding the interaction between Credit Default Swaps (CDS) and national stock indices.
method ARDL technique applied to analyze short and long-run interactions between BIST-100 index and CDS prices over a specific period.
result The study finds that changes in CDS and BIST-100 index prices have significant impacts on each other, with long-term effects being more pronounced.
This paper studies a curious phenomenon in learning energy-based model (EBM) using MCMC. In each learning iteration, we generate synthesized examples by running a non-convergent, non-mixing, and non-persistent short-run MCMC toward the current model, always starting from the same initial distribution such as uniform no…
Machine learning improves measuring climate adaptation impacts.
problem Measuring adaptation to climate change using weather damage elasticities.
method Debiased machine learning approach in panel data settings.
result Long-run impacts of damaging heat exposure significantly offset short-run impacts.
Accurate estimation of the run time of computational codes has a number of significant advantages for scientific computing. It is required information for optimal resource allocation, improving turnaround times and utilization of science gateways. Furthermore, it allows users to better plan and schedule their research,…
Improved canary crafting for one-run privacy auditing reduces leakage estimates.
problem Detecting canaries in one-run privacy auditing to estimate leakage effectively.
method Optimizes canaries for detectability and diversity, using a greedy initialization and bilevel optimization.
result Achieves stronger leakage estimates at lower computational cost.
This paper uses SDEs to analyze GANs training and long-run behavior.
problem Understanding the training process and long-run behavior of GANs.
method Established SDE approximations for GANs training and analyzed long-run behavior via invariant measures.
result The long-run behavior of GANs training can be studied via the invariant measures of its SDE approximations.
Study shows climate change can cause a 'run on fossil fuels' affecting prices and production.
problem Impact of climate change expectations on fossil fuel markets and prices.
method Dynamic, general equilibrium model of climate-change-linked transition risk.
result Climate change expectations can lead to either increased or decreased fossil fuel prices, depending on economic responses.
New framework evaluates MIA without retraining, addressing biases.
problem Evaluate MIA without retraining and under distribution shift.
method Causal inference approach to MIA evaluation.
result Practical estimators for MIA metrics without retraining.
Paper analyzes Hit-and-Run's convergence rates and applies similar methods to randomized Kaczmarz.
problem Quantifying advantages of Hit-and-Run's coordinate-free property.
method Sharp estimates via coupling methods and mixing time bounds.
result Ballistic and superdiffusive convergence rates in certain settings.
Study optimal control of diffusion processes with infimum or supremum costs.
problem Optimizing control of a diffusion process with costs dependent on its infimum or supremum.
method Introduced novel integral operators to solve two-dimensional singular control problems.
result Explicit solutions for optimal dividend problem with time-dependent preferences.
Analysis finds no evidence of banks managing deposit run risk prior to 2023 Regional Banking Crisis.
problem Determining factors for deposit run risk management before a regional banking crisis.
method Cross-sectional analysis of interest rate and equity use by banks.
result No evidence of banks managing deposit run risk via their balance sheet.
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…
Recurrent neural networks can be large and compute-intensive, yet many applications that benefit from RNNs run on small devices with very limited compute and storage capabilities while still having run-time constraints. As a result, there is a need for compression techniques that can achieve significant compression wit…
For organizing large text corpora topic modeling provides useful tools. A widely used method is Latent Dirichlet Allocation (LDA), a generative probabilistic model which models single texts in a collection of texts as mixtures of latent topics. The assignments of words to topics rely on initial values such that general…
In-Run Data Shapley offers efficient data attribution for large-scale models.
problem Existing data attribution methods are computationally intensive and cannot target specific models.
method In-Run Data Shapley, which efficiently attributes data contributions to a specific model without re-training.
result In-Run Data Shapley achieves significant efficiency, enabling data attribution for pretraining models.
Model explains how stablecoin runs are influenced by large sales and reserve quality.
problem Understanding and predicting stablecoin runs due to large sales and poor reserve quality.
method Global game model addressing both large sales and poor reserve quality, analyzing risk components.
result The probability of a run increases with large sales and decreases with precise public knowledge, but increases with precise private signals when fundamentals are weak.
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 …
LLMs fail to match statistical ground truth despite stable run-to-run performance.
problem LLMs lack validation against statistical ground truth in automated scientific workflows.
method Introduced a behavioral evaluation framework for LLMs, separating four decision-making dimensions.
result LLMs can exhibit near-perfect stability but diverge from statistical ground truth.
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…
Trading styles affect long-run variance of asset prices, increasing under trend-following and decreasing under mean-reverting.
problem Understanding how different trading styles impact the long-run variance of asset prices.
method Probabilistic models designed to capture the direction of trading were used.
result Trading styles increase long-run variance under trend-following and decrease it under mean-reverting conditions.
Mixed datasets consist of both numeric and categorical attributes. Various k-means-based clustering algorithms have been developed for these datasets. Generally, these algorithms use random partition as a starting point, which tends to produce different clustering results for different runs. In this paper, we propose, …
Improves Monte-Carlo simulations for consistent mean and variance.
problem Artificial randomness in running mean calculations.
method Combining running mean and variance with accurate summing.
result Increased accuracy and robustness of Monte-Carlo estimates.
