AM-PPI uses multiple predictors to reduce label cost in healthcare AI.
problem Reduces label cost in post-deployment monitoring of healthcare AI.
method Combines model predictions with a small labeled sample, routing each instance to a cost-appropriate subset of predictors.
result Produces narrower confidence intervals than single-predictor methods.
Paper introduces SUEL model for integrating predictors without labeled data.
problem Combining predictors with unknown accuracy and high correlation.
method Structured unsupervised ensemble learning (SUEL) with correlation-based decomposition algorithms.
result Efficient integration of dependent predictors without labeled data.
We analyze the local Rademacher complexity of empirical risk minimization (ERM)-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learning. Rather than using the trace norm to regularize the multi-label predictor, we instead minimize the tail sum of the singular values of th…
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 …
CAOS aggregates multiple one-shot predictors for efficient uncertainty quantification.
problem Lack of principled uncertainty quantification in one-shot prediction.
method CAOS, a conformal framework that aggregates multiple one-shot predictors and uses a leave-one-out calibration scheme.
result CAOS produces smaller prediction sets with reliable coverage compared to split conformal baselines.
Paper proposes a method to recover accurate labels from partially valid data in multi-label learning.
problem Tackles noisy supervision in multi-label learning with partially valid labels.
method Develops a two-stage method that estimates label enrichment and ground-truth confidences.
result Demonstrates improved performance over state-of-the-art PML methods.
Bayesian framework evaluates predictors of subjective visual tasks.
problem Evaluating uncertainty in machine learning predictors for tasks with subjective annotations.
method Bayesian framework to estimate epistemic uncertainty from human labels.
result Framework successfully applied to four image classification tasks.
PML-LFC improves PML by estimating label confidence from both feature and label spaces.
problem PML challenges in real-world scenarios where only some labels are relevant.
method PML-LFC estimates label confidence using feature and label space similarities, training a predictor with these values.
result PML-LFC achieves superior performance on synthetic and real-world datasets.
Extends risk control to adaptive data collection, anytime-valid guarantees.
problem Ensuring safety of machine learning models with critical risk measures.
method Sequential risk controlling prediction sets (RCPS) for adaptive data collection and active labeling.
result Anytime-valid guarantees for risk control in sequential data collection.
This paper continues study, both theoretical and empirical, of the method of Venn prediction, concentrating on binary prediction problems. Venn predictors produce probability-type predictions for the labels of test objects which are guaranteed to be well calibrated under the standard assumption that the observations ar…
New method calibrates uncertainty estimates for image classifiers without labeled data.
problem Uncertainty estimates for modern classifiers are unreliable without labeled calibration data.
method Calibrates uncertainty estimates using unlabeled examples for distribution shifts.
result Proposes a method that provides excellent uncertainty estimates under natural distribution shifts.
Proposes efficient calibration for indoor localization models.
problem Calibration data scarcity in wireless indoor localization.
method Uses synthetic labels and prediction sets to fine-tune a predictor and estimate bias.
result Yields rigorous coverage guarantees for prediction sets.
A Bayesian approach to multilabel classification using tree-based models.
problem Challenges in multilabel classification due to complex label relationships and correlations.
method Bayesian Additive Regression Trees (BART) framework for modeling multilabel classification.
result Improved predictive performance compared to other models, including an oracle model.
New method calibrates multi-class predictions efficiently without sacrificing accuracy.
problem Efficiently calibrating multi-class predictions without sacrificing accuracy.
method Formulated robust projected smooth calibration and new recalibration algorithms.
result Achieves strong guarantees for binary classification tasks with polynomial complexity.
SFB uses stable features to adapt unstable ones for better performance.
problem Improving classifier performance on out-of-distribution data by leveraging stable features.
method SFB learns a predictor that separates stable and unstable features, then adapts unstable predictions using stable predictions.
result SFB can learn an asymptotically-optimal predictor without test-domain labels.
A new method makes adversarial domain adaptation aware of class relationships.
problem Ignoring inter-class semantic relationships in domain adaptation.
method RADA algorithm that aligns inter-class dependencies learned from domain discriminator with those from label predictor.
result Improves performance on benchmark datasets by incorporating class relationships.
