Self-training improves gradual domain adaptation with unlabeled data.
problem Improving machine learning models' adaptability to gradually shifting data distributions.
method Proved upper bounds on self-training error, highlighted the importance of regularization and label sharpening, and demonstrated algorithmic insights.
result Self-training works well for gradual shifts, especially with small Wasserstein-infinity distance.
IUPM monitors machine learning models under gradual shifts using optimal transport and active labeling.
problem Gradual distribution shifts lead to unnoticed accuracy declines in machine learning models.
method Incremental Uncertainty-aware Performance Monitoring (IUPM) using optimal transport and active labeling.
result IUPM outperforms existing baselines in gradual shift scenarios and guides label acquisition more effectively.
Paper proposes a method to design molecules with specific properties.
problem Designing molecules with desired chemical and biological properties.
method Energy-based model in latent space, SGDS algorithm for gradual distribution shifting.
result Method achieves strong performances on various molecule design tasks.
The paper tackles gradual domain adaptation with manifold-constrained DRO, showing error bounds across distributions.
problem Gradual domain adaptation challenge with manifold-constrained data distributions.
method Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius.
result Theoretical bounds on classification error across distributions, demonstrating error propagation dynamics.
STAD adapts models to evolving time-based data shifts.
problem Gradual distribution shifts over time challenge existing test-time adaptation methods.
method Bayesian filtering method that learns time-varying dynamics in hidden features.
result STAD excels in handling small batch sizes and label shift on real-world data.
A new method uses normalizing flows for gradual domain adaptation.
problem Difficulty in domain adaptation when source and target domains have a large gap.
method Proposes using normalizing flows to learn a transformation from target to Gaussian mixture distribution.
result Improves classification performance and mitigates the problem of gradual self-training failure.
BRPC online Bayesian calibration handles gradual and abrupt system changes.
problem Aligning model outputs with field observations in evolving systems.
method Bayesian Recursive Projected Calibration (BRPC) for streaming data under simulator mismatch and nonstationarity.
result Improves calibration accuracy under gradual changes and robustness under abrupt regime shifts.
DeRegiME forecasts with regime structure, improving probabilistic predictions across various time series.
problem Probabilistic forecasting discards residual uncertainty, and distribution shifts are hard to capture.
method DeRegiME uses a sparse variational Gaussian process with a nonstationary regime-mixing kernel to separate latent uncertainty regimes.
result DeRegiME improves NLPD by 20.3% on average across benchmarks, with gains on CRPS and MSE.
Drift-Resilient TabPFN learns to adapt to changing data distributions.
problem Real-world data often shifts over time, degrading model performance.
method In-Context Learning with a Prior-Data Fitted Network, using structural causal models.
result Significant performance improvements across various datasets.
Gradual domain adaptation improves model transfer between domains with intermediate training.
problem Challenges in unsupervised domain adaptation when distribution shifts are large.
method Gradual self-training using intermediate domains along the Wasserstein geodesic.
result GOAT framework generates intermediate domains for improved adaptation.
Study tackles distribution shift in combinatorial settings using matrix completion techniques.
problem Tackling distribution shift in combinatorial settings with rigorous statistical guarantees.
method Develops novel algorithms and theoretical results for extrapolating to test distributions not covered in training.
result Achieves bilinear combinatorial extrapolation under gradual spectral decay in high-dimensional data.
Gradually Truncated Log-normal distribution - Size distribution of firms Abstract Many natural and economical phenomena are described through power law or log- normal distributions. In these cases, probability decreases very slowly with step size compared to normal distribution. Thus it is essential to cut-off these di…
A cost-effective framework for gradual domain adaptation using multifidelity.
problem Degrading prediction performance due to large domain distance.
method Combines multifidelity and active domain adaptation.
result Improves prediction performance with reduced sample cost.
Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as they process data, requiring very low learning rates. If the training distribution shifts, the network is slow to adapt, and when it does adapt…
DGSAM improves domain generalization by minimizing individual sharpness.
problem Improving domain generalization models that perform well on unseen target domains.
method Shifts DG paradigm toward minimizing individual sharpness across source domains.
result DGSAM reduces performance variance across domains with less computational overhead.
A feature-weighted mean shift algorithm improves clustering in high-dimensional data.
problem Clustering high-dimensional data with traditional mean shift algorithms.
method Feature-weighted mean shift algorithm.
result The algorithm outperforms conventional mean shift and preserves computational simplicity.
