Rethinking Distance Metrics for Counterfactual Explainability
Fuente:
arXiv
Guardado en:
| Autores principales: | Williams, Joshua Nathaniel, Katakkar, Anurag, Heidari, Hoda, Kolter, J. Zico |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizers
por: Williams, Joshua Nathaniel, et al.
Publicado: (2024)
por: Williams, Joshua Nathaniel, et al.
Publicado: (2024)
Prompt Recovery for Image Generation Models: A Comparative Study of Discrete Optimizers
por: Williams, Joshua Nathaniel, et al.
Publicado: (2024)
por: Williams, Joshua Nathaniel, et al.
Publicado: (2024)
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
por: Akinwande, Victor, et al.
Publicado: (2024)
por: Akinwande, Victor, et al.
Publicado: (2024)
Why is SAM Robust to Label Noise?
por: Baek, Christina, et al.
Publicado: (2024)
por: Baek, Christina, et al.
Publicado: (2024)
Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning
por: Huang, Benhao, et al.
Publicado: (2026)
por: Huang, Benhao, et al.
Publicado: (2026)
Rethinking LLM Memorization through the Lens of Adversarial Compression
por: Schwarzschild, Avi, et al.
Publicado: (2024)
por: Schwarzschild, Avi, et al.
Publicado: (2024)
Mimetic Initialization of MLPs
por: Trockman, Asher, et al.
Publicado: (2026)
por: Trockman, Asher, et al.
Publicado: (2026)
From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence
por: Finzi, Marc, et al.
Publicado: (2026)
por: Finzi, Marc, et al.
Publicado: (2026)
Predicting the Performance of Black-box LLMs through Follow-up Queries
por: Sam, Dylan, et al.
Publicado: (2025)
por: Sam, Dylan, et al.
Publicado: (2025)
Diffusing Differentiable Representations
por: Savani, Yash, et al.
Publicado: (2024)
por: Savani, Yash, et al.
Publicado: (2024)
One-Step Diffusion Distillation via Deep Equilibrium Models
por: Geng, Zhengyang, et al.
Publicado: (2023)
por: Geng, Zhengyang, et al.
Publicado: (2023)
Measuring Five-Nines Reliability: Sample-Efficient LLM Evaluation in Saturated Benchmarks
por: Kim, Eungyeup, et al.
Publicado: (2026)
por: Kim, Eungyeup, et al.
Publicado: (2026)
Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation
por: Roderick, Melrose, et al.
Publicado: (2023)
por: Roderick, Melrose, et al.
Publicado: (2023)
An Axiomatic Approach to Model-Agnostic Concept Explanations
por: Feng, Zhili, et al.
Publicado: (2024)
por: Feng, Zhili, et al.
Publicado: (2024)
Context-Parametric Inversion: Why Instruction Finetuning Can Worsen Context Reliance
por: Goyal, Sachin, et al.
Publicado: (2024)
por: Goyal, Sachin, et al.
Publicado: (2024)
Massive Activations in Large Language Models
por: Sun, Mingjie, et al.
Publicado: (2024)
por: Sun, Mingjie, et al.
Publicado: (2024)
The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints
por: Wang, Po-Wei, et al.
Publicado: (2017)
por: Wang, Po-Wei, et al.
Publicado: (2017)
Understanding Hallucinations in Diffusion Models through Mode Interpolation
por: Aithal, Sumukh K, et al.
Publicado: (2024)
por: Aithal, Sumukh K, et al.
Publicado: (2024)
Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws
por: Jiang, Yiding, et al.
Publicado: (2024)
por: Jiang, Yiding, et al.
Publicado: (2024)
Evaluating Language Model Reasoning about Confidential Information
por: Sam, Dylan, et al.
Publicado: (2025)
por: Sam, Dylan, et al.
Publicado: (2025)
When Should We Introduce Safety Interventions During Pretraining?
por: Sam, Dylan, et al.
Publicado: (2026)
por: Sam, Dylan, et al.