This paper develops a method to derive optimal portfolios and risk premia explicitly in a general diffusion model for an investor with power utility and a long horizon. The market has several risky assets and is potentially incomplete. Investment opportunities are driven by, and partially correlated with, state variabl…
Improved SVRG for quadratic functions achieves better performance and running times.
problem Minimizing quadratic functions with a specific type of Hessian matrix.
method Variant of SVRG algorithm for quadratic functions with improved analysis.
result Improved performance and running times for quadratic functions compared to state-of-the-art methods.
In this work, we address the problem of tuning communication libraries by using a deep reinforcement learning approach. Reinforcement learning is a machine learning technique incredibly effective in solving game-like situations. In fact, tuning a set of parameters in a communication library in order to get better perfo…
Model shows how banks' hidden-to-maturity accounting can mask run risk and lead to financial instability.
problem Run risk and hidden-to-maturity accounting in banking systems.
method Balance sheet model and optimization problem to assess run risk and resilience.
result Held-to-maturity accounting can mask revaluation losses and increase run risk.
The study identifies key factors affecting cryptocurrency prices, including market beta, trading volume, and volatility.
problem Understanding the factors influencing cryptocurrency prices and their dynamics over time.
method ARDL technique and error-correction models applied to weekly data of Bitcoin, Ethereum, Dash, Litecoin, and Monero from 2010-2018.
result Cryptomarket-related factors are significant determinants of cryptocurrency prices in both short- and long-run, while attractiveness matters only in the long-run.
Study uses artificial counterfactuals to show lockdowns reduced US case and death counts.
problem Impact of lockdowns on US case and death counts during the pandemic.
method Artificial counterfactual approach comparing states with and without lockdowns.
result Average two times more cases would have occurred without lockdowns.
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement lea…
Study shows HFT benefits large traders under certain conditions.
problem Influence of high-frequency traders (HFTs) on large traders.
method Analyzes the impact of HFT front-running on large traders under different conditions.
result HFT benefits large traders when there is high-speed noise trading and vague HFT predictions.
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…
We conduct an empirical study of machine learning functionalities provided by major cloud service providers, which we call machine learning clouds. Machine learning clouds hold the promise of hiding all the sophistication of running large-scale machine learning: Instead of specifying how to run a machine learning task,…
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…
We study k-GenEV, the problem of finding the top k generalized eigenvectors, and k-CCA, the problem of finding the top k vectors in canonical-correlation analysis. We propose algorithms LazyEV and LazyCCA to solve the two problems with running times linearly dependent on the input size and…
This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of unnormalized probability densities for which the negative log density (or energy function) is a ConvNet. We find that many of the techniques used…
In the paper portfolio optimization over long run risk sensitive criterion is considered. It is assumed that economic factors which stimulate asset prices are ergodic but non necessarily uniformly ergodic. Solution to suitable Bellman equation using local span contraction with weighted norms is shown. The form of optim…
Edge language models show bias over time, especially on resource-constrained devices.
problem Bias in edge language models on resource-constrained devices.
method Comparative analysis of text-based bias across edge, cloud, and desktop environments; optimized Llama-2 model on Raspberry Pi 4; feedback loop mechanism to correct bias.
result Llama-2 on Raspberry Pi 4 shows 43.23% and 21.89% more bias over time compared to cloud and desktop models.
Investigates long-term performance of multi-fidelity Bayesian optimization.
problem Potential long-term under-performance of multi-fidelity Bayesian optimization.
method Simple benchmark study to investigate long-term performance.
result Under-performance of multi-fidelity Bayesian optimization in certain scenarios.
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…
In this paper, we present our approach to solve a physics-based reinforcement learning challenge "Learning to Run" with objective to train physiologically-based human model to navigate a complex obstacle course as quickly as possible. The environment is computationally expensive, has a high-dimensional continuous actio…
The online problem of computing the top eigenvector is fundamental to machine learning. In both adversarial and stochastic settings, previous results (such as matrix multiplicative weight update, follow the regularized leader, follow the compressed leader, block power method) either achieve optimal regret but run slow,…
A new method learns latent space normalizing flow for approximate inference in generator models.
problem Approximate inference in generator models with complex posterior distributions.
method Jointly learns latent space normalizing flow and generator model using MCMC-based maximum likelihood.
result The short-run Langevin flow approximates the posterior and aligns with the normalizing flow prior.
Efficiently learns complex Boolean functions under Gaussian distributions.
problem Learning complex Boolean functions of halfspaces under Gaussian marginals.
method First efficient proper agnostic learning algorithm for arbitrary Boolean functions of K halfspaces.
result Matches the best known improper learning algorithm's run-time dependence on dimension.
We propose an algorithm, HPREF (Hierarchical Partitioning by Repeated Features), that produces a hierarchical partition of a set of clusterings of a fixed dataset, such as sets of clusterings produced by running a clustering algorithm with a range of parameters. This gives geometric structure to such sets of clustering…
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-…
Oil markets profoundly influence world economies through determination of prices of energy and transports. Using novel methodology devised in frequency domain, we study the information transmission mechanisms in oil-based commodity markets. Taking crude oil as a supply-side benchmark and heating oil and gasoline as dem…
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one of the most popular CI tests in the non-parametric setting, but many investigators cannot use KCIT with large datasets because the test sca…
Emulator speeds up landslide run-out modeling sensitivity analysis.
problem Computational challenges in assessing landslide run-out model sensitivity.
method Gaussian process emulation integrated into r.avaflow.
result Strong interactions detected between friction coefficients and release volume.