New approach minimizes robust baseline regret for safe policy improvement.
problem Computing a safe policy using limited data and inaccurate dynamics models.
method Model-based approach that minimizes negative robust regret w.r.t. a baseline policy.
result Our method significantly outperforms standard approaches in empirical tests.
The paper refutes standard asset pricing models and introduces new theories.
problem Inaccuracies in standard asset pricing models.
method Introduces new theories and empirical tests to explain asset pricing anomalies.
result New theories explain why standard models are inaccurate and provide insights.
Deep learning detects inaccurate smart meters for resource savings.
problem Detecting and replacing inaccurate smart meters to save resources.
method Developed a deep-learning method using LSTM and CNN to predict electricity usage trajectories.
result High accuracy in detecting inaccurate meters for practical usage.
Despite the importance of sparsity signal models and the increasing prevalence of high-dimensional streaming data, there are relatively few algorithms for dynamic filtering of time-varying sparse signals. Of the existing algorithms, fewer still provide strong performance guarantees. This paper examines two algorithms f…
A new method uses neural networks to improve POD-Galerkin models for complex systems.
problem Improving computational efficiency and accuracy in solving non-linear high-dimensional systems.
method Deep learning-based closure modeling using neural networks to approximate POD-Galerkin operators.
result The CD-ROM approach produces more accurate and stable models for complex systems.
DL-FUMI learns target and nontarget dictionary atoms for accurate target detection.
problem Target detection with inaccurate training labels.
method Multiple Instance Dictionary Learning using functions of multiple instances.
result DL-FUMI finds more representative target dictionary atoms than existing methods.
TSCI improves causal inference in dynamical systems using vector fields.
problem Challenges in causal discovery with time series data in dynamical systems.
method TSCI method using vector fields to check for synchronization between learned dynamics.
result TSCI outperforms traditional methods like CCM and its generalizations.
Data-driven approach learns effective equations for phase field interfaces.
problem Learning accurate equations for phase field interface dynamics.
method Data-driven identification of partial differential equations from phase field data.
result Data-driven equations outperform analytical approximations in certain regimes.
The paper introduces a method to create more reliable models for robot dynamics.
problem Creating accurate models for robot dynamics to improve control and planning algorithms.
method A primal-dual method to enforce constraints on error in specific parts of the state-space.
result The learned models have more predictable error characteristics, enhancing their usability for planning and control algorithms.
The study revisits inaccuracies in time series averaging under dynamic time warping.
problem Inaccuracies in time series averaging under dynamic time warping.
method Analysis of existing correctness-criterion and introduction of drift-outs, showing their insufficiency and inconclusiveness.
result Sample means as global minimizers of a Fréchet function never drift out, and the adjusted drift-out is a test for coherence.
Paper proposes BMPO to optimize policies using bidirectional models.
problem Model-based reinforcement learning's reliance on forward model accuracy.
method Develops BMPO using both forward and backward models for policy optimization.
result BMPO outperforms state-of-the-art methods in sample efficiency and asymptotic performance.
This paper has been withdrawn by the author due to some inaccurate descriptions in the section of INTRODUCTION and CONCLUSIONS.
The study examines how many samples are needed to minimize a noisy convex function with inaccurate gradient estimates.
problem Determining the number of samples needed to minimize a noisy convex function with inaccurate gradient estimates.
method Using Stochastic Convex Optimization as a case study, the study analyzes the relationship between the number of samples and the accuracy of gradient estimates.
result The study provides partial answers to the question, showing that a general analyst requires Ω ( 1 / ε 3 ) Ω(1/ε^3) Ω ( 1/ ε 3 ) samples and that under certain assumptions, i l d e Ω ( 1 / ε 2.5 ) ilde Ω(1/ε^{2.5}) i l d e Ω ( 1/ ε 2.5 ) samples are necessary for gradient descent to interact with the oracle. Two simulation-based methods improve optimal sampling design in systems biology.
problem Optimal selection of sampling points for accurate parameter estimation in dynamical systems.
method E-optimal-ranking (EOR) and LSTM neural network-based methods.
result Simulation studies show the proposed methods outperform random selection and classical E-optimal design.
New method improves uncertainty quantification for large batch sizes and misspecified models.
problem Challenges in tuning algorithms for accurate uncertainty quantification in large batch sizes and misspecified models.
method Proposes new discrete-time approximations to SGD and SGLD, proving error bounds for practical tuning.
result Quantitative, non-asymptotic error bounds for accurate predictions of covariance and autocorrelation time.
Deep reinforcement learning optimizes power allocation in wireless networks.
problem Challenges in optimizing power allocation in large wireless networks.
method Distributively executed dynamic power allocation scheme using deep Q-learning.
result Achieves near-optimal power allocation in real-time with delayed CSI.
