Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in E…
On-device research index
arXiv research
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.
169,181 papers · 148 categories
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48 results for “self-controlled case series”
ConvSCCS model detects rare adverse drug reactions from EHRs.
problem Underreporting of adverse drug reactions due to physician reports.
method Conditional Poisson model with convolution and penalized step functions.
result Improves estimation of relative risks in diabetic patients.
PACC Discovery improves causal inference from limited data.
problem Inferring causal relationships from finite data.
method Extends PAC learning principles to causal inference.
result Theoretical guarantees for various causal methods.
ARM estimator improves gradient backpropagation in binary networks.
problem Improving gradient backpropagation through stochastic binary layers.
method ARM estimator using augment-REINFORCE-merge approach.
result ARM estimator achieves state-of-the-art performance in binary models.
CARMS improves gradient estimation for categorical variables.
problem Accurately backpropagating gradients through categorical variables.
method CARMS combines REINFORCE with antithetic sampling to create unbiased gradient estimators.
result CARMS outperforms competing methods on various tasks.
New hashing method improves document retrieval precision.
problem Efficiently retrieving similar documents from large text databases.
method Pairwise supervised hashing with Bernoulli VAE and unbiased gradient estimator.
result Superior performance compared to existing methods.
The significance of the study of the theoretical and practical properties of AdaBoost is unquestionable, given its simplicity, wide practical use, and effectiveness on real-world datasets. Here we present a few open problems regarding the behavior of "Optimal AdaBoost," a term coined by Rudin, Daubechies, and Schapire …
Novel financial time-series data representation improves industry sector classification.
problem Classifying industries using historical stock returns time-series data.
method Proposed a novel representation based on stock returns embeddings for time-series data, overcoming representational challenges of conventional approaches.
result Substantial performance improvements over baselines using conventional representations.
Paper proposes a privacy-preserving DML framework using local randomization and ADMM perturbation.
problem Privacy concerns in distributed machine learning with sensitive user data.
method Local randomization and ADMM perturbation to provide differential privacy and heterogeneous privacy levels.
result The framework minimizes privacy losses and maintains model generalization.
New method uses Transformers for flu forecasting.
problem Forecasting influenza-like illness trends.
method Transformer-based machine learning models with self-attention.
result Forecasting results are competitive with state-of-the-art methods.
Detects crime series using RBM embeddings from crime narratives.
problem Detecting related crime series from crime records.
method Unsupervised learning of latent feature embeddings using Gaussian-Bernoulli RBM.
result Related cases are closer in feature space, unrelated cases are far apart.
Our aim is to prove that two formal power series of importance to quantum topology are Gevrey. These series are the Kashaev invariant of a knot (reformulated by Huynh and the second author) and the Gromov norm of the LMO of an integral homology 3-sphere. It follows that the power series associated to a simple Lie algeb…
We determine the lower central and derived series of the n-string braid groups B_n(RP^2) of the real projective plane. We are motivated in part by the study of Fadell-Neuwirth short exact sequences, but the problem is interesting in its own right. For n=1,2, B_n(RP^2) is finite and its lower central and derived series …
Method summarizes and predicts time series data for COVID-19 cases and deaths.
problem Summarizing and predicting time series data for multiple related time series.
method Hierarchical algorithm generating shapelets for centroids, nearest neighbor search for labeling, dynamic time warping for non-uniform lengths.
result Predictive model for individual time series based on aggregated statistics.
Paper introduces Native Guide for generating time series counterfactual explanations.
problem Lack of explainability for time series data in AI systems.
method Model-agnostic, instance-based counterfactual generation for time series classification.
result Native Guide produces better counterfactual explanations than benchmarks.
Study series invariants of plumbed 3-manifolds using root lattices.
problem Understanding invariants of plumbed 3-manifolds twisted by root lattices.
method Use formal series to study invariants, decompose , and compute in specific cases.
result Show that is unique and decomposes into related series invariant under five Neumann moves.
Adaptive Conformal Inference improves time series forecasting uncertainty.
problem Uncertainty quantification in time series models with dependency.
method AgACI, an adaptive method based on online expert aggregation.
result AgACI provides efficient prediction intervals for day-ahead electricity price forecasting.
