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Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction
This paper presents a unifying framework for heterogeneous FL algorithms with online model extraction and provides a general convergence analysis for the first time.
Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction
This paper presents a unifying framework for heterogeneous FL algorithms with online model extraction and provides a general convergence analysis for the first time.
c5dac56bdbbee9fb457946742d613d71-Paper-Conference.pdf
In particular, we will be interested in log-supermodular (LSM) potential functions as they encourage agreement between the variables upon which theyoperate.
c5cf13bfd3762821ef7607e63ee90075-Supplemental-Conference.pdf
Suppose that there arennodes overaconnected graph G = {E,V}, where E and V represent the edges and vertices.
c5cf13bfd3762821ef7607e63ee90075-Paper-Conference.pdf
Suppose that there arennodes overaconnected graph G = {E,V}, where E and V represent the edges and vertices.
AsymptoticallyExactErrorCharacterizationof OfflinePolicyEvaluationwithMisspecifiedLinear Models
Recently, theoretical understanding of OPE has been rapidly advanced under (approximate) realizability assumptions, i.e., where the environments of interest are well approximated with the given hypothetical models.
Exact inference in structured prediction
Neural Information Processing Systems http://nips.cc/