{"total":51,"rows":[{"id":"2501.15638","title":"A Comprehensive Survey on Self-Interpretable Neural Networks","created":"2025-01-26","cited_by_count":18,"area":null},{"id":"2503.04363","title":"Causally Reliable Concept Bottleneck Models","created":"2025-03-06","cited_by_count":17,"area":null},{"id":"2504.19774","title":"If Concept Bottlenecks are the Question, are Foundation Models the Answer?","created":"2025-04-28","cited_by_count":15,"area":null},{"id":"2504.14094","title":"Leakage and Interpretability in Concept-Based Models","created":"2025-04-18","cited_by_count":13,"area":null},{"id":"2504.09459","title":"Measuring Leakage in Concept-Based Methods: An Information Theoretic Approach","created":"2025-04-13","cited_by_count":9,"area":null},{"id":"2504.17921","title":"Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts","created":"2025-04-24","cited_by_count":9,"area":null},{"id":"2508.14255","title":"Graph Concept Bottleneck Models","created":"2025-08-19","cited_by_count":9,"area":null},{"id":"2506.04877","title":"There Was Never a Bottleneck in Concept Bottleneck Models","created":"2025-06-05","cited_by_count":8,"area":null},{"id":"2504.18026","title":"Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization","created":"2025-04-25","cited_by_count":7,"area":"alignment"},{"id":"2501.07021","title":"Neural Probabilistic Circuits: Enabling Compositional and Interpretable Predictions through Logical Reasoning","created":"2025-01-13","cited_by_count":6,"area":"lr"},{"id":"2510.07115","title":"Enhancing Concept Localization in CLIP-based Concept Bottleneck Models","created":"2025-10-08","cited_by_count":6,"area":null},{"id":"2505.16705","title":"An Analysis of Concept Bottleneck Models: Measuring, Understanding, and Mitigating the Impact of Noisy Annotations","created":"2025-05-22","cited_by_count":5,"area":null},{"id":"2512.07383","title":"LogicCBMs: Logic-Enhanced Concept-Based Learning","created":"2025-12-08","cited_by_count":5,"area":null},{"id":"2506.05014","title":"Towards Reasonable Concept Bottleneck Models","created":"2025-06-05","cited_by_count":3,"area":null},{"id":"2506.21102","title":"Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning","created":"2025-06-26","cited_by_count":3,"area":null},{"id":"2507.04695","title":"Interpretable Reward Modeling with Active Concept Bottlenecks","created":"2025-07-07","cited_by_count":3,"area":"alignment"},{"id":"2510.05670","title":"Quantifying the Accuracy-Interpretability Trade-Off in Concept-Based Sidechannel Models","created":"2025-10-07","cited_by_count":3,"area":null},{"id":"2512.07355","title":"A Geometric Unification of Concept Learning with Concept Cones","created":"2025-12-08","cited_by_count":3,"area":"mechinterp"},{"id":"2602.23947","title":"Hierarchical Concept-based Interpretable Models","created":"2026-02-27","cited_by_count":3,"area":null},{"id":"2505.06003","title":"From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection","created":"2025-05-09","cited_by_count":2,"area":null},{"id":"2601.10562","title":"Process-Guided Concept Bottleneck Model","created":"2026-01-15","cited_by_count":2,"area":null},{"id":"2602.02886","title":"Mixture of Concept Bottleneck Experts","created":"2026-02-02","cited_by_count":2,"area":null},{"id":"2604.16076","title":"Prototype-Grounded Concept Models for Verifiable Concept Alignment","created":"2026-04-17","cited_by_count":2,"area":null},{"id":"2605.16405","title":"Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations","created":"2026-05-13","cited_by_count":2,"area":null},{"id":"2605.20612","title":"Matryoshka Concept Bottleneck Models","created":"2026-05-20","cited_by_count":2,"area":null},{"id":"2410.18705","title":"Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks","created":"2024-10-24","cited_by_count":1,"area":null},{"id":"2510.01618","title":"Automated Genomic Interpretation via Concept Bottleneck Models for Medical Robotics","created":"2025-10-02","cited_by_count":1,"area":null},{"id":"2511.19636","title":"Parameter-Efficient Construction of the Rashomon Slice for Concept Bottleneck Models","created":"2025-11-24","cited_by_count":1,"area":null},{"id":"2512.15938","title":"SALVE: Sparse Autoencoder-Latent Vector Editing for Mechanistic Control of Neural Networks","created":"2025-12-17","cited_by_count":1,"area":"mechinterp"},{"id":"2601.13953","title":"Differentiable Logic Synthesis: Spectral Coefficient Selection via Sinkhorn-Constrained Composition","created":"2026-01-20","cited_by_count":1,"area":null},{"id":"2602.19442","title":"UrbanAlign: Post-hoc Semantic Calibration for VLM-Human Preference Alignment","created":"2026-02-23","cited_by_count":1,"area":"mllm"},{"id":"2603.13884","title":"SCoCCA: Multi-modal Sparse Concept Decomposition via Canonical Correlation Analysis","created":"2026-03-14","cited_by_count":1,"area":"lr"},{"id":"2606.00082","title":"Hoeffding Concept Bottleneck Models with Applications to Overhead Images","created":"2026-05-22","cited_by_count":1,"area":null},{"id":"2606.12289","title":"The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics","created":"2026-06-10","cited_by_count":1,"area":null},{"id":"2502.05950","title":"Survival Concept-Based Learning Models","created":"2025-02-09","cited_by_count":0,"area":null},{"id":"2508.01407","title":"Signals, Concepts, and Laws: Toward Universal, Explainable Time-Series Forecasting","created":"2025-08-02","cited_by_count":0,"area":null},{"id":"2509.17522","title":"Chat-CBM: Towards Interactive Concept Bottleneck Models with Frozen Large Language Models","created":"2025-09-22","cited_by_count":0,"area":null},{"id":"2510.00773","title":"Uncertainty-Aware Concept Bottleneck Models with Enhanced Interpretability","created":"2025-10-01","cited_by_count":0,"area":null},{"id":"2510.08219","title":"Post-hoc Stochastic Concept Bottleneck Models","created":"2025-10-09","cited_by_count":0,"area":null},{"id":"2602.02239","title":"Interpretability in Deep Time Series Models Demands Semantic Alignment","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2603.01372","title":"Causal Neural Probabilistic Circuits","created":"2026-03-02","cited_by_count":0,"area":null},{"id":"2603.10084","title":"Digging Deeper: Learning Multi-Level Concept Hierarchies","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2604.02353","title":"Prism: Policy Reuse via Interpretable Strategy Mapping in Reinforcement Learning","created":"2026-03-04","cited_by_count":0,"area":"deeprl"},{"id":"2605.11558","title":"A Composite Activation Function for Learning Stable Binary Representations","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2605.29836","title":"CB-SLICE: Concept-Based Interpretable Error Slice Discovery","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2606.05471","title":"Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.10669","title":"In Defense of Information Leakage in Concept-based Models","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2606.19489","title":"Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks","created":"2026-06-17","cited_by_count":0,"area":"lr"},{"id":"2609.34459","title":"Escaping Local Views: Discovering Latent Concepts for Interpretable Multi-Agent Reinforcement Learning","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.38625","title":"Interpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbations","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.13693","title":"Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}