Improves bandit regret with small loss range, even with limited information.
problem Improving bandit regret with small loss range.
method Develops a novel technique to convert algorithms with regret depending on loss range to ones with regret depending only on effective range.
result Shows how to improve bandit regret guarantees with small loss range under certain assumptions.
Study extends compactness theorems to weighted manifolds with integral curvature bounds.
problem Estimating diameter of weighted manifolds under curvature constraints.
method Extended Sprouse's compactness theorems to weighted manifolds with integral curvature bounds. Used ε-range to handle specific cases. Extended segment inequality to weighted manifolds.
result Proved theorems for weighted manifolds with effective dimension ≤ 1 and ≥ dimension.
Introduces Spectral Attention for better long-range time series forecasting.
problem Challenges in capturing long-range dependencies in time series forecasting.
method Spectral Attention mechanism that preserves temporal correlations and long-range dependencies.
result Achieves state-of-the-art results on 11 real-world time series datasets.
A multi-scale model predicts atomic-scale properties using both local and long-range information.
problem Inability of machine-learning schemes to capture long-range physical effects.
method Combines local and non-local information in a multipole expansion framework.
result Demonstrates the ability to model electrostatics, polarization, and dispersion.
Improved online learning algorithm for easy data with reduced regret.
problem Improving prediction accuracy with limited advice for easy data.
method Second Order Difference Adjustments (SODA) algorithm for online learning.
result Achieves improved regret guarantees for both stochastic and adversarial loss sequences.
We report an empirical study of Tehran Price Index (TEPIX). To analyze our data we use various methods like as, rescaled range analysis (R/S), modified rescaled range analysis (Lo's method), Detrended Fluctuation Analysis (DFA) and generalized Hurst exponents analysis. Based on numerical results, the scaling range of…
Consensual model explains spurious long-range memory in financial markets.
problem Understanding the origin of long-range memory in financial volatility.
method Non-linear stochastic differential equations.
result Empirical burst and inter-burst duration statistics can be explained by non-linear models.
In this paper, we present the results of Monte Carlo simulations for two popular techniques of long-range correlations detection - classical and modified rescaled range analyses. A focus is put on an effect of different distributional properties on an ability of the methods to efficiently distinguish between short and …
New perspective on SGD reveals short-range memory effects in deep learning.
problem Understanding the efficacy of stochastic gradient descent (SGD) in deep learning.
method Proposed that SGD is a discretization of an SDE driven by fractional Brownian motion (FBM).
result SGD stays longer in flat minima, favoring generalization.
A new financial model merges long-range dependence and leverage effects.
problem Challenges posed by financial markets' stylized facts.
method Develops a fractional and mixed-fractional CEV model using fractional calculus.
result Analytical valuation formula for European Call options and Greeks.
Study compares price limit and circuit breaker effects in stock markets.
problem Preventing rapid and steep price drops in stock exchanges.
method Agent-based model for financial market simulation.
result Price limit and circuit breaker have similar effects under same conditions, but price limit less effective with shorter limit time range.
Applications of Quantum Tunneling effect have long gone beyond the traditional physical meaning. Initially created by Gamow to explain α-decay of nuclear particles, along the time, quantum tunneling found fertile domain of research in chemistry and recently in biology, where the new discipline of Quantum Biology emerge…
Compressive Transformer learns long-range sequences by compressing past memories.
problem Learning long-range sequences in language and speech models.
method Compressive Transformer compresses past memories for efficient long-range sequence learning.
result State-of-the-art performance on language and speech benchmarks.
Norm-range partition improves MIPS search efficiency by reducing query complexity.
problem Efficiently searching for maximum inner product in large datasets.
method Norm-range partition technique that divides datasets into sub-datasets with similar norms and builds independent hash indexes.
result Significantly reduces the number of probed buckets for LSH-based MIPS algorithms.
Cointegration helps insurers understand long-range mortality patterns.
problem Insurers struggle to detect long-range dependence in their mortality data.
method Cointegration techniques applied to mixed fractional Brownian motion (mfBm) to capture long-range dependence.
result Cointegration brings long-range dependence information from national mortality data to insurers' models.
A new regularizer boosts long-range dependency in sequence data.
problem Improving long-range dependency in sequence data models.
method Developed a mutual information regularizer to enhance sequence learning.
result The approach increases mutual information and likelihood on holdout data.