New techniques improve the accuracy of identifying nonlinear systems from noisy data.
problem Identifying nonlinear dynamical systems from noisy state measurements.
method Comparative study of local and global smoothing techniques to denoise state measurements and improve sparse regression methods.
result Global smoothing methods outperform local methods in improving the accuracy of governing equation recovery.
Improves stability of actor-critic methods by penalizing TD error.
problem Instability in actor-critic methods during learning.
method Regularize actor's learning objective by penalizing critic's TD error.
result Improves stability and overall performance of actor-critic methods.
BOMS enhances offline MBRL by improving model selection with Bayesian optimization.
problem Inaccurate model selection in offline MBRL due to distribution shift.
method Proposes BOMS, an active model selection framework using Bayesian optimization.
result Improves model selection with only a small amount of online interaction.
We study the value of information in sequential compressed sensing by characterizing the performance of sequential information guided sensing in practical scenarios when information is inaccurate. In particular, we assume the signal distribution is parameterized through Gaussian or Gaussian mixtures with estimated mean…
Paper proposes a deep learning strategy for efficient FC management.
problem Inaccurate and conservative FC dimensioning approaches in realistic settings.
method Uses a Convolutional Neural Network (CNN) to dynamically modulate FC resources.
result Achieves a maximum rejection rate of 3% and 37.5% resource savings.
Improved learning of relational models from partial network data.
problem Inaccurate parameter estimates for relational models from network samples.
method Stochastic gradient descent for relational logistic regression from partial network crawls.
result Accurate parameter estimates and confidence intervals achieved.
Hybrid model combines machine learning and knowledge-based forecasting for chaotic systems.
problem Improving accuracy of chaotic system forecasts using limited knowledge.
method Combining machine learning with a knowledge-based model.
result Hybrid model predicts longer into the future than either component alone.
Aioli unifies language model data mixing methods and improves performance.
problem Optimizing the mixture of training data groups for language models.
method Unified optimization framework for dynamically adjusting mixture proportions.
result Aioli outperforms existing methods by up to 12.012 test perplexity points.
This paper tackles imbalanced classification with weakly supervised oversampling.
problem Imbalanced classification in high-dimensional datasets.
method Weakly supervised SMOTE, cost-sensitive NCA, bootstrap ensemble.
result Improved classification performance on synthetic and real-world datasets.
The paper tackles model-based RL's inaccuracy issue by dynamically adjusting planning horizons.
problem Model-based RL's failure due to model inaccuracy over long planning horizons.
method State-dependent planning horizon, learning cumulative model errors with Temporal Difference methods.
result The proposed method successfully adapts planning horizons to state-dependent model accuracy, improving policy learning efficiency.
Neural networks improve wave-equation simulation accuracy.
problem Inaccurate discretization of Laplacian in wave-equation simulation leads to numerical dispersion.
method Intersperse CNNs between low-fidelity timesteps to correct wavefield and limit numerical dispersion.
result Neural network augmentation reduces numerical dispersion artifacts in wave-equation simulation.
Study proposes method to estimate causal effects from noisy treatment data.
problem Estimating causal effects from noisy treatment data without side information.
method Deep latent variable model with neural network parameterization and amortized importance-weighted variational objective.
result Causal effect estimates are identifiable without side information and measurement error variance knowledge.
Paper uses robust GMM to reconstruct missing data in Sentinel-2 images for crop monitoring.
problem Missing data in remote sensing images, especially from multispectral and SAR sensors.
method Robust Gaussian Mixture Models (GMM) with outlier detection using isolation forest.
result Robust GMM outperforms standard GMM in reconstructing imputed values, reducing errors.
Proposes a novel imputation network for clinical time series data.
problem Missing value imputation in clinical time series data with sparsity, irregularity, and high-dimensionality.
method Variational-recurrent imputation network that considers correlated features, temporal dynamics, and uncertainty.
result The proposed method outperformed state-of-the-art methods on real-world EHR datasets.
New model combines ICA and HMM for unsupervised learning of nonstationary time series.
problem Manual segmentation of non-stationary data is computationally expensive and inaccurate.
method Combines Hidden Markov Model with nonlinear ICA for unsupervised learning.
result Proves identifiability of the model for general mixing nonlinearity.
New algorithms for constrained online optimization with memory and predictions.
problem Control of constrained dynamical systems and scheduling with reconfiguration budgets.
method Proposed algorithms achieving sublinear regret and constraint violation under time-varying constraints, both with and without predictions.
result First algorithms achieving sublinear regret and constraint violation in constrained online optimization with memory.
