Paper explains why Dropout and BN lead to worse performance when combined and proposes solutions.
problem Worse performance when Dropout and BN are combined.
method Theoretical analysis and experiments on various networks to identify variance shift and propose solutions.
result Dropout shifts variance of a specific neural unit, while BN maintains accumulated variance, leading to unstable predictions.
The paper analyzes how re-weighting helps in reducing variance in high-dimensional kernel methods under covariate shifts.
problem The challenge of high-dimensional kernel methods under covariate shifts and the role of re-weighting.
method Derives asymptotic expansion of high-dimensional kernels under covariate shifts, analyzes bias-variance decomposition, and characterizes the regularized kernel.
result Re-weighting helps in decreasing variance and can be seen as a data-dependent regularization.
Reduces quantifier variance with accuracy optimization of base classifier.
problem Minimizing quantifier variance under prior probability shift.
method Optimizes the Brier score of a base classifier for training data.
result Optimizing Brier score on training data reduces quantifier variance on test data.
Federated learning method improves covariate shift adaptation for missing target values.
problem Missing target values in federated learning.
method Federated covariate shift adaptation algorithm for missing target output values.
result Asymptotically unbiased and efficient algorithm for federated learning.
Optimizes weights for better model performance in shifting data.
problem Improper importance weighting leads to poor model performance in data shifts.
method Interprets weights as a bias-variance trade-off and optimizes them simultaneously with model parameters.
result Optimizing weights significantly improves model generalization performance.
New algorithms learn robust policies from shifted distributions.
problem Learning robust policies in environments with distributional shifts.
method Two novel model-free algorithms: distributionally robust Q-learning and variance-reduced distributionally robust Q-learning.
result Achieves minimax sample complexity upper bound of ildeO(∣S∣∣A∣(1−γ)−4ε−2). The paper introduces a method to decompose variance in twin networks for better treatment effect estimation.
problem Accurate treatment effect estimation requires reliable uncertainty measures to locate model failures.
method Layer-wise variance decomposition using Monte Carlo Dropout in twin networks.
result The encoder component dominates under distributional shift, providing a practical diagnostic for data collection.
Active learning method balances bias and variance under class imbalance.
problem Active learning under label shift when class proportions differ.
method Mediated Active Learning under Label Shift (MALLS) using a 'medial distribution'.
result MALLS reduces asymptotic sample complexity under arbitrary label shift.
Novel hyperparameter optimization for target tasks under covariate shift.
problem Hyperparameter optimization under multi-source covariate shift.
method Construct variance reduced estimator to unbiasedly approximate target objective; propose no-regret hyperparameter optimization procedure.
result Proposed framework broadens applications of automated hyperparameter optimization.
TSFMs embed non-stationary time series data, revealing specific types of changes.
problem Understanding non-stationarity in TSFMs' embedding spaces.
method Examined mean shifts, variance changes, linear trends, and persistence in TSFMs.
result Different TSFMs exhibit distinct failure modes in detecting non-stationarity.
Proposes SVI for covariate-shift generalization with sparse variable independence.
problem Covariate-shift generalization with limited data and unstable variables.
method Introduces sparsity constraint and combines reweighting and selection in an iterative way.
result Improves covariate-shift generalization performance on synthetic and real-world datasets.
A new control chart detects shifts in binary data streams quickly and reliably.
problem Early detection of small shifts in multiple binary data streams.
method Cumulative Standardized Binomial EWMA (CSB-EWMA) chart with exact variance derivation.
result Adaptive control limits ensure robust detection across different data distributions.
Proposes using elastic demand to improve forecasting accuracy.
problem Mismatch between planned supply and actual demand due to demand variance.
method Reallocate historical elastic demand to reduce forecasting variance.
result Improves forecasting and supply planning effectiveness.
Efficiently simulates SABR model with novel sampling methods.
problem Sampling integrated variance and terminal forward price in SABR model.
method Moment-matched shifted lognormal approximation for integrated variance, CEV approximation for terminal forward price.
result Enhanced simulation scheme is highly efficient, accurate, and reliable.
Over-parameterized models reduce Out-of-Distribution (OOD) generalization loss.
problem Understanding how over-parameterized models handle non-trivial distributional shifts.
method Investigating random feature models and examining non-trivial natural distributional shifts.
result Increasing model parameterization reduces OOD loss.
Introduces MWLD to measure loss inequality across groups.
problem Machine learning's focus on average loss can lead to large group loss discrepancies.
method Defines MWLD, relates it to fairness and robustness, and provides estimation methods.
result MWLD can be estimated efficiently under certain weighting functions and reduces loss variance without significant accuracy loss.
