The work provides the first formal definitions of Rashomon sets for federated learning and introduces a multiplicity-aware training pipeline evaluated on standard benchmarks.
The mythos of model interpretability
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
abstract
Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and sometimes non-overlapping motivations for interpretability, and offer myriad notions of what attributes render models interpretable. Despite this ambiguity, many papers proclaim interpretability axiomatically, absent further explanation. In this paper, we seek to refine the discourse on interpretability. First, we examine the motivations underlying interest in interpretability, finding them to be diverse and occasionally discordant. Then, we address model properties and techniques thought to confer interpretability, identifying transparency to humans and post-hoc explanations as competing notions. Throughout, we discuss the feasibility and desirability of different notions, and question the oft-made assertions that linear models are interpretable and that deep neural networks are not.
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A piecewise biconvex optimization framework unifies sparse dictionary learning variants, explains their pathologies via spurious optima, and enables feature anchoring to restore identifiability.
Agentic-imodels evolves scikit-learn regressors via an autoresearch loop to jointly boost predictive performance and LLM-simulatability, improving downstream agentic data science tasks by up to 73% on the BLADE benchmark.
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
Data-driven MoCap-to-radar models often fail to learn underlying physics despite low reconstruction error, with temporal attention proving critical for transformers to achieve physical consistency.
A cycle-consistent GAN generates counterfactual medical images to attribute classification decisions more comprehensively than standard saliency methods.
Current XAI methods for DNNs and LLMs rest on paradoxes and false assumptions that demand a paradigm shift to verification protocols, scientific foundations, context-aware design, and faithful model analysis rather than post-hoc explanations.
citing papers explorer
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Rashomon Sets and Model Multiplicity in Federated Learning
The work provides the first formal definitions of Rashomon sets for federated learning and introduces a multiplicity-aware training pipeline evaluated on standard benchmarks.
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A Unified Theory of Sparse Dictionary Learning in Mechanistic Interpretability: Piecewise Biconvexity and Spurious Minima
A piecewise biconvex optimization framework unifies sparse dictionary learning variants, explains their pathologies via spurious optima, and enables feature anchoring to restore identifiability.
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Agentic-imodels: Evolving agentic interpretability tools via autoresearch
Agentic-imodels evolves scikit-learn regressors via an autoresearch loop to jointly boost predictive performance and LLM-simulatability, improving downstream agentic data science tasks by up to 73% on the BLADE benchmark.
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Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
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What Physics do Data-Driven MoCap-to-Radar Models Learn?
Data-driven MoCap-to-radar models often fail to learn underlying physics despite low reconstruction error, with temporal attention proving critical for transformers to achieve physical consistency.
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Seeing What Shouldn't Be There: Counterfactual GANs for Medical Image Attribution
A cycle-consistent GAN generates counterfactual medical images to attribute classification decisions more comprehensively than standard saliency methods.
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Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
Current XAI methods for DNNs and LLMs rest on paradoxes and false assumptions that demand a paradigm shift to verification protocols, scientific foundations, context-aware design, and faithful model analysis rather than post-hoc explanations.