A mean-pool deep set trained on sets of size at most two produces an encoder that generalizes to arbitrary sizes, decoupling representation learning from posterior modeling and making training cost independent of deployment set size N.
The 22nd international conference on artificial intelligence and statistics , pages=
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Introduces a robust OT divergence with stochastic subgradient algorithm and bootstrap-based SBI procedure for parameter inference under joint geometric and TV contamination.
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It Just Takes Two: Scaling Amortized Inference to Large Sets
A mean-pool deep set trained on sets of size at most two produces an encoder that generalizes to arbitrary sizes, decoupling representation learning from posterior modeling and making training cost independent of deployment set size N.
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Robust Simulation Based Inference Through Robust Optimal Transport
Introduces a robust OT divergence with stochastic subgradient algorithm and bootstrap-based SBI procedure for parameter inference under joint geometric and TV contamination.