Study lower central and derived series of braid and pure braid groups on compact surfaces.
problem Determine residual properties of braid and pure braid groups on compact surfaces.
method Analyzing semi-direct products and calculating lower and derived series explicitly.
result Explicit calculations and estimates for residual properties of braid groups on various compact surfaces.
Research tackles unequal length time series for classification.
problem Unequal length time series in real-world data.
method Identified and evaluated two classes of unequal length mechanisms.
result Practical recommendations for handling unequal length time series.
A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Seriesstat.ML
Unified framework for long-range and cold-start seasonal forecasts.
problem Forecasting seasonal profiles with limited historical data.
method Combining high-dimensional regression and matrix factorization.
result Framework accurately forecasts seasonal profiles on multiple datasets.
We provide a simple way to obtain the meromorphic extension of Eisenstein series and Scattering matrices under conditions which generalize the case of discrete groups acting convex cocompactly on hyperbolic spaces.
Review of distance-based methods for time series classification.
problem Challenges in classifying time series data.
method Distance-based approaches for time series classification.
result New methods exploit distances to improve classification performance.
Methodology to measure lag relevance in time series models.
problem Measuring lag relevance in machine learning models for univariate time series.
method Ghost variables, Shapley values, additive importance measures, auto-relevance and partial auto-relevance functions, one-step forecast.
result Calculated relevance measures successfully demonstrate expected lag structure in almost all cases.
New method improves causal discovery in time series with latent confounders.
problem Low recall in causal discovery for autocorrelated time series with latent confounders.
method Iterative procedure that includes causal parents in conditioning sets, using novel orientation rules.
result Significantly higher recall compared to existing methods, especially in strong autocorrelation cases.
Proposes adjusting neural network errors for time series forecasting.
problem Autocorrelated errors in neural networks for time series.
method Jointly learn autocorrelation coefficient with model parameters.
result Enhances performance in almost all time series forecasting cases.
Study series invariants for plumbed 3-manifolds and their properties.
problem Understanding series invariants for plumbed 3-manifolds and their applications.
method Twisted root lattice, gluing and splitting properties, explicit description of lens spaces and Brieskorn spheres.
result Series verify gluing and splitting properties of 3-manifolds.
This short note suggests a heuristic method for detecting the dependence of random time series that can be used in the case when this dependence is relatively weak and such that the traditional methods are not effective. The method requires to compare some special functionals on the sample characteristic functions with…
LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patternsstat.AP
LSTM-MSNet forecasts time series with multiple seasonal patterns using a unified model.
problem Forecasting time series with multiple seasonal cycles.
method Decomposition-based, unified prediction framework using LSTM.
result LSTM-MSNet outperforms state-of-the-art methods on various datasets.
Polynomial expansions improve option pricing accuracy.
problem Efficiently pricing and Greeks in stochastic volatility models.
method Analytic series representations for European and exotic options.
result Polynomial expansions match Fourier transform accuracy.
Proposes T-CGAN for generating time series data with irregular sampling.
problem Generating time series data with irregular sampling and noise.
method Conditional Generative Adversarial Network (CGAN) with deconvolutional and convolutional neural networks, conditioned on timestamps.
result T-CGAN-generated time series perform as well as real data for classification tasks.
New formulas for colored Jones polynomials of double twist knots generalize series and duality.
problem Calculating colored Jones polynomials for double twist knots.
method Using Takata's result and comparing with cyclotomic expansions.
result Generalizes Kontsevich-Zagier series and duality at roots of unity.
New model predicts univariate and multivariate time series with improved accuracy.
problem Complex patterns in univariate and multivariate time series forecasting.
method Uses autoregressive convolutional recurrent neural network with feature extraction and recurrent encoder.
result Outperforms existing architectures in multivariate time series datasets.
New deep probabilistic model handles missing data in time series forecasting.
problem Handling missing data in time series forecasting.
method Combination of deep learning and probabilistic methods.
result Advantage in forecasting and novelty detection with missing data.