ACI identifies cause-effect relationships and causal influence ranges in dynamical systems.
problem Detecting and quantifying causal influence ranges in complex systems.
method Bayesian data assimilation and assimilative causal inference (ACI) to trace causes back from observed effects.
result Mathematically rigorous formulations of forward and backward causal influence ranges (CIRs) for nonlinear dynamical systems.
With the daily and minutely data of the German DAX and Chinese indices, we investigate how the return-volatility correlation originates in financial dynamics. Based on a retarded volatility model, we may eliminate or generate the return-volatility correlation of the time series, while other characteristics, such as the…
We investigate serial correlation, periodic, aperiodic and scaling behaviour of eigenmodes, i.e. daily price fluctuation time-series derived from eigenvectors, of correlation matrices of shares listed on the Johannesburg Stock Exchange (JSE) from January 1993 to December 2002. Periodic, or calendar, components are dete…
A new method for forming learning objectives using the sum of ranked range.
problem Forming learning objectives from aggregated values.
method Sum of ranked range (SoRR) minimization with DCA.
result The proposed method effectively forms learning objectives and is applicable to binary and multi-label/multi-class classification.
The study establishes comparison theorems for weighted Finsler manifolds and spacetimes.
problem Analyzing weighted Finsler manifolds and spacetimes with curvature conditions.
method Using weight function and ε-range, the Bonnet-Myers theorem, Laplacian comparison theorem, and Bishop-Gromov volume comparison theorem are formulated. result New comparison theorems for weighted Finsler manifolds and spacetimes are derived, including those for weighted Riemannian manifolds.
SSMs have a built-in bias towards low-frequency components, which can be adjusted.
problem Frequency bias in SSMs affects their performance on long-range sequences.
method Proposed two mechanisms to tune frequency bias: scaling initialization or applying a Sobolev-norm-based filter.
result Tuning frequency bias improves SSMs' performance on long-range sequence learning tasks.
We present a simple microstructure model of financial returns that combines (i) the well-known ARFIMA process applied to tick-by-tick returns, (ii) the bid-ask bounce effect, (iii) the fat tail structure of the distribution of returns and (iv) the non-Poissonian statistics of inter-trade intervals. This model allows us…
Approximates derivative pricing under fractional stochastic volatility.
problem Derivative pricing under fractional stochastic volatility model.
method Approximate expression derived from deterministic functions and fractional Ornstein-Uhlenbeck process.
result Numerical simulations show the feasibility and effect of long-range dependencies on derivative prices.
Novel graph neural network combines random walks with local message passing.
problem Graph neural networks struggle with long-range dependencies.
method Combines random walks with local message passing in a novel architecture.
result Significant performance improvements on graph benchmarks.
We extend our previous study of scaling range properties done for detrended fluctuation analysis (DFA) \cite{former_paper} to other techniques of fluctuation analysis (FA). The new technique called Modified Detrended Moving Average Analysis (MDMA) is introduced and its scaling range properties are examined and compared…
We propose a general interpretation for long-range correlation effects in the activity and volatility of financial markets. This interpretation is based on the fact that the choice between `active' and `inactive' strategies is subordinated to random-walk like processes. We numerically demonstrate our scenario in the fr…
LLMs can be influenced by unseen dataset subtexts, revealing new ways to select data subsets.
problem Understanding how datasets subtly influence LLMs and their properties.
method Logit-Linear-Selection (LLS) method to select subsets of datasets.
result LLS reveals hidden effects in LLMs that persist across different models and architectures.
We investigate the Japanese personal income distribution in the high income range over the 112 years 1887-1998, and that in the middle income range over the 44 years 1955-98. It is observed that the distribution pattern of the lognormal with power law tail is the universal structure. However the indexes specifying the …
MRIF models dynamic user interests at multiple temporal-ranges.
problem Capturing dynamic and multi-resolution user interests in recommendation.
method Multi-resolution Interest Fusion (MRIF) model that considers both temporal-ranges and drifts in user interests.
result MRIF outperforms state-of-the-art recommendation methods consistently.
New model incorporates long-range dependence in mortality rates for better valuation and risk management.
problem Lack of appropriate models for valuing and managing mortality securities with long-range dependence.
method Proposes a novel class of Volterra mortality models that incorporate LRD, derived in closed-form solution.
result Models provide flexibility and tractability for valuing and hedging mortality-related products.
A new algorithm estimates output ranges for deep neural networks efficiently.
problem Estimating output ranges in deep neural networks with complex non-linearities.
method Integrates Simulated Annealing tailored for constrained domains and global optima.
result Guaranteed convergence and robust performance across various DNN architectures.