A TTA framework improves forecasting accuracy in non-stationary time series.
problem Improving forecasting accuracy in non-stationary time series.
method Normalization-based test-time adaptation for causal timeseries forecasting and direction classification.
result Normalization-based TTA improves forecasting error in synthetic gradual drift and can even hurt in aggressive norm-only adaptation in financial markets.
Scales gradual pattern discovery from imprecise data.
problem Managing large collections of gradual patterns from imprecise data.
method Introduces gradualness thresholds and considers attribute value distribution.
result Eliminates noise patterns, showing a smaller set of meaningful patterns.
Paper proposes an adaptive gradient method for federated learning.
problem Communication efficiency in federated learning.
method Adaptive gradient method for federated learning.
result Guaranteed convergence and communication efficiency in federated learning.
Algorithm balances learning and coverage for multi-robots over unknown fields.
problem Balancing learning and coverage for multi-robots over unknown, nonuniform sensory fields.
method DSLC algorithm that schedules learning and coverage epochs, using Gaussian Process modeling and coverage regret analysis.
result Upper bound on expected cumulative coverage regret provided for DSLC.
Streaming adaptations of manifold learning based dimensionality reduction methods, such as Isomap, are based on the assumption that a small initial batch of observations is enough for exact learning of the manifold, while remaining streaming data instances can be cheaply mapped to this manifold. However, there are no t…
First-best climate policy is a uniform carbon tax which gradually rises over time. Civil servants have complicated climate policy to expand bureaucracies, politicians to create rents. Environmentalists have exaggerated climate change to gain influence, other activists have joined the climate bandwagon. Opponents to cli…
New approach models how explanations shift with distribution changes.
problem Model performance drops with changing input data distributions.
method Models explanation shifts and compares them to state-of-the-art techniques.
result Modeling explanation shifts better detects out-of-distribution behavior.
Unified framework certifies predictor performance under distribution shift.
problem Certifying predictor performance under distribution shift.
method Unified framework with explicit inequalities, sound verification, and identifiable structure.
result Explicit upper bound on excess risk under shift.
This research examines how model explanations change under distribution shifts in tabular data.
problem Detecting distribution shifts in tabular data affecting model performance and explanations.
method Investigates the relationship between model performance and explanation characteristics under distribution shifts.
result Explanation shifts are a better indicator for detecting predictive performance changes than traditional distribution shift techniques.
Paper tackles high-dimensional quantile regression with distribution shift using transfer learning.
problem Efficiency of knowledge transfer is severely impacted by distribution shift in high-dimensional regression.
method Proposes a novel transferable set and framework for three types of distribution shift: parameter, covariate, and residual.
result Establishes estimation error bounds and source detection consistency for the proposed method.
This work evaluates graph models' robustness to structural distributional shifts.
problem Evaluating graph models' robustness to structural distributional shifts.
method Proposes a general approach for inducing diverse distributional shifts based on graph structure.
result Simple models often outperform more sophisticated methods on structural distributional shifts.
A new policy for contextual bandits adapts to reward vector shifts.
problem Learning under reward vector shifts with ordered rewards.
method Adaptive-discretization and optimistic elimination policy.
result Established upper bounds on preference-based regret.
Dark Experience improves continual learning with a simple, strong baseline.
problem General Continual Learning in scenarios where tasks are not sequential and offline training is not possible.
method Mixing rehearsal with knowledge distillation and regularization.
result Dark Experience outperforms consolidated approaches and leverages limited resources.
Paper proposes SJS model to estimate model performance under covariate and label shifts.
problem Estimating model performance when both covariates and labels shift.
method Sparse Joint Shift (SJS) model and SEES algorithm.
result SEES achieves significant shift estimation error improvements over existing approaches.
Paper proposes a framework to detect distribution shifts using embedding space geometry.
problem Detecting distribution shifts in candidate datasets to improve model generalizability.
method Non-parametric framework using embedding space geometry for two tests: robustness boundary and in-distribution/out-of-distribution classification.
result Both tests successfully detect distribution shifts in various scenarios for both synthetic and real-world datasets.
AdapTable adapts tabular models to shifts without source data, improving HELOC performance.
problem Distribution shifts in tabular data threaten model performance.
method Shift-aware uncertainty calibrator and label distribution handler.
result Up to 16% improvement on HELOC dataset.
New perspective on distribution shift helps make learning easier.
problem Learning with different training and target distributions.
method Formalizing and exploring Positive Distribution Shift (PDS).
result Distribution shift can be positive, making learning easier.
