Noise increases the Rashomon ratio, leading simpler models to perform similarly to complex ones.
problem Why simpler models perform similarly to complex models on noisy datasets.
method Analyzed the data generation process and model training choices, introduced pattern diversity.
result Noisier datasets lead to larger Rashomon ratios, explaining simpler models' performance.
New algorithms approximate Rashomon set for sparse models, aiding expert interaction.
problem Lack of interaction between models and domain experts in classical machine learning.
method Approximate Rashomon set of sparse, generalized additive models using ellipsoids.
result Efficiently approximated Rashomon set facilitates model selection and exploration.
Framework uses dropout to efficiently explore Rashomon set for multiplicity estimation.
problem Efficiently measuring and mitigating conflicting model outputs in classification tasks.
method Dropout-based exploration of Rashomon set for multiplicity estimation.
result Framework outperforms baselines in multiplicity metric estimation with significant runtime speedup.
New method uses Rashomon sets to improve Bayesian inference in factorial designs.
problem Combustion of model uncertainty in factorial designs leads to multimodal posterior and convergence issues.
method Rashomon-seeded annealing, integrating high-performing models as warm start for AIS.
result Restores full posterior inference without exhaustive enumeration of model space.
AXE evaluates explanations to avoid misleading Rashomon set model selection.
problem Evaluating explanations for Rashomon set models to avoid false selection.
method Proposed AXE method to evaluate explanation quality.
result AXE detects adversarial fairwashing with 100% success rate.
The paper analyzes the Rashomon ratio for infinite classifier families and shows its importance for choosing good classifiers.
problem Analyzing the Rashomon ratio for infinite classifier families.
method Quantifying the Rashomon ratio in two examples and providing guarantees for estimating it.
result A large Rashomon ratio guarantees choosing a classifier with good empirical accuracy will not significantly increase empirical loss.
Paper explores Rashomon set models for more trustworthy medical conclusions.
problem Lack of comprehensive analysis of models in Rashomon set leads to misleading conclusions.
method Introduces Rashomon_DETECT algorithm and Profile Disparity Index (PDI).
result Combining differently behaving models in Rashomon set provides more trustworthy conclusions.
It is almost always easier to find an accurate-but-complex model than an accurate-yet-simple model. Finding optimal, sparse, accurate models of various forms (linear models with integer coefficients, decision sets, rule lists, decision trees) is generally NP-hard. We often do not know whether the search for a simpler m…
REALITrees uses a Rashomon ensemble approach for active learning in sparse decision trees.
problem Active learning reduces labeling costs by selecting informative samples, but current methods often sacrifice model diversity and direct characterization of the hypothesis space.
method REALITrees constructs a committee of all near-optimal sparse decision tree models using a Rashomon Set and a Gibbs posterior to weight them by empirical risk.
result REALITrees outperforms randomized ensembles, especially in noisy environments, by leveraging expanded model multiplicity.
Introduces Rashomon Capacity to measure predictive multiplicity in probabilistic classifiers.
problem Predictive multiplicity in classification models leading to unjustified decisions.
method Introduces Rashomon Capacity, a metric for probabilistic classifiers, and provides a rigorous derivation.
result Rashomon Capacity captures nuanced score variations and provides strategies for disclosing conflicting models.
UNREAL selectively ensembles distinct models to improve active learning performance.
problem Difficulty in distinguishing genuine uncertainty from noise in limited labeled data.
method Selective ensembling of distinct models from the Rashomon set.
result UNREAL achieves faster convergence and up to 20% predictive accuracy improvement.
Paper presents a Rashomon Quartet to show different model explanations.
problem Models with similar performance can explain data differently.
method Synthetic dataset with four equally effective models.
result Models with similar performance can explain data differently.
Study shows more data improves model explanations, aiding reliable knowledge extraction.
problem Challenges in deriving reliable knowledge from machine learning models due to the Rashōmon effect.
method Examined the influence of sample size on explanations from models in a Rashōmon set using SHAP.
result Explanations from <128 samples are highly variable, but agreement improves with more data.
The Rashomon effect shows many models can perform similarly, explored in this paper.
problem Why do many models perform similarly in machine learning?
method Categorized causes into statistical, structural, and procedural sources.
result Structural multiplicity persists and cannot be resolved without additional assumptions.
Commentary on Rashomon Effect complicating model selection.
problem Many models equally predict data; hard to draw conclusions.
method Connections to recent ML literature exploring implications.
result Grasping Rashomon Effect can foster collaboration.