Transfer learning improves sentiment classification using XR framework.
problem Lack of labeled data for deep learning.
method XR framework applied to transfer learning between related tasks, using expected label proportions.
result Improved performance on aspect-based sentiment classification.
This work improves structured prediction by learning the balance between signal and random noise.
problem Structured prediction with random perturbations.
method Learning the variance of randomized structured predictors to balance signal and noise.
result Learning the balance improves structured prediction effectiveness.
ETM models improve efficiency in semi-supervised logistic regression.
problem Improving efficiency in logistic regression with limited labeled data.
method Developed exponential tilt mixture (ETM) models for semi-supervised estimation.
result ETM-based estimation demonstrates improved efficiency over supervised logistic regression.
A new IL framework estimates invariant predictors with single domain data.
problem Deep networks inherit spurious correlations and fail on unseen domains.
method Assumes multiple labeled domains for higher-level tasks, uses single domain for target task, employs cross-validation for hyperparameter selection.
result Empirically demonstrates effectiveness and correctness of hyperparameter selection.
New truthful calibration errors improve model ranking in multiclass prediction.
problem Non-truthful calibration errors can mislead model comparisons.
method Introduced perfectly truthful calibration errors for multiclass predictions.
result Truthful calibration errors preserve decision-theoretic dominance and stabilize model rankings.
Efficiency criteria improve conformal predictors' performance.
problem Improving the performance of conformal predictors.
method Learning classifiers by minimizing observed fuzziness as a training objective function.
result Conformal predictors trained by minimizing observed fuzziness perform better than traditional ones.
MEC improves efficiency and robustness in semi-supervised inference.
problem Efficient inference with limited labeled data and robust uncertainty quantification.
method Machine-Learning-Assisted Generalized Entropy Calibration (MEC) using cross-fitted, calibration-weighted PPI.
result MEC achieves semiparametric efficiency bounds under weaker assumptions and provides near-nominal coverage.
Adapts to shifts in latent subgroup distributions without labeled target data.
problem Adapting to domain shifts when latent subgroup distributions differ.
method Uses concept and proxy variables from source domain, and unlabeled target data.
result Optimal target predictor can be identified and estimated.
New method MRI improves machine learning models' ability to generalize to unseen data.
problem Machine learning models often fail to generalize well to out-of-distribution data.
method Introduces a new notion of invariance (MRI) and a practical version (MRI-v1) to improve model generalization.
result MRI-v1 guarantees invariant predictors and outperforms IRM-v1 in various settings.
This work provides minimax bounds for structured prediction models.
problem Limited understanding of necessary sample complexity for structured prediction.
method Analysis of factor-graph inference models for structured prediction.
result Characterization of necessary sample complexity for any algorithm.
New bounds explain deterministic non-smooth deep nets without large Lipschitz constants.
problem Challenges in explaining generalization of deterministic non-smooth deep nets.
method De-randomized PAC-Bayes margin bounds for deterministic non-convex and non-smooth predictors.
result New bounds avoid large Lipschitz constants, providing generalization guarantees.
This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems. We suggest that for each individual label, it cannot only be implicitly connected with other labels via the low-rank constraint over label predictors, but also its performa…
We propose a novel nonparametric online predictor for discrete labels conditioned on multivariate continuous features. The predictor is based on a feature space discretization induced by a full-fledged k-d tree with randomly picked directions and a recursive Bayesian distribution, which allows to automatically learn th…
New method reduces generalization error for interpolating predictors.
problem Understanding and reducing generalization error for predictors that interpolate training data.
method Derandomization and conditional distribution to control generalization error.
result Surrogates constructed by conditioning and denoising have uniformly small generalization error.
This paper proposes a new algorithmic framework, predictor-verifier training, to train neural networks that are verifiable, i.e., networks that provably satisfy some desired input-output properties. The key idea is to simultaneously train two networks: a predictor network that performs the task at hand,e.g., predicting…
Adaptive region-based active learning seeks labels for complex data.
problem Efficiently label complex datasets with minimal human effort.
method Adaptive region partitioning and active learning for distinct predictors.
result Substantial empirical benefits over existing methods.