New method predicts covariate shift with various losses and feature views.
problem Covariate shift prediction with poor performance guarantees and high variance.
method Robustly minimize various loss functions and shape influence by feature views.
result Improved robustness and applicability to more task scenarios.
Method identifies shifts leading to large model performance differences.
problem Detecting shifts in distribution that affect model performance.
method Parametric changes in causal mechanisms define robustness sets; worst-case optimization problem approximated as non-convex quadratic.
result Second-order approximation of worst-case loss for small shifts, leading to efficient algorithms.
New insights into how high-dimensional models handle covariate shifts.
problem Covariate shift in high-dimensional random feature regression.
method Exact high-dimensional asymptotics of random feature regression under covariate shift.
result Overparameterized models exhibit enhanced robustness to covariate shift.
Online monitoring system for safety classifiers with shift detection and conformal adaptation
problem Detecting and adapting to distributional shifts in deployed safety classifiers
method Calibrated sequential statistics for online monitoring, conformal abstention for adaptation
result 86.6% valid detection with mean latency of 39.5 steps
A new family of conformal test martingales based on Legendre polynomials for online exchangeability testing.
problem Detecting variance, skewness, and higher-order deviations from uniformity in online data.
method A family of conformal test martingales based on shifted Legendre polynomials.
result The Variational Legendre Jumper reduces exponential scaling to linear time with minimal loss in power.
Method improves deep learning robustness to domain shifts.
problem Domain shift robustness in deep learning.
method Conditional variance regularization (CoRe) to penalize style feature changes.
result Improves predictive accuracy in domain shifts.
REx tackles distributional shift by reducing risk differences across domains.
problem Tackling distributional shift when transferring machine learning systems to real-world applications.
method Risk Extrapolation (REx) assumes training domains represent test-time variations and uses extrapolated domains to minimize risk variance.
result REx reduces sensitivity to extreme distributional shifts, including causal and anti-causal inputs.
GS-B3SE improves label shift estimation by smoothing priors on a graph.
problem Label shift adaptation when source and target distributions share conditional but not marginal probabilities.
method Graph-Smoothed Bayesian Black-Box Shift Estimator (GS-B3SE) places Laplacian-Gaussian priors on log-priors and confusion-matrix columns tied by a label-similarity graph. result GS-B3SE produces a tractable posterior with HMC or Newton-CG schemes, proving identifiability, contraction, and robustness. Unified framework for distribution shift estimation, explanation, and improvement.
problem Estimating, explaining, and improving model performance on target domains with distribution shift.
method Entropic Projection Alignment (EPA) aligns source and target distributions by matching moments and minimizing KL divergence.
result EPA consistently outperforms state-of-the-art baselines while offering computational efficiency.
Two-stage model improves credit scoring predictions.
problem Distribution shift in finance datasets.
method Two-stage model with out-of-distribution detection and domain knowledge.
result Highly reliable predictions for most datasets.
LCW reduces activation shift in neural networks, improving training efficiency and generalization.
problem Activation shift in neural networks leading to non-zero mean preactivation values.
method Linearly constrained weights (LCW) to reduce activation shift in fully connected and convolutional layers.
result LCW resolves the vanishing gradient problem and improves generalization of neural networks.
VA-OPE improves OPE by incorporating variance information, achieving tighter error bounds.
problem Estimating value function of a target policy from offline data collected by a behavior policy.
method Proposes VA-OPE, an algorithm that reweights Bellman residual using estimated variance of the value function.
result Achieves a tighter error bound than the best-known result.
Study on estimating signals from shifted and noisy copies in high dimensions, revealing a phase transition.
problem Estimating a signal in high-dimensional space from its circularly-shifted and noisy copies.
method Analysis of sample complexity in the high-dimensional regime, focusing on the parameter α.
result A phase transition phenomenon governed by α, with different sample complexities based on α values.
A new estimator combines KMM and NR to robustly correct covariate shift.
problem Correcting sampling biases in learning problems with different distributions.
method Integrates residuals of nonparametric regression with kernel mean matching reweighting.
result Proposed estimator outperforms or matches existing rates for KMM and NR.
Optimal feature transfer identified through bias-variance analysis.
problem Optimizing feature transfer in transfer learning.
method Simple linear model with fine-grained bias-variance decomposition.
result Optimal pretrained feature transform is naturally sparse.
The paper analyzes Nordic stock markets' correlation structures and regime shifts.
problem Understanding and exploiting regime shifts in Nordic stock markets.
method Examined two decades of daily data for OMXS30, OMXC20, and OMXH25 universes; proposed an adaptive portfolio allocation framework.
result Documented pronounced regime dependence in rolling correlation matrices; proposed an adaptive portfolio allocation framework.
