Paper proposes BCNMCC for system identification with noisy input.
problem System identification with noisy input and impulsive output noise.
method Introduces BCV to NMCC algorithm to compensate for input noise bias.
result BCNMCC outperforms other algorithms in noisy input conditions.
SCN learns and compensates for data bias in citizen science projects.
problem Data bias in citizen science datasets.
method End-to-end learning scheme that learns and compensates for data shift.
result SCN outperforms models that ignore data bias and handles massive high-dimensional data.
This paper explores how enforcing equivariance constraints limits neural network expressivity and proposes compensatory model size increases.
problem The impact of enforcing equivariance constraints on the expressive power of neural networks.
method Examined 2-layer ReLU networks, analyzed boundary hyperplanes and channel vectors, and constructed upper bounds on model size required for compensation.
result Enforcing equivariance constraints reduces the expressive power of neural networks, but this can be compensated by increasing model size.
The paper corrects biases in estimating intrinsic dimension and differential entropy.
problem Systematic bias in estimating intrinsic dimension and differential entropy.
method A bias-corrected estimator for both measures is proposed, highlighting shared steps and useful consequences.
result Simultaneous estimation of differential entropy and intrinsic dimension provides complementary perspectives on underlying manifolds.
Resampling outperforms reweighting for correcting biased data in machine learning models.
problem Correcting sampling bias in machine learning models trained on biased data sets.
method Compared resampling and reweighting techniques, focusing on their performance with stochastic gradient algorithms.
result Resampling outperforms reweighting when combined with stochastic gradient algorithms.
BC-ACI corrects time series forecast bias, improving prediction intervals.
problem Persistent bias in time series forecasts leads to overly conservative prediction intervals.
method Augments ACI with an EWM estimate of forecast bias to correct nonconformity scores and re-center intervals.
result Reduces Winkler interval scores by 13-17% under distribution shifts, improving calibration.
Improved GPS accuracy in urban areas.
problem Urban GPS inaccuracies due to environmental factors.
method Probabilistic model to learn and compensate for environmental biases.
result Localization error less than 10 meters in LTE networks.
Paper addresses quantization bias in neural networks, proposing a simple method to improve performance.
problem Quantization bias causes performance degradation in low-precision neural networks.
method Compensating for quantization-induced shift by adding a constant to the bias term.
result Simple methods for estimating correction constants can match training-based quantization performance at lower cost.
The Canonical Regression Quantile method predicts CEO compensation and future performance.
problem Determining fair CEO compensation and its impact on company performance.
method Canonical Regression Quantile method to assess CEO pay and performance.
result The method can predict future CEO performance and distinguish over/underpaid CEOs.
The paper simplifies calculus for semimartingales using multiplicative compensation.
problem Developing a formula for complex-valued semimartingales to simplify stochastic calculus.
method Multiplicative compensation for complex-valued semimartingales.
result The stochastic exponential of complex-valued semimartingales becomes a true martingale after compensation.
Study multi-armed bandits with compensation to maximize total reward and minimize payments.
problem Optimize reward collection from short-term players with compensation.
method Propose KCMAB problem, provide lower bound, analyze three algorithms, and demonstrate performance.
result Algorithms achieve O(log T) regret and O(log T) compensation matching theoretical lower bound.
Machine learning improves PMD compensation in multiplexed systems.
problem Improving performance in multiplexed systems with PMD.
method Model-based machine learning parameterizing the Manakov-PMD equation.
result Performance close to PMD-free case achieved with hardware-friendly DBP and PMD compensation.
New multi-step approach improves fiber nonlinearity compensation efficiency.
problem Fewer steps are traditionally considered better for fiber nonlinearity compensation.
method Carefully designed multi-step machine learning approaches.
result Multi-step approaches lead to better performance-complexity trade-offs.
Chronologically consistent models maintain accuracy with time-restricted data.
problem Training data introduces lookahead bias and training leakage in large language models.
method ChronoBERT and ChronoGPT trained with only available data at each time point.
result Models achieve strong performance and competitive with larger models, mitigating lookahead bias.
This work compares regularization and constrained inference for label constraints in machine learning.
problem Improving model performance with label constraints in machine learning.
method Comparison of regularization and constrained inference strategies.
result Constrained inference reduces population risk by correcting model violations, while regularization narrows the generalization gap but introduces bias.
