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cs.LG2026
Certified Approximation for Interpretable Representer Landmarks
Jayanta Mukherjee, Shourya Verma, Mengbo Wang +2
Representer explanations rank the training landmarks that most influence a self-supervised representation. At scale, this ranking rests on up to four stacked approximations of the…
cs.LG2026
Learning Implicit Causal World Models from Multi-Agent Demonstrations
Jasorsi Ghosh
In model-based reinforcement learning, world models exist as internal simulators, but their training often conflates statistical correlations with causal mechanisms. This problem i…