Saved in:
| Main Authors: | Choi, Seewon, Solko-Breslin, Alaia, Alur, Rajeev, Wong, Eric |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.24123 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Data-Efficient Learning with Neural Programs
by: Solko-Breslin, Alaia, et al.
Published: (2024)
by: Solko-Breslin, Alaia, et al.
Published: (2024)
CAMEL: An ECG Language Model for Forecasting Cardiac Events
by: Velingker, Neelay, et al.
Published: (2026)
by: Velingker, Neelay, et al.
Published: (2026)
Stable Prediction of Adverse Events in Medical Time-Series Data
by: Keoliya, Mayank, et al.
Published: (2025)
by: Keoliya, Mayank, et al.
Published: (2025)
Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities
by: Khare, Avishree, et al.
Published: (2023)
by: Khare, Avishree, et al.
Published: (2023)
Risk-Sensitive Agent Compositions
by: Shabadi, Guruprerana, et al.
Published: (2025)
by: Shabadi, Guruprerana, et al.
Published: (2025)
Dolphin: A Programmable Framework for Scalable Neurosymbolic Learning
by: Naik, Aaditya, et al.
Published: (2024)
by: Naik, Aaditya, et al.
Published: (2024)
Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference
by: Xue, Anton, et al.
Published: (2024)
by: Xue, Anton, et al.
Published: (2024)
Scenario-based Compositional Verification of Autonomous Systems with Neural Perception
by: Watson, Christopher, et al.
Published: (2025)
by: Watson, Christopher, et al.
Published: (2025)
Chordal Sparsity for SDP-based Neural Network Verification
by: Xue, Anton, et al.
Published: (2022)
by: Xue, Anton, et al.
Published: (2022)
Do We Need Frontier Models to Verify Mathematical Proofs?
by: Naik, Aaditya, et al.
Published: (2026)
by: Naik, Aaditya, et al.
Published: (2026)
Tensor Sketch: Fast and Scalable Polynomial Kernel Approximation
by: Pham, Ninh, et al.
Published: (2025)
by: Pham, Ninh, et al.
Published: (2025)
Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks
by: Xue, Anton, et al.
Published: (2022)
by: Xue, Anton, et al.
Published: (2022)
Neurosymbolic Grounding for Compositional World Models
by: Sehgal, Atharva, et al.
Published: (2023)
by: Sehgal, Atharva, et al.
Published: (2023)
On the Hardness of Probabilistic Neurosymbolic Learning
by: Maene, Jaron, et al.
Published: (2024)
by: Maene, Jaron, et al.
Published: (2024)
From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditing
by: Cen, Sarah H., et al.
Published: (2024)
by: Cen, Sarah H., et al.
Published: (2024)
The Impossibility of Inverse Permutation Learning in Transformer Models
by: Alur, Rohan, et al.
Published: (2025)
by: Alur, Rohan, et al.
Published: (2025)
Lego Sketch: A Scalable Memory-augmented Neural Network for Sketching Data Streams
by: Feng, Yuan, et al.
Published: (2025)
by: Feng, Yuan, et al.
Published: (2025)
Optimisation in Neurosymbolic Learning Systems
by: van Krieken, Emile
Published: (2024)
by: van Krieken, Emile
Published: (2024)
On the Independence Assumption in Neurosymbolic Learning
by: van Krieken, Emile, et al.
Published: (2024)
by: van Krieken, Emile, et al.
Published: (2024)
On Calibration in Multi-Distribution Learning
by: Verma, Rajeev, et al.
Published: (2024)
by: Verma, Rajeev, et al.
Published: (2024)
Uncertainty Quantification for Neurosymbolic Programs via Compositional Conformal Prediction
by: Ramalingam, Ramya, et al.
Published: (2024)
by: Ramalingam, Ramya, et al.
Published: (2024)
Imbalances in Neurosymbolic Learning: Characterization and Mitigating Strategies
by: Wang, Kaifu, et al.