Gradient descent protects large neural networks from overfitting in high-dimensional data.
problem Generalization error in large neural networks trained on high-dimensional data.
method Average case analysis using random matrix theory and linear model solutions.
result Gradient descent naturally protects against overtraining in large networks, reducing overfitting at intermediate network sizes.
Proposes a method to train deep neural networks robust to label noise in remote sensing images.
problem Training deep neural networks on datasets with inaccurate labels leads to overfitting and poor performance.
method Uses entropic optimal transport to learn robust deep neural networks.
result Empirically demonstrates superior performance compared to state-of-the-art methods on remote sensing datasets.
New normalization method makes neural networks more robust to adversarial attacks.
problem Adversarial vulnerability of BatchNorm in deep neural networks.
method Identified distribution shift caused by adversarial images, proposed RobustNorm to use inference-time statistics.
result RobustNorm makes models more robust to adversarial attacks without sacrificing BatchNorm benefits.
Improves feature selection in high-dimensional data using LLM-generated weights.
problem Inaccurate LLM-generated weights degrade feature selection performance.
method Integrates LLM-generated weights into prior inclusion probabilities using LLM Sparsity Prior (LSP).
result Improves prediction accuracy and identifies clinically relevant features.
Study optimizes pricing under uncertainty and capacity constraints.
problem Optimizing pricing decisions under demand uncertainty and capacity constraints.
method Analyzes linear demand, stochastic noise, and finite capacity; uses certified demand forecasts and control variates.
result Certified demand forecasts reduce regret from O ( T ) O(\sqrt{T}) O ( T ) to O ( log T ) O(\log T) O ( log T ) under certain conditions. DC-NAS improves neural architecture search by clustering and evaluating sub-networks.
problem Inaccurate evaluation of neural architectures in large search spaces.
method Divide-and-Conquer approach: feature representation, clustering, and evaluation of clusters.
result Achieved 75.1% top-1 accuracy on ImageNet, surpassing state-of-the-art methods.
The predictions of the S&P 500 returns made in 2007 have been tested and the underlying models amended. The period between 2003 and 2008 should be described by the dependence of the S&P 500 stock market index on real GDP because the population pyramid was highly inaccurate. The 2008 trough and 2009 rally are well predi…
Paper proposes a new framework to improve policy optimization by aligning real and simulated data distributions.
problem Inaccurate model estimation leads to performance degradation in model-based reinforcement learning.
method Introduces unsupervised model adaptation to minimize the IPM between real and simulated data distributions.
result Achieves state-of-the-art performance in sample efficiency on various continuous control tasks.
Multivariate regular variation plays a role assessing tail risk in diverse applications such as finance, telecommunications, insurance and environmental science. The classical theory, being based on an asymptotic model, sometimes leads to inaccurate and useless estimates of probabilities of joint tail regions. This pro…
New bounds for sampling algorithms without log-concavity assumptions.
problem Sampling high-dimensional probability measures without log-concavity assumptions.
method Euler discretisation of SDEs with novel convergence rates and coupling construction.
result Explicit L 2 L^2 L 2 convergence rates and non-asymptotic bounds for sampling algorithms. Convolutional networks struggle with repeating patterns in ECGs.
problem Modeling repeating patterns in electrocardiogram signals.
method Demonstrated through ECG examples, highlighting systemic issues in deep learning.
result Counterintuitive effects on generalization in deep networks.
We apply multiple testing procedures to the validation of estimated default probabilities in credit rating systems. The goal is to identify rating classes for which the probability of default is estimated inaccurately, while still maintaining a predefined level of committing type I errors as measured by the familywise …
Enhances ENet's prediction accuracy while maintaining uncertainty estimation.
problem Gradient shrinkage problem in ENet's loss function.
method Proposes a multi-task learning framework with a modified MSE loss function.
result Improves ENet's prediction accuracy without losing uncertainty estimation.
Incorporating domain knowledge into the modeling process is an effective way to improve learning accuracy. However, as it is provided by humans, domain knowledge can only be specified with some degree of uncertainty. We propose to explicitly model such uncertainty through probabilistic constraints over the parameter sp…
Local control regression improves portfolio optimization accuracy.
problem Expensive and inaccurate global control regression for portfolio optimization.
method Introduced local control regression combined with adaptive grids.
result Choosing a coarse grid for local regression produces accurate results.
SBI provides more accurate pole positions than chi-squared minimization in model misspecification.
problem Accurate pole position estimation in pi-pi scattering models.
method Simulation Based Inference (SBI) method compared to chi-squared minimization.
result SBI leads to more robust predictions of pole positions in models of pi-pi scattering.