Paper learns DAG models with signal-dependent variance.
problem Learning large-scale DAG models with identifiable and computationally tractable noise variance.
method Introduces QVF DAG models, introduces ODS algorithm for learning.
result ODS algorithm statistically consistent in high-dimensional settings.
New method infers human sensorimotor costs from behavior.
problem Inferring human sensorimotor costs from observed behavior.
method Inverse optimal control with signal-dependent noise.
result Recovering costs and benefits in sensorimotor behavior.
A new framework enhances binaural audio for moving talkers.
problem Real-time binaural audio enhancement for moving sound sources.
method Mixture-of-experts framework combining multiple binaural filters.
result Dynamic spatial audio rendering adapts to moving talkers.
Unified theory for optimal execution through signal-adaptive quotes in limit order books.
problem Optimal execution in limit order books with signal-dependent factors.
method Develops a unified solution theory for four execution criteria, incorporating signal-dependent drift, price impact, inventory risk, and execution risk.
result Explicit formulas reveal optimal quoting strategies and show signal-dependent drift can significantly affect execution.
We investigate how simultaneously recorded long-range power-law correlated multi-variate signals cross-correlate. To this end we introduce a two-component ARFIMA stochastic process and a two-component FIARCH process to generate coupled fractal signals with long-range power-law correlations which are at the same time lo…
Prediction markets can shape political behavior through persistent signals, not just forecast accuracy.
problem The role of prediction markets beyond forecasting.
method Transaction-level evidence from the 2024 U.S. presidential election, Signal Credibility Index (SCI).
result Price signals in prediction markets are more influential due to persistence, breadth of trader types, and cross-platform consensus.
This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images. Unmixing is performed without the use of any dictionary, and assumes that the number of constituent materials in the scene and their spectral signatures are unknown. The estimated abundances satisfy the desired sum-to-one …
The relation between performance and stress is described by the Yerkes-Dodson Law but varies significantly between individuals. This paper describes a method for determining the individual optimal performance as a function of physiological signals. The method is based on attention and reasoning tests of increasing comp…
Unified theory for adaptive image convolutions using metric perspectives.
problem Fixed kernels in convolutions limit adaptability in image processing.
method Metric perspective on images as 2D manifolds with local distances, proposing metric convolutions.
result Metric convolutions provide better generalisation and competitive performance.
L2R learns to denoise images without needing noise distribution knowledge.
problem Traditional denoising methods require noise distribution knowledge, limiting their applicability.
method L2R uses a learnable monotonic neural network to learn recorruption without distribution knowledge.
result L2R achieves state-of-the-art performance across various noise distributions.
Bayesian hyperprior stabilizes image restoration for noisy and missing data.
problem Stability and adaptability in image restoration for noisy and missing data.
method Proposes a hyperprior approach to stabilize Bayesian image restoration.
result Effective restoration of high dynamic range images from a single sensor.
This work studies scaling laws for low-precision training in high-dimensional linear regression.
problem Optimizing trade-off between model quality and training costs in high-dimensional linear regression.
method Theoretical study of scaling laws for low-precision training within a high-dimensional sketched linear regression framework, analyzing multiplicative and additive quantization.
result Multiplicative quantization maintains full-precision model size, while additive quantization reduces effective model size.
Study shows resampling labels improves classifier performance in noisy data.
problem Balancing sample size vs label reliability in noisy data.
method Comparing different validation strategies and analyzing MNIST database with varying noise levels.
result Classifier performance declines with high incorrect labels, highlighting the importance of resampling.
NR-GANs learn clean images from noisy data.
problem Learning clean images from noisy training data.
method Introduced a noise generator and distribution/transformation constraints.
result NR-GANs can generate clean images from noisy data.
New insights into deep learning via max-affine spline operators.
problem Understanding the inner workings of deep neural networks.
method Mapping deep networks to max-affine spline operators (MASOs).
result MASOs reveal signal-dependent templates and orthogonalization improves performance.
ZM-Net efficiently manipulates images with unseen signals in real-time.
problem Efficiently alter images with diverse guiding signals (e.g. paintings, attributes).
method Proposes ZM-Net, a fully-differentiable architecture that jointly optimizes TNet and PNet.
result ZM-Net performs high-quality image manipulation in real-time (tens of milliseconds per image) for unseen signals.
