A new method for adapting to label shifts using class probability matching.
problem Adapting to label shifts where class probabilities differ between source and target domains.
method Class Probability Matching using Kernel Methods (CPMKM) framework.
result CPMKM outperforms existing methods on real datasets.
Paper tackles robust classification under class-dependent domain shift.
problem Class-dependent domain shift in machine learning.
method Defined a simple optimization problem with an information theoretic constraint and solved it using neural networks.
result Demonstrated that the proposed method can learn robust classifiers that generalize well to unseen domains.
Proposes a method to adapt to new classes in a domain shift.
problem Learning new classes in a domain shift without labeled supervision.
method Inspired by prototypical networks, the method classifies target samples into shared and novel classes.
result Superior performance compared to DA and CI methods in the CIDA paradigm.
New method needed for class prior estimation when covariates are reduced.
problem Class prior estimation fails under covariate shift when covariates are reduced.
method Propose a probing algorithm for class prior estimation.
result Provable transformations preserving covariate shift are necessary for class prior estimation.
FJS method improves multinomial classification accuracy.
problem Improving multinomial classification accuracy under dataset shift.
method Derive FJS representation and propose alternative methods.
result Factorizable joint shift is not fully identifiable without additional assumptions.
SJS model predicts label shifts in multinomial datasets.
problem Predicting label shifts in multinomial datasets.
method Sparse joint shift model for dataset shift.
result Valid predictions and class prior probabilities estimates.
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.
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.
Friedman's method performs well for estimating class distributions.
problem Estimating prior class probabilities without label observations.
method Friedman's method and DeBias method for designing linear equation systems.
result Friedman's method performs well for binary and multi-class quantification.
Detects changes in classifier scores to identify shifts in class priors.
problem Label shift changes in classification data.
method Sequential changepoint detection of classifier scores.
result Outperforms other detection procedures in label shift settings.
Big mapping class groups of infinite type surfaces have infinite asymptotic dimension.
problem Understanding asymptotic dimension of big mapping class groups of infinite type surfaces.
method Analyzing big mapping class groups with coarsely bounded generating sets and essential shifts.
result Big mapping class groups of infinite type surfaces have infinite asymptotic dimension.
New method for estimating class proportions in open-set label shift data.
problem Estimating class proportions and distributions when test data includes novel classes.
method Semiparametric density ratio model framework with maximum empirical likelihood estimators and confidence intervals.
result Improved estimation accuracy and classification performance compared to existing methods.
Study reveals differences in label shift problem difficulty in supervised vs. unsupervised settings.
problem Label shift problem in non-parametric classification.
method Analysis of minimax rates in supervised and unsupervised settings, focusing on class conditional distributions estimation.
result A class proportion estimation approach is minimax rate-optimal in the unsupervised setting.
New bounds for contrastive learning handle domain shifts and generalization.
problem Domain shifts and generalization challenges in downstream tasks.
method Novel generalization bounds accounting for both domain shift and generalization.
result Performance of contrastively learned representations depends on statistical discrepancy between pretraining and downstream distributions.
RLSbench benchmarks domain adaptation under label proportion shifts, revealing widespread failures and proposing a two-step meta-algorithm.
problem Domain adaptation under label proportion shifts is poorly understood and inconsistent across methods.
method RLSbench introduces a large-scale benchmark with 500 distribution shift pairs. It proposes a two-step meta-algorithm to improve domain adaptation methods under label proportion shifts.
result The two-step meta-algorithm improves domain adaptation methods by 2-10% accuracy points under large label proportion shifts.
Paper tackles target shift in zero-shot learning using adversarial learning.
problem Target shift in zero-shot learning leads to performance degradation.
method Estimates target shift using class-attribute mapping and applies grouped adversarial learning.
result Improves zero-shot learning performance on multiple datasets.
