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Found 17 papers, 8 papers with code
Robust Hybrid Learning With Expert Augmentation
Hybrid modelling reduces the misspecification of expert models by combining them with machine learning (ML) components learned from data.
CPR: Classifier-Projection Regularization for Continual Learning
Inspired by both recent results on neural networks with wide local minima and information theory, CPR adds an additional regularization term that maximizes the entropy of a classifier's output probability.
Beyond Adult and COMPAS: Fairness in Multi-Class Prediction
We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks.
Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks
Then, the identified tumors are passed to a second CNN for recurrence risk prediction.
Correspondence Analysis Using Neural Networks
Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies.
Rashomon Capacity: A Metric for Predictive Multiplicity in Classification
Predictive multiplicity occurs when classification models with statistically indistinguishable performances assign conflicting predictions to individual samples.
Arbitrary Decisions are a Hidden Cost of Differentially Private Training
We demonstrate that such randomization incurs predictive multiplicity: for a given input example, the output predicted by equally-private models depends on the randomness used in training.
Arbitrariness Lies Beyond the Fairness-Accuracy Frontier
Machine learning tasks may admit multiple competing models that achieve similar performance yet produce conflicting outputs for individual samples -- a phenomenon known as predictive multiplicity.
Generalizing Bottleneck Problems
Given a pair of random variables $(X, Y)\sim P_{XY}$ and two convex functions $f_1$ and $f_2$, we introduce two bottleneck functionals as the lower and upper boundaries of the two-dimensional convex set that consists of the pairs $\left(I_{f_1}(W; X), I_{f_2}(W; Y)\right)$, where $I_f$ denotes $f$-information and $W$ varies over the set of all discrete random variables satisfying the Markov condition $W \to X \to Y$.
Generalizing Correspondence Analysis for Applications in Machine Learning
In this paper, we provide a novel interpretation of CA in terms of an information-theoretic quantity called the principal inertia components.
Correspondence Analysis of Government Expenditure Patterns
We analyze expenditure patterns of discretionary funds by Brazilian congress members.
Obfuscation via Information Density Estimation
The core of this mechanism relies on a data-driven estimate of the trimmed information density for which we propose a novel estimator, named the trimmed information density estimator (TIDE).
To Split or Not to Split: The Impact of Disparate Treatment in Classification
To evaluate the effect of disparate treatment, we compare the performance of split classifiers (i. e., classifiers trained and deployed separately on each group) with group-blind classifiers (i. e., classifiers which do not use a sensitive attribute).
Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models
The rapid growth of machine learning has spurred legislative initiatives such as ``the Right to be Forgotten,'' allowing users to request data removal.
Machine Unlearning for Image-to-Image Generative Models
This paper serves as a bridge, addressing the gap by providing a unifying framework of machine unlearning for image-to-image generative models.
Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation
Predictive multiplicity refers to the phenomenon in which classification tasks may admit multiple competing models that achieve almost-equally-optimal performance, yet generate conflicting outputs for individual samples.
OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning
We illustrate that a simplified prompt-based method can achieve results comparable to previous state-of-the-art (SOTA) methods equipped with a prompt pool, using much less learnable parameters and lower inference cost.