Study incentivizes exploration in non-stationary MAB with compensation.
problem Incentivized exploration for non-stationary stochastic bandits with biased feedback.
method Proposed algorithms for abruptly-changing and continuously-changing non-stationary environments.
result Achieves sublinear regret and compensation over time.
Improves LASSO by scaling to reduce bias and enhance model selection.
problem LASSO's bias issue affecting sparse model selection.
method Introducing a scaling method to expand LASSO estimator.
result Empirical scaling improves LASSO and enhances model selection.
EControl improves fast distributed optimization with compression and error control.
problem Stable convergence issues in distributed training with compression.
method Proposes EControl to regulate error compensation and prove fast convergence.
result Proves fast convergence for EControl in various convex settings without additional assumptions.
New neural network improves MRI reconstruction for non-Cartesian data.
problem Improving MRI reconstruction for non-Cartesian data acquisitions.
method Density-compensated unrolled neural networks.
result Density-compensated unrolled neural networks outperform baselines.
Proposes a new algorithm to reduce communication costs in decentralized training.
problem How to apply error-compensated compression to decentralized training.
method Error-compensated stochastic gradient descent for decentralized training.
result Proposed algorithm outperforms existing methods in communication cost reduction.
The paper analyzes fairness of compensation-based risk-sharing schemes for fund payouts.
problem Fair allocation of payouts in an endowment contingency fund.
method Analyzes two types of administrators and general non-negative loss distributions.
result General conditions for actuarial fairness are provided.
A machine learning model for PMD compensation in dual-polarization systems.
problem Compensating for polarization-mode dispersion (PMD) in dual-polarization systems.
method Model-based machine learning approach using the split-step Fourier method for the Manakov-PMD equation.
result The model converges to within 1% of peak dB performance after 428 iterations, achieving a 0.30 dB reduction in effective signal-to-noise ratio compared to PMD-free case.
We present a method to compensate statistical errors in the calculation of correlations on asynchronous time series. The method is based on the assumption of an underlying time series. We set up a model and apply it to financial data to examine the decrease of calculated correlations towards smaller return intervals (E…
Extends compactness theory to variable-coefficient pseudo-differential operators on manifolds.
problem Compensated compactness for pseudodifferential operators on vector bundles.
method Establishes a theorem for weakly convergent sequences of sections under a pseudo-differential operator.
result Quadratic form converges in distributional sense under certain conditions.
A new IC-Connection improves disentanglement in conditional GANs.
problem Poor disentanglement of latent variables in conditional GANs.
method Information Compensation Connection (IC-Connection) for disentanglement.
result Our method achieves better disentanglement than state-of-the-art GANs.
We discuss the pricing of defaultable assets in an incomplete information model where the default time is given by a first hitting time of an unobservable process. We show that in a fairly general Markov setting, the indicator function of the default has an absolutely continuous compensator. Given this compensator we t…
This paper optimizes autonomous vehicle controllers using data-driven methods.
problem Designing robust controllers for autonomous vehicles that handle external and internal disturbances.
method Data-driven approach using principal component analysis and time delay neural networks.
result Improved controller performance through a feed-forward compensator.
New ranking algorithms improve online content delivery by learning from click data.
problem Bias in ranking systems due to production system biases.
method Proposed novel extensions of LinUCB and Linear Thompson Sampling algorithms to handle position-based click model.
result Validated the proposed algorithms through offline and online experiments.
Proposes DC-S3GD for efficient large-scale decentralized neural network training.
problem Training large-scale decentralized neural networks efficiently.
method Decentralized stale-synchronous version of DC-ASGD with gradient correction.
result Achieves state-of-the-art results in training Convolutional Neural Networks.
UDVD uses deep learning to denoise videos without supervision.
problem Lack of clean video data for training deep learning models.
method UDVD is a CNN trained solely on noisy video data, adapting to local motion.
result UDVD performs as well as supervised methods, even with limited training data.
We present two statistical causes for the distortion of correlations on high-frequency financial data. We demonstrate that the asynchrony of trades as well as the decimalization of stock prices has a large impact on the decline of the correlation coefficients towards smaller return intervals (Epps effect). These distor…
A new method reduces communication in Federated Learning by pulling less often.
problem Reducing communication overhead in Federated Learning.
method Pulling Reduction with Local Compensation (PRLC) for SGD.
result PRLC achieves lower pulling frequency and maintains the same convergence rate as synchronous SGD.