NeuroMemFPP uses LSTM to estimate FPP parameters with high accuracy.
problem Estimating parameters of fractional Poisson process with memory and long-range dependence.
method Recurrent Neural Network (RNN), specifically Long Short-Term Memory (LSTM), for parameter estimation.
result The LSTM-based approach reduces MSE by about 55.3% compared to traditional MOM method.
Paper analyzes output range of deep neural networks for verification.
problem Verifying deep neural networks for high-assurance applications.
method Combines local search and linear programming to find output ranges.
result Efficient algorithm for guaranteed range estimation of NN outputs.
Several machine learning problems arising in natural language processing can be modeled as a sequence labeling problem. We provide Gaussian process models based on pseudo-likelihood approximation to perform sequence labeling. Gaussian processes (GPs) provide a Bayesian approach to learning in a kernel based framework. …
Proposes a new investment strategy to optimize portfolio value within a target range.
problem Maximizing portfolio value within a specified range of returns.
method Two-stage least squares Monte Carlo method to handle complex payoffs.
result STRS strategy effectively contains portfolio value within the targeted range, improving risk-return trade-off.
A method for estimating the cross-correlation Cxy(τ) of long-range correlated series x(t) and y(t), at varying lags τ and scales n, is proposed. For fractional Brownian motions with Hurst exponents H1 and H2, the asymptotic expression of Cxy(τ) depends only on the lag τ (wide-sense stationarit…
This work examines how non-identical data distributions affect Federated Learning performance.
problem The impact of non-identical data distributions on Federated Learning performance.
method Synthesized datasets with varying degrees of data distribution similarity, evaluated Federated Averaging algorithm performance, proposed server momentum mitigation.
result Performance of Federated Learning degrades as data distributions differ more, and a mitigation strategy improves accuracy.
Automated method bounds causal effects in discrete data.
problem Partial identification of causal effects in discrete settings.
method Polynomial programming and dual relaxation for automated bounds.
result Algorithm provides guaranteed non-sharp and ε-sharp bounds. Transformer model improves source code summarization.
problem Generating readable summaries of source code.
method Transformer model with self-attention mechanism for code representation.
result Transformer model outperforms state-of-the-art techniques.
Bayesian Complementary Kernelized Learning models complex spatiotemporal data.
problem Modeling complex, nonstationary, and nonseparable spatiotemporal data.
method Integrates kernelized low-rank tensor factorization and short-range spatiotemporal Gaussian Processes.
result BCKL offers superior performance in providing accurate posterior mean and high-quality uncertainty estimates.
Many machine learning (ML) approaches are widely used to generate bioclimatic models for prediction of geographic range of organism as a function of climate. Applications such as prediction of range shift in organism, range of invasive species influenced by climate change are important parameters in understanding the i…
An average instantaneous cross-correlation function is introduced to quantify the interaction of the financial market of a specific time. Based on the daily data of the American and Chinese stock markets, memory effect of the average instantaneous cross-correlations is investigated over different price return time inte…
Ensemble sampling extends Thompson sampling's applicability to complex models.
problem Limited applicability of Thompson sampling to complex models.
method Develops ensemble sampling to approximate Thompson sampling for complex models.
result Ensemble sampling expands the viability of Thompson sampling to more applications.
Research reveals how diversity impacts ensemble generalization in classification tasks.
problem Understanding the relationship between diversity and generalization in classification ensembles.
method Investigated diversity measurement, its relationship with generalization error, and pruning methods.
result Generalization error is reduced effectively only when diversity is increased in specific ranges, not in others.
A new method estimates treatment effects in mixed groups, improving accuracy.
problem Estimating treatment effects in mixed groups with heterogeneous responses.
method PCM (pre-cluster and merge) approach for nonparametric estimation.
result Asymptotic consistency and significant improvement in accuracy over existing methods.
New method uses curvature to improve graph neural networks.
problem Graph Neural Networks struggle with over-smoothing and over-squashing.
method Augmented Forman-Ricci curvature (AFRC) for scalable rewiring.
result AFRC effectively mitigates over-smoothing and over-squashing.
New insights show Medicaid impacts on ED use vary widely, with some groups seeing significant increases.
problem Understanding the varied impacts of Medicaid on emergency department use.
method Causal machine learning methods to identify heterogeneous impacts.
result Meaningful heterogeneity in the effect of Medicaid on ED use, with a small group driving the overall effect.