The Grassmannian model represents harmonic maps from Riemann surfaces by families of shift-invariant subspaces of a Hilbert space. We impose a natural symmetry condition on the shift-invariant subspaces that corresponds to considering an important class of harmonic maps into symmetric and k k k -symmetric spaces. In parti…
Continues work on derived manifolds and symplectic schemes, constructing virtual classes.
problem Constructing virtual fundamental classes for derived manifolds and schemes.
method Cosection localization, reduced virtual fundamental classes, and applications to Donaldson-Thomas theory.
result Virtual fundamental classes for ( − 2 ) (-2) ( − 2 ) -shifted symplectic derived schemes are consistent with algebraic and differential geometric constructions. 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.
In this paper, we study two classes of planar self-similar fractals T ε T_\varepsilon T ε with a shifting parameter ε \varepsilon ε . The first one is a class of self-similar tiles by shifting x x x -coordinates of some digits. We give a detailed discussion on the disk-likeness ({\it i.e., the property of being a topological disk}…
We study density estimation for classes of shift-invariant distributions over R d \mathbb{R}^d R d . A multidimensional distribution is "shift-invariant" if, roughly speaking, it is close in total variation distance to a small shift of it in any direction. Shift-invariance relaxes smoothness assumptions commonly used in non-p…
GS-B 3 ^3 3 SE 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-B 3 ^3 3 SE) places Laplacian-Gaussian priors on log-priors and confusion-matrix columns tied by a label-similarity graph. result GS-B 3 ^3 3 SE produces a tractable posterior with HMC or Newton-CG schemes, proving identifiability, contraction, and robustness. DeepCCG adapts classifiers to representation shifts in one step.
problem Adapting classifiers to shifts in continuous representation.
method Empirical Bayesian approach using class conditional Gaussian classifier and KL divergence for selection.
result DeepCCG reduces performance change due to representation shifts.
Bayesian framework estimates label shift for improved classifier performance.
problem Label shift in supervised learning leading to degraded classifier performance.
method Bayesian framework with dynamic Dirichlet priors and online EM algorithms.
result Significant improvements in classifier accuracy over state-of-the-art methods.
This paper rethinks confidence calibration under covariate shifts.
problem Calibration methods struggle with covariate shifts and unstable importance weighting.
method Derives Expectation consistency condition and proposes Expectation consistency loss (ECL).
result ECL loss is compatible with various types of calibration and has the same sample complexity as ECE.
Optimal transport method rejects new classes and adjusts class ratios for open set domain adaptation.
problem Handling new classes in target domains with distribution shifts.
method Two-step optimal transport approach: reject new classes first, then adjust class ratios.
result Outperforms state-of-the-art methods in open set domain adaptation.
Study on tracking preference shifts in dueling bandits problems.
problem Tracking significant preference shifts in dueling bandits problems.
method Analysis of dueling bandits with distribution shifts, focusing on significant shifts (Suk and Kpotufe, 2022).
result Design of adaptive algorithms with O ( K i l d e L T ) O(\sqrt{K ilde{L}T}) O ( K i l d e L T ) dynamic regret for certain preference distribution classes. CSI detects novelty by contrasting shifted instances, outperforming existing methods.
problem Detecting samples from outside the training distribution.
method Contrastive learning with distributionally shifted augmentations.
result CSI outperforms existing methods in various novelty detection scenarios.
Paper proposes DAPNA to improve few-shot learning with domain adaptation.
problem Improving recognition of unseen classes with limited samples.
method Domain adaptation prototypical network with attention (DAPNA).
result DAPNA outperforms state-of-the-art FSL alternatives in experiments.
Paper tackles dynamic label shift in online learning, achieving optimal performance.
problem Adapting to changing class marginals in online supervised and unsupervised learning.
method Develops novel algorithms reducing adaptation to online regression, achieving optimal dynamic regret.
result Achieves superior performance in various online label shift scenarios.
Optimally tackles covariate shift in RKHS-based nonparametric regression.
problem Covariate shift in nonparametric regression over RKHS.
method Two families of covariate shift problems defined using likelihood ratios. Minimax rate-optimal estimators for KRR and reweighted KRR.
result KRR is minimax rate-optimal and strictly sub-optimal compared to naive estimator under covariate shift.
