:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Sani, Numair, Malinsky, Daniel, Shpitser, Ilya
Format:	Preprint
Published:	2020
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2006.02482
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Multiply Robust Causal Mediation Analysis with Continuous Treatments
by: Xu, Yizhen, et al.
Published: (2021)

Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance Prediction
by: Kostovska, Ana, et al.
Published: (2025)

Black Box Causal Inference: Effect Estimation via Meta Prediction
by: Bynum, Lucius E. J., et al.
Published: (2025)

In-Context Explainers: Harnessing LLMs for Explaining Black Box Models
by: Kroeger, Nicholas, et al.
Published: (2023)

Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box
by: Moreira, Catarina, et al.
Published: (2022)

Explaining Time Series Classification Predictions via Causal Attributions
by: Alcaraz, Juan Miguel Lopez, et al.
Published: (2024)

Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies
by: Kekić, Armin, et al.
Published: (2025)

Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding
by: Bethell, Daniel, et al.
Published: (2024)

Unpacking the Black Box: Regulating Algorithmic Decisions
by: Blattner, Laura, et al.
Published: (2021)

Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms
by: Dagdanov, Resul, et al.
Published: (2022)

Towards Learning and Explaining Indirect Causal Effects in Neural Networks
by: Reddy, Abbavaram Gowtham, et al.
Published: (2023)

LiBOG: Lifelong Learning for Black-Box Optimizer Generation
by: Pei, Jiyuan, et al.
Published: (2025)

Comparing Two Proxy Methods for Causal Identification
by: Guo, Helen, et al.
Published: (2025)

Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market
by: Peter, Philipp Kai, et al.
Published: (2024)

Sharpness-Aware Black-Box Optimization
by: Ye, Feiyang, et al.
Published: (2024)

Explaining Predictions by Characteristic Rules
by: Alkhatib, Amr, et al.
Published: (2024)

SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning
by: Huang, Tairan, et al.
Published: (2025)

Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible?
by: Gerogiannis, Argyrios, et al.
Published: (2024)

Learning Surrogates for Offline Black-Box Optimization via Gradient Matching
by: Hoang, Minh, et al.
Published: (2025)

Can Language Models Explain Their Own Classification Behavior?
by: Sherburn, Dane, et al.
Published: (2024)

No Black Box Anymore: Demystifying Clinical Predictive Modeling with Temporal-Feature Cross Attention Mechanism
by: Li, Yubo, et al.
Published: (2025)

In-Context Black-Box Optimization with Unreliable Feedback
by: Blumer, Nicolas Samuel, et al.
Published: (2026)

Optimistic Gradient Learning with Hessian Corrections for High-Dimensional Black-Box Optimization
by: Kfir, Yedidya, et al.
Published: (2025)

DiConStruct: Causal Concept-based Explanations through Black-Box Distillation
by: Moreira, Ricardo, et al.
Published: (2024)

Analysis of Financial Risk Behavior Prediction Using Deep Learning and Big Data Algorithms
by: Yang, Haowei, et al.
Published: (2024)

Explaining Machine Learning Predictive Models through Conditional Expectation Methods
by: Ruiz-España, Silvia, et al.
Published: (2026)

Deep Causal Behavioral Policy Learning: Applications to Healthcare
by: Knecht, Jonas, et al.
Published: (2025)

Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models
by: Ciaperoni, Martino, et al.
Published: (2026)

Learning by Self-Explaining
by: Stammer, Wolfgang, et al.
Published: (2023)

BRAID: Input-Driven Nonlinear Dynamical Modeling of Neural-Behavioral Data
by: Vahidi, Parsa, et al.
Published: (2025)

Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies
by: Sharifi, Iman, et al.
Published: (2023)

Turning Black Box into White Box: Dataset Distillation Leaks
by: Chen, Huajie, et al.
Published: (2026)

Shortcut Learning in Binary Classifier Black Boxes: Applications to Voice Anti-Spoofing and Biometrics
by: Sahidullah, Md, et al.
Published: (2026)

Optimizing the Unknown: Black Box Bayesian Optimization with Energy-Based Model and Reinforcement Learning
by: Miao, Ruiyao, et al.
Published: (2025)

Robust Guided Diffusion for Offline Black-Box Optimization
by: Chen, Can Sam, et al.
Published: (2024)

Semantic Prototypes: Enhancing Transparency Without Black Boxes
by: Menis-Mastromichalakis, Orfeas, et al.
Published: (2024)

OptunaHub: A Platform for Black-Box Optimization
by: Ozaki, Yoshihiko, et al.
Published: (2025)

Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs
by: Li, Changhao, et al.
Published: (2024)

Black-Box Optimization From Small Offline Datasets via Meta Learning with Synthetic Tasks
by: Fadhel, Azza, et al.
Published: (2026)

Trust Regions for Explanations via Black-Box Probabilistic Certification
by: Dhurandhar, Amit, et al.
Published: (2024)