Sharp bounds derived for minimizing empirical variance.
problem Minimizing empirical variance over functional classes.
method Sharp non-asymptotic bounds derived under mild conditions.
result Fast convergence rates achieved including optimal non-parametric rates.
Empirical study finds variance swap rate is affine in spot variance for S&P500 data.
problem Investigating the relationship between variance swap rate and spot variance.
method Empirical analysis using S&P500 data from 2006-2018, testing different models.
result Affine relationship between variance swap rate and spot variance is supported.
This paper tackles variance issues in GNN training by proposing a method to reduce both embedding and gradient variances.
problem High variance in estimating stochastic gradients in GNN training, especially in large graphs.
method The paper proposes a decoupled variance reduction strategy that employs approximate gradient information to adaptively sample nodes with minimal variance.
result The proposed method achieves faster convergence and better generalization compared to existing sampling methods.
Study shows gradient variance increases during deep learning training, contrary to common belief.
problem Understanding and minimizing gradient variance in deep learning models.
method Gradient Clustering method using stratified sampling to minimize gradient variance.
result Gradient variance increases during training, and smaller learning rates coincide with higher variance.
Sample variance decay is shown in deep ReLU networks, impacting training dynamics.
problem Sample variance decay in deep ReLU networks during training.
method Decomposed total variance into sample variance and network-averaged sum of sample mean and variance.
result Sample variance decays in later layers of deep ReLU networks, impacting training dynamics.
Paper solves a control problem with robust methods.
problem Monotone mean-variance problems with stochastic coefficients.
method Finding saddle point through BSDEs with unbounded coefficients.
result Optimal control and value match mean-variance problems.
The paper analyzes the bias-variance tradeoff for Bregman divergences.
problem Understanding the bias-variance tradeoff for Bregman divergences.
method Analyzes the bias-variance tradeoff through operations in dual space.
result Derives several results including a generalized law of total variance and ensembling operations.
Paper proposes variance reduction for Markov chains, especially useful in MCMC.
problem Reducing variance in Markov chain additive functionals.
method Minimizes asymptotic variance of functionals over control variates.
result Significantly reduces overall finite sample variance in simulations.
Normal distributions ensure asymptotic variance reduction in moment matching Monte Carlo.
problem Asymptotic variance reduction in general integration problems.
method Characterization of conditions for asymptotic variance reduction using normal distributions.
result Asymptotic variance reduction is guaranteed for normal distributions in moment matching Monte Carlo.
Efficient variance estimation for kernel ridge regression.
problem Estimating variance in kernel ridge regression efficiently.
method Random projection approach to estimate variance.
result Optimal variance estimator for various kernels.
A new statistical concept, lepto-variance, is defined for stock returns using Regression Trees.
problem Understanding the underlying structure of stock returns using statistical methods.
method Defining lepto-variance as the variance that cannot be removed by any regression tree of a specific depth and analyzing stock returns with 1- and 2-bit Regression Trees.
result Lepto-variance quantifies the resolving power of Regression Trees for stock returns, decomposing total variance into lepto-variance and macro-variance.
Paper tackles unknown variances in best-arm identification.
problem Identifying the best arm with unknown variances in Gaussian distributions.
method Two approaches: empirical variance plugging or adapting transportation costs.
result The impact of unknown variances is small on sample complexity.
New algorithms improve best-arm identification with varying rewards.
problem Identifying the best arm with varying reward variances in fixed budget.
method Proposed two algorithms: SHVar for known variances, SHAdaVar for unknown variances; uses non-uniform budget allocation.
result Bounding misidentification probabilities for both algorithms.
Study on variance of policy gradient in simple RL environments.
problem Understanding variance of policy gradient estimators in continuous RL.
method Analyzes REINFORCE estimator in linear-quadratic environments with Gaussian noise.
result Derives and validates bounds on estimator variance empirically.
A new measure k-variance captures local distributional shape.
problem Summarizing distributional shape with local information.
method Random bipartite matchings and stochastic approximation.
result Easily approximated k-variance measures capture local distributional properties. Modern neural networks show no bias-variance tradeoff with increased parameters.
problem The traditional bias-variance tradeoff does not hold in over-parameterized neural networks.
method Empirical measurements and theoretical analysis of bias and variance in modern neural networks.
result Bias and variance can decrease as the number of parameters grows in over-parameterized neural networks.
