Copertina

Causal Deep Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Berrevoets, Jeroen, Kacprzyk, Krzysztof, Qian, Zhaozhi, van der Schaar, Mihaela
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
di: Kacprzyk, Krzysztof, et al.
Pubblicazione: (2024)

DAGnosis: Localized Identification of Data Inconsistencies using Structures
di: Huynh, Nicolas, et al.
Pubblicazione: (2024)

Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments
di: Rauba, Paulius, et al.
Pubblicazione: (2024)

No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs
di: Kacprzyk, Krzysztof, et al.
Pubblicazione: (2025)

Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees
di: Huynh, Nicolas, et al.
Pubblicazione: (2026)

Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations
di: Kacprzyk, Krzysztof, et al.
Pubblicazione: (2024)

Preference Learning for AI Alignment: a Causal Perspective
di: Kobalczyk, Katarzyna, et al.
Pubblicazione: (2025)

Not All Explanations for Deep Learning Phenomena Are Equally Valuable
di: Jeffares, Alan, et al.
Pubblicazione: (2025)

Active Timepoint Selection for Learning Measure-Valued Trajectories
di: Huynh, Nicolas, et al.
Pubblicazione: (2026)

Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning
di: Sun, Hao, et al.
Pubblicazione: (2024)

Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond
di: Jeffares, Alan, et al.
Pubblicazione: (2024)

Deep Generative Symbolic Regression
di: Holt, Samuel, et al.
Pubblicazione: (2023)

Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transport
di: Amad, Harry, et al.
Pubblicazione: (2026)

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
di: Sun, Hao, et al.
Pubblicazione: (2025)

Adaptive Experiment Design with Synthetic Controls
di: Hüyük, Alihan, et al.
Pubblicazione: (2024)

Discovery of Hidden Miscalibration Regimes
di: Kobalczyk, Katarzyna, et al.
Pubblicazione: (2026)

Beyond the ATE: Interpretable Modelling of Treatment Effects over Dose and Time
di: Piskorz, Julianna, et al.
Pubblicazione: (2025)

Language Bottleneck Models for Qualitative Knowledge State Modeling
di: Berthon, Antonin, et al.
Pubblicazione: (2025)

Simulating Viva Voce Examinations to Evaluate Clinical Reasoning in Large Language Models
di: Chiu, Christopher, et al.
Pubblicazione: (2025)

Eliciting Numerical Predictive Distributions of LLMs Without Autoregression
di: Piskorz, Julianna, et al.
Pubblicazione: (2026)

Technical Report: Facilitating the Adoption of Causal Inference Methods Through LLM-Empowered Co-Pilot
di: Berrevoets, Jeroen, et al.
Pubblicazione: (2025)

Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL
di: Sun, Hao, et al.
Pubblicazione: (2023)

Machine Learning with Requirements: a Manifesto
di: Giunchiglia, Eleonora, et al.
Pubblicazione: (2023)

Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes
di: Seedat, Nabeel, et al.
Pubblicazione: (2023)

You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling
di: Seedat, Nabeel, et al.
Pubblicazione: (2024)

Large Language Models to Enhance Bayesian Optimization
di: Liu, Tennison, et al.
Pubblicazione: (2024)

Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes
di: Kobalczyk, Katarzyna, et al.
Pubblicazione: (2024)

Time Series Diffusion in the Frequency Domain
di: Crabbé, Jonathan, et al.
Pubblicazione: (2024)

Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments
di: Schweisthal, Jonas, et al.
Pubblicazione: (2024)

AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning
di: Imrie, Fergus, et al.
Pubblicazione: (2022)

Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference
di: Amad, Harry, et al.
Pubblicazione: (2025)

Defining Expertise: Applications to Treatment Effect Estimation
di: Hüyük, Alihan, et al.
Pubblicazione: (2024)

DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems
di: Seedat, Nabeel, et al.
Pubblicazione: (2022)

Hypothesis Hunting with Evolving Networks of Autonomous Scientific Agents
di: Liu, Tennison, et al.
Pubblicazione: (2025)

Active Task Disambiguation with LLMs
di: Kobalczyk, Katarzyna, et al.
Pubblicazione: (2025)

When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective
di: Sun, Hao, et al.
Pubblicazione: (2023)

The foundations of cost-sensitive causal classification
di: Verbeke, Wouter, et al.
Pubblicazione: (2020)

Reusing Embeddings: Reproducible Reward Model Research in Large Language Model Alignment without GPUs
di: Sun, Hao, et al.
Pubblicazione: (2025)

Deep Hierarchical Learning with Nested Subspace Networks for Large Language Models
di: Rauba, Paulius, et al.
Pubblicazione: (2025)

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback
di: Saveliev, Evgeny S., et al.
Pubblicazione: (2026)