Publicado: (2026)
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression
por: Zhai, Runtian, et al.
Publicado: (2023)
por: Zhai, Runtian, et al.
Publicado: (2023)
A Simple and Effective Pruning Approach for Large Language Models
por: Sun, Mingjie, et al.
Publicado: (2023)
por: Sun, Mingjie, et al.
Publicado: (2023)
Looking beyond the next token
por: Thankaraj, Abitha, et al.
Publicado: (2025)
por: Thankaraj, Abitha, et al.
Publicado: (2025)
Scaling Laws for Data Filtering -- Data Curation cannot be Compute Agnostic
por: Goyal, Sachin, et al.
Publicado: (2024)
por: Goyal, Sachin, et al.
Publicado: (2024)
Bayesian Neural Networks with Domain Knowledge Priors
por: Sam, Dylan, et al.
Publicado: (2024)
por: Sam, Dylan, et al.
Publicado: (2024)
Mimetic Initialization Helps State Space Models Learn to Recall
por: Trockman, Asher, et al.
Publicado: (2024)
por: Trockman, Asher, et al.
Publicado: (2024)
Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line
por: Kim, Eungyeup, et al.
Publicado: (2023)
por: Kim, Eungyeup, et al.
Publicado: (2023)
Superhuman AI for Stratego Using Self-Play Reinforcement Learning and Test-Time Search
por: Sokota, Samuel, et al.
Publicado: (2025)
por: Sokota, Samuel, et al.
Publicado: (2025)
Finetuning CLIP to Reason about Pairwise Differences
por: Sam, Dylan, et al.
Publicado: (2024)
por: Sam, Dylan, et al.
Publicado: (2024)
Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning
por: Xu, Yixuan Even, et al.
Publicado: (2025)
por: Xu, Yixuan Even, et al.
Publicado: (2025)
Forcing Diffuse Distributions out of Language Models
por: Zhang, Yiming, et al.
Publicado: (2024)
por: Zhang, Yiming, et al.
Publicado: (2024)
Weight Ensembling Improves Reasoning in Language Models
por: Dang, Xingyu, et al.
Publicado: (2025)
por: Dang, Xingyu, et al.
Publicado: (2025)
Predicting the Performance of Foundation Models via Agreement-on-the-Line
por: Saxena, Rahul, et al.
Publicado: (2024)
por: Saxena, Rahul, et al.
Publicado: (2024)
TOFU: A Task of Fictitious Unlearning for LLMs
por: Maini, Pratyush, et al.
Publicado: (2024)
por: Maini, Pratyush, et al.
Publicado: (2024)
Consistency Models Made Easy
por: Geng, Zhengyang, et al.
Publicado: (2024)
por: Geng, Zhengyang, et al.
Publicado: (2024)
Mean Flows for One-step Generative Modeling
por: Geng, Zhengyang, et al.
Publicado: (2025)
por: Geng, Zhengyang, et al.
Publicado: (2025)
From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers
por: Gurumurthy, Swaminathan, et al.
Publicado: (2024)
por: Gurumurthy, Swaminathan, et al.
Publicado: (2024)
Provably Bounding Neural Network Preimages
por: Kotha, Suhas, et al.
Publicado: (2023)
por: Kotha, Suhas, et al.
Publicado: (2023)
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation
por: He, Yutong, et al.
Publicado: (2024)
por: He, Yutong, et al.
Publicado: (2024)
Ejemplares similares
-
FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizers
por: Williams, Joshua Nathaniel, et al.
Publicado: (2024) -
Prompt Recovery for Image Generation Models: A Comparative Study of Discrete Optimizers
por: Williams, Joshua Nathaniel, et al.
Publicado: (2024) -
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
por: Akinwande, Victor, et al.
Publicado: (2024) -
Why is SAM Robust to Label Noise?
por: Baek, Christina, et al.
Publicado: (2024) -
Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning
por: Huang, Benhao, et al.
Publicado: (2026)