When the lower central series stops: a comprehensive study for braid groups and their relativesmath.GT
Study on when the lower central series stops for various groups, including braid groups.
problem Understanding when the lower central series stops for different groups.
method Various techniques applied to braid groups and related groups.
result Complete computation of the lower central series for most groups studied.
Paper proposes CNN-based time series anomaly detection with transfer learning.
problem Time series anomaly detection in automated monitoring systems.
method CNN for segmentation, transfer learning framework, fine-tuning on unseen classes.
result Successfully tested on multiple synthetic and real data sets.
The COS method proposed in Fang and Oosterlee (2008), although highly efficient, may lack robustness for a number of cases. In this paper, we present a Stable pricing of call options based on Fourier cosine series expansion. The Stability of the pricing methods is demonstrated by error analysis, as well as by a series …
New formulas for colored Jones polynomials of double twist knots and related series.
problem Calculating colored Jones polynomials and related series for double twist knots.
method Utilized Takata's result and Bailey pairs, along with Walsh's formulas.
result Found new families of -hypergeometric series generalizing the Kontsevich-Zagier series.
Safe active learning for time-series models with Gaussian processes.
problem Learning time-series models while respecting safety constraints.
method Employing Gaussian processes with a nonlinear exogenous input structure, the approach dynamically explores the input space to generate data for model learning.
result The approach effectively learns time-series models under safety constraints, as demonstrated in a technical application.
This paper introduces new invariants for time series analysis.
problem Analyzing the diversity and invariants of time series data.
method Introduces new invariants derived from the continuity of magnitude and maximum diversity.
result Demonstrates improved performance in machine learning experiments with real-world data.
Paper proposes forecast-necessity testing for accurate causal interpretation in nonlinear time-series models.
problem Misinterpretation of causal scores from nonlinear models as regression coefficients.
method Systematic edge ablation and forecast comparison to evaluate causal necessity.
result Causal relationships with similar scores can differ in their necessity for accurate prediction.
Neural GARCH models financial time series with time-varying coefficients.
problem Modeling conditional heteroskedasticity in financial time series.
method Neural network adaptation of GARCH and BEKK models with time-varying coefficients parameterized by a recurrent neural network.
result Neural Students t model consistently outperforms other models on financial time series.
A new test for volatility in clustered time series data, robust to distributional assumptions.
problem Volatility issues in clustered multiple time series data, especially in stock market indicators.
method Bootstrap method for multiple time series, accounting for contagion effect.
result The test is correctly sized and powerful, especially for stationary mean and contained volatility in fewer clusters.
New invariants for 3-manifolds derived from supergroup representations.
problem Developing invariants for 3-manifolds using supergroup analogues.
method Introducing supergroup analogues of 3-manifold invariants for superunitary groups, focusing on SU(2|1). Calculating q-series for specific 3-manifolds and studying their properties.
result Explicit calculation and study of q-series for certain 3-manifolds, providing a formula relating new invariants to quantum invariants.
Unsupervised clustering of series using dynamic programming.
problem Clustering coherent segments in multi-variate series.
method Dynamic programming algorithm with constraints.
result Clusters of coherent segments identified using Waxman-Smits equation.
Robust archetypal analysis for financial time series simplifies complex data understanding.
problem Sensitivity to outliers in traditional archetypal analysis.
method Robust M-estimators for multivariate and functional data.
result New methodology outperforms existing methods in simulations and real data.
The paper introduces a fast algorithm for learning and forecasting nonlinear dynamics from noisy time series data.
problem Challenges in capturing nonlinear dynamics from noisy time series data.
method A projected nonlinear state-space model with kernel functions applied to projected lines.
result The model effectively learns and forecasts complex nonlinear dynamics with computational efficiency.
New imputation method for time series with categorical variables.
problem Missing values in multivariate time series data.
method Expectation Maximization over dynamic Bayesian networks.
result Outperforms state-of-the-art methods in synthetic and real data.
Generative models improve commodity hedging using deep learning.
problem Improving risk management in commodity markets.
method Four state-of-the-art generative models adapted for commodity time series.
result Deep hedging of commodity options trained on generated time series shows promising results.