Study evaluates methods for improving model robustness to various real-world distribution shifts.
problem Improving model robustness to real-world distribution shifts like geographic changes.
method Introduced new datasets and evaluated existing methods on four types of shifts (style, blurriness, location, camera operation).
result Data augmentations and larger models can improve robustness on real-world distribution shifts, contrary to prior claims.
New CPS model tackles conditional probability shift in machine learning.
problem Discrepancy between source and target distributions in machine learning.
method Conditional Probability Shift Model (CPSM) using multinomial regression and EM algorithm.
result Superior balanced classification accuracy on target data compared to existing methods.
Study shows current image classification models lack robustness to real-world dataset shifts.
problem Robustness of current image classification models to natural distribution shifts in real datasets.
method Evaluation of 204 ImageNet models in 213 different test conditions.
result Little to no transfer of robustness from synthetic to natural distribution shifts.
Extends FJS analysis to general label spaces, including classification and regression.
problem Distribution shift in general label spaces, including covariate and label shifts.
method Proposes a framework for analyzing FJS in general label spaces and generalizes existing results.
result Generalizes FJS analysis to general label spaces, including classification and regression.
Graphs models are vulnerable to distribution shifts, which this work explains and mitigates.
problem Graph Neural Networks (GNNs) are susceptible to distribution shift, leading to performance degradation.
method Theoretical analysis quantifying conditional shift, proposing an approach to estimate and minimize it.
result The proposed approach demonstrates up to 10% absolute ROC AUC improvement under various distribution shifts.
Unsupervised learning representations generalize better than supervised learning under distribution shifts.
problem Robustness of unsupervised representations to distribution shift.
method Extensive evaluation on synthetic and realistic datasets, including controllable domain generalization datasets.
result Unsupervised representations learned from SSL and AE generalize better than supervised learning under various distribution shifts.
Modeling financial market dynamics with noise and fundamentalist agents.
problem Understanding opinion formation and market behavior in financial markets.
method Agent-based model with Erdös-Rényi random graph structure, incorporating anxiety parameter.
result Model accurately reproduces key market features like fat-tailed returns and volatility clustering.
Improved analysis of gradual domain adaptation with better generalization bounds.
problem Improving generalization in target domain through intermediate unlabeled domains.
method Analyzed gradual self-training under more general assumptions, proving a new generalization bound.
result Proved a significantly improved generalization bound of ε0 + O(TΔ + T/√n) + ˜O(1/√nT).
Develops estimators for near-optimal linear regression under distribution shift.
problem Linear regression under distribution shift with scarce target domain data.
method Minimax linear risk estimators covering various transfer learning settings.
result Achieves near-optimal risk for linear regression problems under distribution shift.
ProteuS generates synthetic financial data with regime changes for testing drift detection.
problem Simulating concept drift in financial markets for model evaluation.
method ARMA-GARCH models fitted to ETF data, generating synthetic time series with predefined regime changes.
result Generated datasets reveal the complexity of detecting and adapting to market regime changes.
Unified learning bound for covariate and concept shifts.
problem Generalization under distribution shift in machine learning.
method Support-agnostic definitions of covariate and concept shifts using entropic optimal transport, leading to a unified error bound applicable to various loss functions and label spaces.
result Development of estimators for shifts with concentration guarantees and the DataShifts algorithm for quantifying and estimating the error bound.
Combines adversarial and interventional robustness for machine learning models.
problem Designing robust models for distribution shifts in machine learning.
method RISe formulation using distributionally robust optimization.
result Demonstrates efficacy of RISe approach with synthetic and real-world datasets.
Paper develops a new method to improve model calibration under distribution shifts.
problem Challenges in uncertainty quantification with different training and test distributions.
method Develops multi-domain temperature scaling to handle distribution shifts.
result Outperforms existing methods on in-distribution and out-of-distribution test sets.
New method learns distribution shifts caused by predictive models in social computing.
problem Learning distribution shifts due to predictive models in social computing.
method Reverse causal model with microfoundation for agents' actions.
result Effective in minimizing performative prediction risk.
This paper improves conformal prediction for robust interval estimation under distribution shifts.
problem Robustness of conformal prediction under distribution shifts.
method Modeling distribution shifts using Levy-Prokhorov (LP) ambiguity sets, which capture both local and global perturbations.
result Constructs robust conformal prediction intervals that remain valid under distribution shifts.