ASAC uses actor-critic models to optimize observation selection in medical settings.
problem Optimizing observation selection in costly sequential observation scenarios.
method ASAC framework with selector and predictor networks, using actor-critic models for training.
result ASAC significantly outperforms state-of-the-art methods in real-world medical datasets.
New method tackles OOD robustness with a single additional variable.
problem Out-of-distribution generalization with unobserved confounders.
method Identifiability assumptions using a single additional variable.
result Superior empirical performance on benchmark tasks.
RFMs transition from linear to nonlinear under specific input-label correlation.
problem Understanding the transition from linear to nonlinear behavior in RFMs.
method Analyzing RFMs under spiked covariance designs, characterizing the interaction between anisotropy and input-label correlation.
result The RFM generalization error is governed by the strength of input-label correlation, leading to a clear nonlinear advantage above a specific boundary.
Binary classification improves with a small fraction of corrupted labels.
problem Binary classification with corrupted labels.
method Established corruption as a form of regularization and computed upper bounds on estimation error.
result Corruption is beneficial only up to a small fraction of the total sample, scaling with the square root of the sample size.
Study on approximability and generalization in machine learning.
problem Understanding how approximation affects learning and generalization in machine learning.
method Introducing a notion of sensitivity to analyze the impact of approximation operators on predictors and proving upper bounds on generalization.
result Proven that approximable target concepts are learnable with fewer labelled samples and sufficient unlabelled data.
Proposes MGPLL for PL learning with non-random noise.
problem Partial label learning with non-random label noise.
method Bi-directional mapping framework, conditional noise label generation, multi-class predictor, adversarial learning.
result Demonstrates state-of-the-art performance in partial label learning.
We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, …
Unified approach for learning with weak labels across various tasks.
problem Learning with noisy or incomplete labels in diverse machine learning settings.
method Implicit posterior models for joint label inference.
result Unified training objective for various machine learning tasks.
A novel framework for regression with multiple experts, addressing challenges in infinite and continuous label spaces.
problem Challenges in regression with multiple experts due to the infinite and continuous nature of the label space.
method Introduces a novel framework for regression with deferral, analyzing both single-stage and two-stage scenarios with new surrogate loss functions.
result Proves H-consistency bounds for both single-stage and two-stage methods, providing stronger guarantees than Bayes consistency. Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations. This paper investigates anonymization methods based on representation learning and deep neural networks, and motivated by novel information theoretica…
Study shows semi-supervised learning can be more robust with fewer labeled examples.
problem Learning robust predictors in semi-supervised PAC model with minimal labeled data.
method Characterizes the minimal labeled and unlabeled data required for robust learning.
result Proves nearly matching upper and lower bounds on labeled sample complexity.
Proposes a new method to unlearn from specific data points in conformal predictors.
problem Challenges of existing unlearning methods in conformal predictors.
method Formalizes conformal unlearning, introduces practical metrics, and presents an optimization algorithm.
result Demonstrates effective removal of targeted information while preserving utility.
Protein-protein interaction (PPI) prediction is an important problem in machine learning and computational biology. However, there is no data set for training or evaluation purposes, where all the instances are accurately labeled. Instead, what is available are instances of positive class (with possibly noisy labels) a…
New method learns optimal prediction strategies in adversarial games.
problem Learning optimal prediction procedures in uncertain data environments.
method Adversarial Monte Carlo approach with neural network architecture.
result Optimal strategy is equivariant and invariant to various transformations.
Study semi-supervised learning with noisy proxy covariates, deriving bounds and showing gains.
problem Learning from noisy proxy covariates with scarce labels.
method Two-stage estimator learning kernel eigenfeatures from all proxy covariates and fitting a ridge predictor on labeled data.
result Finite sample bounds show fast labeled sample rates and consistent gains over supervised and semi-supervised baselines.
Operations is a key challenge in the domain of machine learning pipeline deployments involving monitoring and management of real-time prediction quality. Typically, metrics like accuracy, RMSE etc., are used to track the performance of models in deployment. However, these metrics cannot be calculated in production due …