Study shows diverse data types improve SARS-COV-2 case surge predictions.
problem Improving pandemic case surge predictions using multimodal data.
method Investigated the effectiveness of biological, public health, and behavioral features.
result Diverse feature sets enhance prediction accuracy, varying by country and phase.
Study models extreme skew surges along French Atlantic coast.
problem Appropriate modelling of extreme skew surges for coastal risk management.
method Peak-over-threshold framework, multivariate generalized Pareto distribution, extreme regression framework.
result Reconstructed historical skew surge time series at stations with limited data.
Study predicts future hospitalizations to manage COVID-19 patient surge.
problem Managing hospitalization needs during COVID-19 pandemic.
method Used 4 recurrent neural networks to predict hospitalization changes.
result Sequence to sequence model with attention achieved high accuracy and AUC.
Neural network predicts vessel motions with high accuracy.
problem Real-time prediction of heave and surge motions for improved performance and safety.
method Developed an LSTM-based machine learning model trained on measured waves and motion data.
result The model predicts vessel motions up to 46.5 seconds into the future with an average accuracy of 90%.
BreakGPT predicts asset price surges using LLMs.
problem Predicting sharp upward movements in volatile financial markets.
method Adapts LLMs for time series forecasting, combining LLM capabilities with Transformer models.
result BreakGPT effectively captures local and global temporal dependencies.
Research predicts XRP price anomalies using graph topologies.
problem Forecasting extreme price movements in XRP cryptoasset.
method Analyzed topological features of XRP transaction graphs.
result Topological features indicate extreme price surges.
The study finds generalized torsion elements in 3-manifolds from specific knot surgeries.
problem Identifying generalized torsion elements in 3-manifolds from knot surgeries.
method Using the JSJ-decomposition of the 3-manifold and bi-orderability properties.
result Existence and properties of generalized torsion elements in specific 3-manifolds.
Paper improves prediction sets for distribution shifts without labels.
problem Improving prediction sets effectiveness in the presence of distribution shifts.
method Develops ECP and EACP methods to adjust score function based on model uncertainty.
result Consistent improvement over existing baselines and nearly matches fully supervised methods.
This paper presents a model of the dynamics of the wage income distribution.
Exchanges acquire excess processing capacity to accommodate trading activity surges associated with zero-sum high-frequency trader (HFT) "duels." The idle capacity's opportunity cost is an externality of low-latency trading. We build a model of decentralized exchanges (DEX) with flexible capacity. On DEX, HFTs acquire …
Study improves trading decisions by predicting profit and loss outcomes.
problem Inconsistent profitability of machine learning forecasts in financial markets.
method Developed a novel algorithm for forecasting profit and loss outcomes, integrating with market trend predictions.
result Significantly improved performance of trading strategies, including traditional and algorithmic trading.
Study uses ML and statistical models to analyze climate impacts of industrial growth.
problem Understanding and predicting environmental impacts of industrial activities.
method Comparative analysis of ML and statistical models on time series data.
result ML models outperform statistical models in predicting environmental impacts.
Survey on calibration in machine learning, viewing it as indistinguishability.
problem Evaluating continuous probability predictions in discrete outcome settings.
method Defining and measuring calibration error through indistinguishability.
result Calibration measures quantify distinguishability between hypothesized and real-world outcomes.
The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowly adapting the new methods to its problems, but due to problems such as the non-stationary nature of financial data, significant challenges …
PsychFM predicts individual gambling choices using psychological and machine learning models.
problem Predicting individual gambling choices with high precision.
method PsychFM combines machine learning and psychological theories.
result PsychFM outperforms existing models like random forest and factorization machines.
Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human brain. While many rs-fMRI studies have focused on static measures of functional connectivity, there has been a recent surge in examining th…
The prominent inequality of wealth and income is a huge concern especially in the United States. The likelihood of diminishing poverty is one valid reason to reduce the world's surging level of economic inequality. The principle of universal moral equality ensures sustainable development and improve the economic stabil…
Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such cont…
EMDLOT predicts bond defaults better than traditional methods.
problem Lack of interpretability and irregular temporal dependencies in financial data.
method Integrates time-series and textual data, uses Time-Aware LSTM, soft clustering, and multi-level attention.
result EMDLOT outperforms traditional and deep learning benchmarks in recall, F1-score, and mAP.
The worldwide surge of multiresistant microbial strains has propelled the search for alternative treatment options. The study of Protein-Protein Interactions (PPIs) has been a cornerstone in the clarification of complex physiological and pathogenic processes, thus being a priority for the identification of vital compon…
With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large numbe…
Study optimizes climate adaptation strategies for NYC.
problem Catastrophic damages from extreme weather in NYC.
method Real options analysis and extreme value theory.
result Optimal adaptation pathways identified for NYC.
The recent surge of text-based online counseling applications enables us to collect and analyze interactions between counselors and clients. A dataset of those interactions can be used to learn to automatically classify the client utterances into categories that help counselors in diagnosing client status and predictin…
The paper proposes DEA to make graph neural networks fairer in link prediction.
problem Graph neural networks can unfairly prioritize certain social groups in link prediction.
method Drop Edges and Adapt (DEA) fine-tuning strategy with covariance constraints.
result DEA improves fairness and accuracy in link prediction tasks.
Fan tokens surged before World Cup matches, but declined during them, revealing cognitive biases.
problem Analyzing the impact of FIFA World Cup matches on fan tokens.
method Event study and intraday analysis of blockchain-based fan tokens.
result Fan tokens experienced a surge in returns six months before the World Cup, followed by a decline during the matches, revealing asymmetries in performance.
