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Papers
ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression
MLHC 2025
Interpolating Neural Network-Tensor Decomposition (INN-TD): a scalable and interpretable approach for large-scale physics-based problems
ICML 2025
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment
ICML 2025
Interpretable Deep Clustering for Tabular Data
ICML 2024
Learning Exceptional Subgroups by End-to-End Maximizing KL-Divergence
ICML 2024
Learning Transparent Reward Models via Unsupervised Feature Selection
CoRL 2024
Multi-group Learning for Hierarchical Groups
ICML 2024
Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph
ICML 2024