Leo Breiman's Rashomon Effect and Occam Dilemma are re-evaluated in the context of modern machine learning.
problem The tradeoff between model complexity and accuracy in machine learning.
method Modern perspective on Breiman's arguments using current computational capabilities.
result Algorithmic models can be accurate without being complex, nullifying the Occam Dilemma.
Framework for assessing fairness across similar predictive models.
problem Fairness in predictive models across different groups.
method Develops a framework for characterizing fairness over the set of good models under selective labels.
result Framework can replace or audit models for better fairness properties.
Bayesian framework for model uncertainty identifies complex heterogeneity without strong assumptions.
problem Identifying complex heterogeneity in factorial data with varying covariates.
method Rashomon Partition Sets (RPS) using l0 prior for robust model uncertainty.
result RPS provides a robust set of models capturing complex heterogeneity without strong assumptions.
This paper explores how balancing and filtering techniques affect predictive multiplicity in machine learning models.
problem Predictive multiplicity due to Rashomon effect in high-stakes environments.
method Investigates the impact of balancing and filtering techniques on predictive multiplicity using 21 real-world datasets.
result Data-centric AI strategies can mitigate predictive multiplicity, but preprocessing methods may introduce it.
New framework quantifies variable importance across all good models and is stable across data distribution.
problem Conflicting variable importance conclusions from different models trained on the same data.
method Proposes a new variable importance framework that considers all good models and is stable across data distribution.
result Framework accurately estimates true variable importance and recovers rankings for complex setups.
A GPU framework speeds up BnB for discrete optimization problems.
problem Optimizing large-scale discrete problems with GPU limitations.
method Parallel BnB nodes in GPU batches, using padding and custom kernels.
result One to two orders of magnitude speedup and zero optimality gap.
Breiman's data analysis dichotomy is outdated, offering a third approach: mechanistic models.
problem Data analysis dichotomy between data modelers and algorithmic modelers.
method Interpolating between simple interpretable models and flexible function approximations using mechanistic models.
result Flexible, interpretable, and scientifically-informed hybrids can provide accurate and robust predictions.
Survey on principles and challenges of interpretable machine learning.
problem Improving machine learning models' interpretability for high-stakes decisions.
method Identification and analysis of 10 technical challenges in interpretable machine learning.
result Identification of 10 technical challenges in interpretable machine learning.
Random projections improve classifier generalization without needing to choose the best threshold.
problem Improving classifier generalization without choosing the best threshold.
method Thresholding a random one-dimensional feature after random projection of data.
result Generalization gap is significantly smaller than linear classifiers.
Interactive EMA combines multiple explainability methods to improve model understanding.
problem Isolated explanations of machine learning models lead to misunderstandings and wrong reasoning.
method Interactive EMA (IEMA) combines multiple explainability methods sequentially.
result Interactive EMA increases the performance and confidence of human decision making.
This work introduces robust counterfactuals for neural networks that remain valid after minor model changes.
problem Generating counterfactual explanations that are robust to slight model changes.
method Introduces a measure called Stability to quantify robustness of counterfactuals to potential model changes, and proposes practical relaxations.
result Counterfactuals with high Stability remain valid after potential model changes with high probability.
Study shows Julia sets and gasket limit sets are quasiconformally different.
problem Quasiconformal non-equivalence of Julia sets and gasket limit sets.
method Proved quasiconformal non-equivalence of Julia sets and gasket limit sets.
result Julia sets and gasket limit sets are quasiconformally different.
Study shows non-symmetric convex sets have full boundary limits.
problem Understanding boundaries of non-symmetric convex sets.
method Proved using proximal limit set analysis.
result Proximal limit set equals full projective boundary for non-symmetric irreducible divisible convex sets.
The paper analyzes set-to-set matching with neural networks, focusing on theoretical generalization.
problem Theoretical analysis of set-to-set matching with neural networks.
method Generalization error analysis of set-to-set matching with neural networks.
result Theoretical insights into the behavior of set-to-set matching models.
Generative model learns to autoencode and generate sets of images.
problem Learning to represent and generate sets of images with unknown number of sets.
method Set Distribution Networks (SDNs) learn set encoder, discriminator, generator, and prior.
result SDNs can reconstruct and generate sets of images with preserved attributes.
Study on cold and freezing sets in digital images.
problem Properties of cold sets in digital images.
method Analysis of properties and relationships between cold and freezing sets.
result Examined relationships between cold and freezing sets.