Published: (2024)
by: Wang, Kaifu, et al.
Published: (2024)
On Improving Neurosymbolic Learning by Exploiting the Representation Space
by: Naik, Aaditya, et al.
Published: (2026)
by: Naik, Aaditya, et al.
Published: (2026)
Adaptive-GraphSketch: Real-Time Edge Anomaly Detection via Multi-Layer Tensor Sketching and Temporal Decay
by: Ekle, Ocheme Anthony, et al.
Published: (2025)
by: Ekle, Ocheme Anthony, et al.
Published: (2025)
Neurosymbolic Diffusion Models
by: van Krieken, Emile, et al.
Published: (2025)
by: van Krieken, Emile, et al.
Published: (2025)
Formally Verified Neurosymbolic Trajectory Learning via Tensor-based Linear Temporal Logic on Finite Traces
by: Chevallier, Mark, et al.
Published: (2025)
by: Chevallier, Mark, et al.
Published: (2025)
Towards Compositionality in Concept Learning
by: Stein, Adam, et al.
Published: (2024)
by: Stein, Adam, et al.
Published: (2024)
Human Expertise in Algorithmic Prediction
by: Alur, Rohan, et al.
Published: (2024)
by: Alur, Rohan, et al.
Published: (2024)
Generative Modeling via Hierarchical Tensor Sketching
by: Peng, Yifan, et al.
Published: (2023)
by: Peng, Yifan, et al.
Published: (2023)
Neurosymbolic Conformal Classification
by: Ledaguenel, Arthur, et al.
Published: (2024)
by: Ledaguenel, Arthur, et al.
Published: (2024)
Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection
by: Tran, Huynh T. T., et al.
Published: (2025)
by: Tran, Huynh T. T., et al.
Published: (2025)
Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation
by: Ran, Yide, et al.
Published: (2026)
by: Ran, Yide, et al.
Published: (2026)
Dynamic Context Evolution for Scalable Synthetic Data Generation
by: Lingo, Ryan, et al.
Published: (2026)
by: Lingo, Ryan, et al.
Published: (2026)
Neurosymbolic Imitation Learning with Human Guidance: A Privileged Information Approach
by: Prabhakar, Nikhilesh, et al.
Published: (2026)
by: Prabhakar, Nikhilesh, et al.
Published: (2026)
Relational Neurosymbolic Markov Models
by: De Smet, Lennert, et al.
Published: (2024)
by: De Smet, Lennert, et al.
Published: (2024)
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
by: Ahmad, Tamim El, et al.
Published: (2023)
by: Ahmad, Tamim El, et al.
Published: (2023)
Learning to Defer to a Population: A Meta-Learning Approach
by: Tailor, Dharmesh, et al.
Published: (2024)
by: Tailor, Dharmesh, et al.
Published: (2024)
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
by: Ding, Mucong, et al.
Published: (2024)
by: Ding, Mucong, et al.
Published: (2024)
Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching
by: Angell, Rico, et al.
Published: (2023)
by: Angell, Rico, et al.
Published: (2023)
Neurosymbolic Reasoning Shortcuts under the Independence Assumption
by: van Krieken, Emile, et al.
Published: (2025)
by: van Krieken, Emile, et al.
Published: (2025)
Similar Items
-
Data-Efficient Learning with Neural Programs
by: Solko-Breslin, Alaia, et al.
Published: (2024) -
CAMEL: An ECG Language Model for Forecasting Cardiac Events
by: Velingker, Neelay, et al.
Published: (2026) -
Stable Prediction of Adverse Events in Medical Time-Series Data
by: Keoliya, Mayank, et al.
Published: (2025) -
Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities
by: Khare, Avishree, et al.
Published: (2023) -
Risk-Sensitive Agent Compositions
by: Shabadi, Guruprerana, et al.
Published: (2025)