Extends weak continuity of Yang-Mills connections to a broader class.
problem Weak compactness of Ω-Yang-Mills connections. method Compensation compactness argument applied to Yang-Mills fields.
result Weak continuity result extended to Ω-Yang-Mills connections. A second order self-adjoint operator Δ=S∂2+U is uniquely defined by its principal symbol S and potential U if it acts on half-densities. We analyse the potential U as a compensating field (gauge field) in the sense that it compensates the action of coordinate transformations on the second derivatives in…
The study proves a theorem on Riemannian manifolds for wedge products of weakly convergent differential forms.
problem Analyzing the limiting behavior of wedge products of weakly convergent differential forms on Riemannian manifolds.
method Formulating and proving compensated compactness theorems for wedge products of differential forms on closed Riemannian manifolds.
result The theorem generalizes the div-curl lemma for vectorfields and applies to critical regularity exponents.
Improved signal processing for long-distance optical signals.
problem Compensating walk-off effect in long-distance optical signals.
method Sub-banded DSP architecture with deep learning for walk-off compensation.
result 2.8 dB SNR improvement over linear equalization.
Proposes a compensation mechanism for improving individual forecast confidence.
problem Difficult to assess the quality of individual probabilistic forecasts and their utilities.
method Compensation mechanism based on fair bets and online learning.
result The proposed mechanism cannot be exploited and ensures forecasted utility matches actual utility.
In this paper the method of compensated compactness is applied to the problem of isometric immersion of a two dimensional Riemannian manifold with negative Gauss curvature into three dimensional Euclidean space. Previous applications of the method to this problem have required decay of order t−4 in the Gauss curva…
A CNN-based method improves DTI of the human heart, compensating for motion.
problem Signal loss due to heart motion in DTI.
method Invertible Wavelet Scattering using CNN.
result Effective motion compensation and improved fiber structures.
Paper introduces non-linear discounting models for default compensation and climate valuation.
problem Valuation of non-replicable value and damage under default risk.
method Develops two models: one for risk-neutralising discounting and another for survival probability dependent discounting.
result Non-decaying discount factors (negative discount rates) are possible under certain scenarios.
Proposes a new method to interpret EEG classification models without needing a baseline.
problem Reliable interpretation of EEG classification models using integrated gradients.
method Compensated Integrated Gradients using Shapley sampling.
result The proposed method provides more reliable attributions than original integrated gradients.
We present a compensated compactness theorem in Banach spaces established recently, whose formulation is originally motivated by the weak rigidity problem for isometric immersions of manifolds with lower regularity. As a corollary, a geometrically intrinsic div-curl lemma for tensor fields on Riemannian manifolds is ob…
Quantized Adam reduces communication cost in deep learning training.
problem Reducing communication cost in distributed deep learning training.
method Gradient and weight quantization with error feedback in Adam.
result Proposed methods converge to first-order stationary points.
Survey on recent developments in isometric immersions using PDE techniques.
problem Analyzing isometric immersions with low Sobolev regularity.
method Compensated compactness and Coulomb-Uhlenbeck gauges.
result Weak continuity and stability of Gauss-Codazzi-Ricci equations.
One-step Bellman alignment improves online RL by reducing task mismatch.
problem Online RL struggles with task similarity defined by rewards or transitions.
method One-step Bellman alignment and re-weighted targeting (RWT) to correct task mismatch.
result Regret bounds show task shift complexity, not target MDP, affects performance.
The paper proves weak continuity of Cartan structural system on semi-Riemannian manifolds with lower regularity.
problem Weak continuity of the Cartan structural system on semi-Riemannian manifolds with lower regularity.
method Formulated and proved a geometric compensated compactness theorem, deduced Lp weak continuity of the Cartan structural system. result Weak continuity of the Cartan structural system and Gauss-Codazzi-Ricci system on semi-Riemannian manifolds with lower regularity.
Compensation methods correct overestimation of adversarial robustness in neural networks.
problem Overestimation of adversarial robustness using first-order attack methods.
method Proposed compensation methods address inaccurate gradient computation and reduce backpropagations.
result Empirical evaluation of adversarial robustness is improved with these methods.
Study proposes framework for cyber bonds to compensate cyber attack losses.
problem Cyber risk treatment in finance industry.
method Developed a framework, used publicly available data to determine loss distribution parameters, numerically simulated bond price and characteristics, considered two coupon calculation approaches.
result Numerical simulations of cyber bond price, yield, and characteristics.