Paper solves whether zero sets are mapping degree sets.
problem Whether finite sets containing zero are mapping degree sets.
method Examined oriented closed connected manifolds of the same dimension.
result Affirmative answer given for both integer and rational settings.
Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem. The difficulties are to extract features to match a correct pair of different sets and also preserve two types of exchangeability required for set-to-set matching: the pair of …
New set-valued star-shaped risk measures introduced for better risk assessment.
problem Improving risk assessment in financial contexts.
method Developed new set-valued star-shaped risk measures and proved their representation theorems.
result Set-valued star-shaped risk measures can be represented as unions of set-valued convex risk measures.
We introduce the concept of hereditarily non uniformly perfect sets, compact sets for which no compact subset is uniformly perfect, and compare them with the following: Hausdorff dimension zero sets, logarithmic capacity zero sets, Lebesgue 2-dimensional measure zero sets, and porous sets. In particular, we give an exa…
Study dynamics and topology of flows near non-saddle sets or W-sets.
problem Understanding the dynamics and topology of flows near specific invariant sets.
method Cohomological relations and global properties analysis.
result Dynamical classification of surfaces and robustness of non-saddle-sets.
The study explores mapping degree sets and their properties for manifolds.
problem Understanding the structure and properties of mapping degree sets for manifolds.
method Analyzes the properties of mapping degree sets and their relationships with self-mapping degree sets.
result Not every multiplicative set containing 0,1 is a self-mapping degree set.
Current approaches for predicting sets from feature vectors ignore the unordered nature of sets and suffer from discontinuity issues as a result. We propose a general model for predicting sets that properly respects the structure of sets and avoids this problem. With a single feature vector as input, we show that our m…
This paper studies the geometry of minimum-volume confidence sets for multinomial parameters.
problem Determining if minimum-volume confidence sets for multinomial outcomes are disjoint.
method Enumerating and covering the continuous regions of the exact p-value function to study the geometry of minimum-volume confidence sets.
result The geometry of minimum-volume confidence sets for multinomial parameters is studied, providing insights into their structure and properties.
Consider a general machine learning setting where the output is a set of labels or sequences. This output set is unordered and its size varies with the input. Whereas multi-label classification methods seem a natural first resort, they are not readily applicable to set-valued outputs because of the growth rate of the o…
Deep Sets approximates functions on sets with high-dimensional latent space.
problem Modeling functions of sets (permutation-invariant functions).
method Deep Sets, a method known to be a universal approximator for continuous set functions.
result Deep Sets' universal approximation property is only guaranteed with a sufficiently high-dimensional latent space.
Study online learning with set-valued feedback, showing differences between deterministic and randomized approaches.
problem Online learning with set-valued feedback, where labels are sets rather than single labels.
method Introduced new combinatorial dimensions (Set Littlestone and Measure Shattering) to characterize learnability.
result Characterized deterministic and randomized online learnability, and established bounds for various learning settings.
A stability-based method selects the most desirable conformal prediction set.
problem Selecting the most desirable conformal prediction set from multiple valid sets invalidates coverage guarantees.
method A stability-based approach that ensures coverage for the selected prediction set.
result The stability-based approach maintains coverage guarantees for the selected prediction set.
The paper links set cuspidality to function regularity and flatness.
problem Linking set cuspidality to function regularity and flatness.
method Analyzes arc-smooth functions and their properties on various sets.
result Establishes a precise link between set cuspidality and function regularity.
This work establishes properties on diffeological structures for set-valued maps and measures.
problem Establish rigorous properties on diffeological structures for set-valued maps and measures.
method Using diffeologies, the authors link various structures including set-valued maps, relations, gradients, measures, and shape analysis.
result Established rigorous properties on sample diffeologies.
Causal Set Theory's Hauptvermutung is resolved in two ways, one of which is true.
problem Formulating and resolving the Hauptvermutung in Causal Set Theory.
method Two mathematically well-defined formulations of the Hauptvermutung, one of which is true.
result The Hauptvermutung is true when finite sets are replaced by countable sets.
This letter introduces an abstract learning problem called the "set embedding": The objective is to map sets into probability distributions so as to lose less information. We relate set union and intersection operations with corresponding interpolations of probability distributions. We also demonstrate a preliminary so…
Find limiting sets for digital cones and suspensions.
problem Digital topology cone and suspension constructions.
method Identify (m, n)-limiting sets, especially (0, 0)-freezing sets.
result Discover (0, 0)-limiting sets for digital cones and suspensions.