The paper proposes a method to assess when automated predictions are reliable.
problem Ensuring reliability and safety of automated decision-making in machine learning.
method Clustering to measure distances between outputs and class centroids, defining a safety threshold based on these distances.
result The proposed metric can efficiently determine when automated predictions are acceptable and when they should be deferred.
Bottlenecks of binary classification from positive and unlabeled data (PU classification) are the requirements that given unlabeled patterns are drawn from the test marginal distribution, and the penalty of the false positive error is identical to the false negative error. However, such requirements are often not fulfi…
Extended flatness approach for discrete-time systems considers forward and backward shifts.
problem Defining flatness for discrete-time systems with forward-shifts.
method Introducing backward-shifts to extend flatness definition.
result Extended flat systems maintain key properties like reachability and controllability.
The purpose of this paper is to investigate shifted ( + 1 ) (+1) ( + 1 ) Poisson structures in context of differential geometry. The relevant notion is shifted ( + 1 ) (+1) ( + 1 ) Poisson structures on differentiable stacks. More precisely, we develop the notion of Morita equivalence of quasi-Poisson groupoids. Thus isomorphism classes of ( + 1 ) (+1) ( + 1 ) …
Speech emotion recognition plays an important role in building more intelligent and human-like agents. Due to the difficulty of collecting speech emotional data, an increasingly popular solution is leveraging a related and rich source corpus to help address the target corpus. However, domain shift between the corpora p…
Study evaluates conformal prediction methods for safety in vision models under shifts and long-tailed data.
problem Safety guarantees of conformal prediction methods under distribution shifts and long-tailed data.
method Empirical evaluation of post-hoc and training-based conformal prediction methods on large-scale datasets and models.
result Performance of conformal prediction methods degrades significantly under distribution shifts and long-tailed data.
In safety-critical applications of machine learning, it is often important to abstain from making predictions on low confidence examples. Standard abstention methods tend to be focused on optimizing top-k accuracy, but in many applications, accuracy is not the metric of interest. Further, label shift (a shift in class …
New method removes pseudo-label bias for unsupervised domain adaptation.
problem Class imbalance and distribution shift between domains.
method Implicit class-conditioned domain alignment without explicit pseudo-label optimization.
result Effective in handling within-domain class imbalance and between-domain class distribution shift.
BREEDS benchmarks assess model robustness to subpopulation shifts.
problem Measuring model robustness to novel subpopulation shifts.
method Controlled synthesis of realistic distribution shifts using class structure.
result Validated model sensitivity and effectiveness of robustness interventions.
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.
The mean shift algorithm is a popular way to find modes of some probability density functions taking a specific kernel-based shape, used for clustering or visual tracking. Since its introduction, it underwent several practical improvements and generalizations, as well as deep theoretical analysis mainly focused on its …
The paper analyzes how machine learning models perform under covariate shift, especially when the feature shift in x x x is larger than that in y y y .
problem Performance of machine learning models under covariate shift with heterogeneous feature changes.
method Empirical risk minimization (ERM) over functions f + g f+g f + g , fit on a training distribution, evaluated on a test distribution with covariate shift. result ERM is more resilient to heterogeneous covariate shifts when the class F F F is simpler than G G G . Paper tackles efficient risk estimation under dataset shift conditions.
problem Limited data from target population; auxiliary data available.
method Semiparametric efficiency theory; efficient and multiply robust estimators.
result Developed estimators for various dataset shift conditions.
Paper tackles unsupervised learning under latent label shift across domains.
problem Discovering classes from unlabeled data with shifting label distributions.
method Introduces unsupervised learning under Latent Label Shift (LLS), leveraging domain-discriminative models.
result Proves that with domain information, unsupervised classification can improve upon standard methods.
Optimizes feature shifts for tree ensemble reclassification.
problem Improving tree ensemble reclassification accuracy through feature perturbation.
method Mathematical optimization of feature shifts to maximize reclassification probability.
result Effective feature ranking and optimization for tree ensemble reclassification.
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.
End-to-end PGL framework tackles open-set domain shift.
problem Real-world domain shift with unknown additional classes.
method Episodic training in graph neural network with adversarial learning.
result Guarantees tighter upper bound of target error.