A new method for covariate shift adaptation using nearest neighbors.
problem Mitigating distribution shift between source and target datasets.
method Directly work on unlabeled target data, labeled by nearest neighbors in source data.
result Optimal choice of k=1 simplifies hyper-parameter tuning and improves efficiency. A robust method for off-policy evaluation in contextual bandits.
problem Evaluating policies when direct methods are unavailable.
method Robust regression approach to off-policy evaluation.
result Superior empirical performance across benchmarks.
We solve continuous-time latent SDE identifiability using diffusion shifts.
problem Identifiability of latent SDEs in continuous-time time series.
method Environment-induced shifts in diffusion covariance for additive-noise latent SDEs.
result Two diagonal diffusion regimes with distinct variance ratios identify latent coordinates up to permutation and scaling.
New method optimizes model selection in high-dimensional regression models.
problem Model selection in high-dimensional misspecified regression models with covariate shift.
method Importance-weighted orthogonal greedy algorithm (IWOGA) and high-dimensional importance-weighted information criterion (HDIWIC).
result IWOGA + HDIWIC achieves optimal convergence rates in terms of prediction error.
RAVEN-UCB addresses non-stationary MAB problems with tighter regret bounds.
problem Non-stationary environments in multi-armed bandits.
method Combines variance-aware adaptation with three innovations: confidence bounds, adaptive control, and recursive updates.
result Achieves tighter regret bounds than UCB1 and UCB-V.
New method prunes classification trees for biased data.
problem Pruning classification trees in imbalanced training data.
method Optimal pruning procedure for inhomogeneous data.
result First efficient procedure for optimal pruning under covariate shift.
The Multi Variate Mixture Dynamics model is a tractable, dynamical, arbitrage-free multivariate model characterized by transparency on the dependence structure, since closed form formulae for terminal correlations, average correlations and copula function are available. It also allows for complete decorrelation between…
A new algorithm reduces bias and variance in distributionally robust optimization.
problem Distributionally robust optimization with bias and variance issues.
method Prospect, a stochastic gradient-based algorithm that reduces hyperparameter tuning.
result Prospect achieves linear convergence and 2-3x faster convergence on various benchmarks.
Traditionally, multi-layer neural networks use dot product between the output vector of previous layer and the incoming weight vector as the input to activation function. The result of dot product is unbounded, thus increases the risk of large variance. Large variance of neuron makes the model sensitive to the change o…
Paper proposes an online adaptation algorithm for improving model performance.
problem Improving model fidelity in real-time for domain shift and time variance.
method Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch strategy.
result Proposed algorithm outperforms existing methods in experiments.
Robust machine learning models improve DNA regulatory sequence prediction under various shifts.
problem Real-world applications of DNA regulatory sequence prediction involve shifts not captured by standard i.i.d. assumptions.
method Introduces a robustness framework combining simulation benchmarks and real data analysis.
result Models remain accurate and calibrated under mild shifts but show higher error and miscalibration under strong shifts.
The paper analyzes the generalization error of min-norm interpolators in transfer learning with limited test samples.
problem Characterizing the generalization error of min-norm interpolators in transfer learning with limited test samples.
method Characterizes the bias and variance of pooled min-ℓ2-norm interpolation under covariate shift and model shift. result Shows that adding data can hurt when SNR is low and is beneficial at higher SNR levels under certain conditions.
A new method for anomaly detection adapts to local non-stationarity in low-data regimes.
problem Adapting conformal anomaly detection to handle distribution shifts in real-world data.
method Proposes a continuous inference relaxation using continuous weighted kernel density estimation to decouple local adaptation from tail resolution.
result Restores detection capabilities and statistical power in low-data regimes while maintaining valid error control.
TransCal calibrates DA models with lower bias and variance.
problem Calibrating DA models to estimate accurate predictive uncertainty.
method Transferable Calibration (TransCal) in a unified hyperparameter-free optimization framework.
result TransCal achieves more accurate calibration with lower bias and variance.
A new method for time series analysis that highlights important signals.
problem Finding signals that matter most in time series data.
method Contrastive Multivariate Singular Spectrum Analysis (CMSA) using a background dataset.
result CMSA identifies signals that are more relevant to the analyst than those with the highest variance.
The paper analyzes how combining samples from two tasks can improve performance, especially in high dimensions.
problem Understanding when combining samples from two related tasks outperforms learning with one task alone.
method Applying random matrix theory to high-dimensional linear regression, focusing on proportional sample size increases.
result Precise high-dimensional asymptotics for bias and variance of HPS estimator, showing phase transitions in transfer performance.