Researchers introduce a method to assess the safety of interpretable machine learning models.
problem Ensuring safety in machine learning models that are easy to understand.
method Introduce maximum deviation as an optimization problem to find the largest deviation from a safe reference model.
result Interpretability helps in assessing the safety of machine learning models.
APGE protects graph node representations from inference attacks.
problem Privacy leakage in graph embedding methods.
method Adversarial training framework with disentangling and purging mechanisms.
result APGE preserves structural and utility attributes while concealing private information.
Develops variational Bayesian neural network for complex biomedical applications.
problem High computational cost of Markov Chain Monte Carlo in BNN.
method Variational Bayes inference for posterior consistency and classification accuracy.
result Developed statistical theory for posterior consistency and prediction accuracy.
Study finds stock prices rarely appreciate during capital inflows but often appreciate during normal flows.
problem Understanding stock price behavior during capital inflows and outflows.
method Identified capital flow episodes using threshold and k-means clustering; detected stock index changepoints using PELT method; combined results over identified capital flows.
result Stock prices rarely appreciate during capital inflows but often appreciate during normal flows.
We analyze 27 house price indexes of Las Vegas from Jun. 1983 to Mar. 2005, corresponding to 27 different zip codes. These analyses confirm the existence of a real-estate bubble, defined as a price acceleration faster than exponential, which is found however to be confined to a rather limited time interval in the recen…
One-pass SGD converges in overparametrized neural networks with random data.
problem Understanding convergence of SGD in neural networks with streaming data.
method Overparameterized two-layer neural networks, one-pass SGD, random initialization, NTK eigen-decomposition, VC dimension, McDiarmid's inequality.
result Prediction error converges in expectation under one-pass SGD in overparametrized neural networks.
SAM optimizer benefits from normalization, stabilizing and guiding optimization.
problem Improving deep neural network performance with SAM optimizer.
method Theoretical and empirical study of normalization in SAM for convex and non-convex functions.
result Normalization helps SAM in stabilizing and guiding optimization along a continuum of minima.
Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each sto…
Mathematical framework for differential machine learning in finance.
problem Theoretical assumptions in financial models and their impact on machine learning algorithms.
method Rigorous mathematical framework for differential machine learning in finance.
result Theoretical grounding enhances the predictive capabilities of neural networks in financial applications.
A new particle filter uses diffusion models to improve state estimation from noisy data.
problem Sequentially estimating the state of a dynamical system from noisy and incomplete observations.
method Uses a diffusion model to simulate and predict system dynamics, incorporating noisy observations to refine predicted states.
result An unbiased particle filtering method that rigorously fuses observational data with diffusion model simulations.
A new embedding method extracts dataset-scale metric distribution into vectorial representation for graph data.
problem Classifying graph-structured data based on overall dataset-scale discrepancies.
method MetricDistribution2vec embedding strategy.
result Significant improvement in supervised prediction tasks on real-world graph datasets.
Advances in deep learning for natural images have prompted a surge of interest in applying similar techniques to medical images. The majority of the initial attempts focused on replacing the input of a deep convolutional neural network with a medical image, which does not take into consideration the fundamental differe…
New method optimizes treatment policies to avoid winner's curse.
problem Winner's curse in treatment policy optimization.
method Inference-aware policy optimization.
result Optimizes for both estimated performance and downstream evaluation.
Work proposes CLAIRE to achieve counterfactual fairness from observational data without causal models.
problem Achieving counterfactual fairness from observational data without prior causal models.
method Proposes CLAIRE, a representation learning framework based on counterfactual data augmentation and an invariant penalty.
result CLAIRE effectively mitigates biases from the sensitive attribute and improves counterfactual fairness and prediction performance.
Advanced travel information and warning, if provided accurately, can help road users avoid traffic congestion through dynamic route planning and behavior change. It also enables traffic control centres mitigate the impact of congestion by activating Intelligent Transport System (ITS) proactively. Deep learning has beco…
This study interprets machine learning models to identify biomarkers for severe COVID-19 infection.
problem The black-box nature of machine learning models makes it difficult for medical researchers to understand and trust their predictions.
method The study uses permutation feature importance, Partial Dependence Plot, Individual Conditional Expectation, Accumulated Local Effects, Local Interpretable Model-agnostic Explanations, and Shapley Additive Explanation to interpret four machine learning models.
result The study identifies NTproBNP, CRP, LDH, LYM, leukocytes, eosinophils, and platelets as biomarkers associated with severe COVID-19 infection.
Researchers develop method to protect against 'weight poisoning' attacks on pre-trained models.
problem The security threat of downloading untrusted pre-trained weights that can be manipulated after fine-tuning.
method RIPPLe regularization method and Embedding Surgery initialization procedure.
result Demonstrated that weight poisoning attacks are possible even with limited knowledge of the dataset and fine-tuning procedure.
In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture that alternates convolution-like operations on the graph and its dual. Our approach allows to lear…
New theory explains how equivariant self-supervised learning improves feature extraction.
problem Contrastive learning sacrifices useful features due to invariance to data augmentations.
method Information-theoretic perspective to understand E-SSL's generalization ability.
result Equivariant self-supervised learning creates synergy between equivariant and classification tasks.
As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpr…
There has been a recent surge of interest in studying permutation-based models for ranking from pairwise comparison data. Despite being structurally richer and more robust than parametric ranking models, permutation-based models are less well understood statistically and generally lack efficient learning algorithms. In…
A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order. However, in real-world scenarios, some items are intrinsically ambiguous in comparisons, which may very well be an inherent uncertainty of the data. In this case, the conventional total order ranking c…
A new method for designing accurate emulators using deep learning with interval calibration.
problem Designing accurate emulators for scientific processes with modern machine learning methods.
method Learn-by-Calibrating (LbC) approach based on interval calibration.
result Significant improvements in generalization error over widely-used loss functions.