arXiv research

A new approach to cost-sensitive multiclass classification prioritizes certain classes over others.

Paper proposes a new regularization method to prevent model degradation under distribution shifts.

Gradient-based methods can be biased by distributional asymmetries in bivariate categorical data.

Audited Conformal Prediction improves conditional coverage in pretrained models under distribution shift.

Paper tackles dynamic label shift in online learning, achieving optimal performance.

Paper proposes DAPNA to improve few-shot learning with domain adaptation.

DeepCCG adapts classifiers to representation shifts in one step.

Optimal transport aligns source and target distributions for domain adaptation.

Faced with distribution shift between training and test set, we wish to detect and quantify the shift, and to correct our classifiers without test set labels. Motivated by medical diagnosis, where diseases (targets) cause symptoms (observations), we focus on label shift, where the label marginal $p(y)$ changes but the …

SJS model predicts label shifts in multinomial datasets.

FJS method improves multinomial classification accuracy.

Paper proposes SJS model to estimate model performance under covariate and label shifts.

Paper introduces a method to create robust representations against covariate shifts.

New framework using Jensen-Shannon divergence improves domain adaptation theory.

Estimates calibration error under label shift without labels.

Develops conformal Bayes for two-sided censored Gaussian regression under label shift.

Proposes a method to improve regression model performance with limited target data using fused-regularizer.

The paper addresses the reliability of conformal prediction under covariate shift.

New DP algorithms with margin guarantees for various hypothesis sets.

ELSA efficiently adapts to label shift without post-prediction calibrations.

This paper explores how effective sample size, dimensionality, and model performance are related in covariate shift adaptation.

RLSbench benchmarks domain adaptation under label proportion shifts, revealing widespread failures and proposing a two-step meta-algorithm.

Nyström subsampling with Tikhonov regularization for covariate shift adaptation under misspecified case

Adaptive monitoring for AI systems detects and diagnoses shifts in data distribution.

Supervised learning with large scale labeled datasets and deep layered models has made a paradigm shift in diverse areas in learning and recognition. However, this approach still suffers generalization issues under the presence of a domain shift between the training and the test data distribution. In this regard, unsup…

The paper improves conformal prediction by analyzing the beta law of conditional coverage.

New CPS model tackles conditional probability shift in machine learning.

ReTaSA tackles continuous target shift in regression problems.

Unified analysis of kernel-based methods under covariate shift.

Causal invariance can improve finite-sample domain adaptation, but only when the target risk margins are large.

Paper introduces a novel measure to analyze excess error in classification under covariate shift.

Paper tackles CATE estimation with missing treatment info.

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…

Method constructs nonparametric prediction intervals with finite-sample guarantees.

Proposes a new method to adapt to covariate shifts in supervised learning.

Study shows pretraining and finetuning can effectively tackle covariate shift in linear regression.

New method improves consistency in preference learning for neural networks.

This paper establishes non-asymptotic learning bounds for the DR covariate shift adaptation.

Unified view of label shift estimation methods.

Domain adaptation is an important technique to alleviate performance degradation caused by domain shift, e.g., when training and test data come from different domains. Most existing deep adaptation methods focus on reducing domain shift by matching marginal feature distributions through deep transformations on the inpu…

New method for valid prediction sets in high-dimensional covariate shifts.

This paper explores conditions for neural networks to extrapolate to new domains.

Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncertainty}. Quantifying uncertainty is especially critical in real-world settings, which often involve …

This paper extends performative prediction to nonlinear cases.

MiCA regulation led to a shift in stablecoin dominance.

This paper proposes a new method to improve domain adaptation by distinguishing between marginal and dependence structure differences.

We present new minimax results that concisely capture the relative benefits of source and target labeled data, under covariate-shift. Namely, we show that the benefits of target labels are controlled by a transfer-exponent $γ$ that encodes how singular Q is locally w.r.t. P, and interestingly allows situations where tr…

Adam's bias shifts from full-batch to max-margin of different norms for separable data.