Improved Hamiltonian Monte Carlo for Bayesian inference reduces variance and improves performance.
problem Efficiently sampling from posterior distributions in Bayesian inference with stochastic gradients.
method Variance reduction techniques applied to Hamiltonian Monte Carlo.
result Theoretical and experimental improvements in convergence and performance compared to variance-reduced Langevin dynamics.
A new model minimizes investment risk at multiple time points.
problem Minimizing risk in investment portfolios with multiple stopping points.
method Developed a multi-time state mean-variance model using Riccati equations.
result Optimal investment strategies can be derived from a sequence of Riccati equations.
Paper improves variance reduction technique for weak approximations.
problem Improving variance reduction for weak approximation schemes.
method Stratified regression-based variance reduction approach.
result Significant variance reduction achieved.
Increasing variance of losses improves learning with noisy labels.
problem Learning with noisy labels and the need to penalize variance of losses.
method Designing regularizers based on the label noise transition matrix to increase variance of losses.
result Increasing variance of losses significantly improves generalization ability.
New method corrects Markowitz variance for trading volume fluctuations.
problem Incorrect risk estimates from Markowitz variance in trading environments.
method Modeling portfolio variance based on trade volume fluctuations.
result Market-based variance can significantly differ from Markowitz variance.
Neural networks exhibit unimodal variance with model complexity, improving generalization.
problem The classical bias-variance trade-off does not apply to neural networks, leading to better generalization with larger models.
method Measured bias and variance of neural networks, confirmed empirically and theoretically.
result Neural networks show unimodal variance, leading to a double descent risk curve.
This study reviews techniques to estimate volatility and price Variance Swaps.
problem Estimating historical volatility and pricing Variance Swaps.
method Review of existing techniques.
result Discussion of various methods to estimate volatility and price Variance Swaps.
The paper extends a variance gamma model to quadratic functions, reducing arbitrage and computational costs.
problem Creating an arbitrage-free interpolation for option pricing models.
method Generalizing the local variance gamma model to a piecewise quadratic local variance function.
result The quadratic model results in an arbitrage-free interpolation of class C3, reducing knots and computational cost.
New method reduces inference variance for faster optimization.
problem High variance in black-box variational inference.
method Joint control variate addressing both data subsampling and Monte Carlo noise.
result Significantly reduced gradient variance, leading to faster optimization.
This work proposes using zero-variance control variates to reduce variance in pathwise gradient estimators for variational inference.
problem Pathwise gradient estimators in variational inference have high variance, leading to inefficient optimization.
method Apply zero-variance control variates to pathwise gradient estimators.
result Zero-variance control variates can significantly reduce the variance of pathwise gradient estimators without requiring complex assumptions.
UORO reduces gradient variance in online RNN learning.
problem Improving gradient estimates in online RNN learning.
method Analyzes and proposes variance reduction techniques for UORO.
result Reduces gradient variance both theoretically and practically.
VarGrad reduces variance in ELBO gradient estimation for variational inference.
problem Improving the variance of gradient estimators in variational inference.
method VarGrad uses a new log-variance loss to estimate the ELBO gradient, achieving lower variance than the score function method.
result VarGrad offers a lower variance gradient estimator compared to other methods.
A simple method treats heteroscedastic variance variatively, improving model calibration and sample quality.
problem Brittle optimization impacts model likelihoods for mean and variance estimation.
method Proposes a variational approach to heteroscedastic variance, improving predictive mean and variance calibration.
result The proposed method significantly improves parameter calibration and sample quality for regression and VAEs.
Optimizes MCMC chains with neural control variates.
problem Reducing variance in Markov Chain Monte Carlo (MCMC) simulations.
method Uses neural networks as control variates to minimize asymptotic variance.
result Derives optimal convergence rate under various ergodicity assumptions.
Improved LLM pre-training performance through better weight and variance control.
problem Improper weight and variance control in LLM pre-training affects downstream task performance.
method Introduced Layer Index Rescaling (LIR) and Target Variance Rescaling (TVR) techniques.
result Substantial improvements in downstream task performance (up to 4.6%) and reduced extreme activation values.
MFVI can overestimate predictive variance compared to the exact posterior
problem MFVI underestimates posterior variance
method Analyzing conjugate Bayesian Linear Regression
result MFVI can overestimate predictive variance compared to the exact posterior