Incremental k-center clustering admits no better than 2-approximation even for non-polynomial algorithms, via a new lower-bound construction.
Xenia Schmalz, Eva Marinus, Max Coltheart, and Anne Castles
3 Pith papers cite this work. Polarity classification is still indexing.
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Proxy compression trains language models on both raw bytes and compressed sequences to enable efficient training with raw-byte inference at test time.
Performance gaps in multilingual LMs frequently arise from modeling choices such as tokenization and data exposure rather than intrinsic linguistic complexity.
citing papers explorer
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The price of incrementality in k-center clustering
Incremental k-center clustering admits no better than 2-approximation even for non-polynomial algorithms, via a new lower-bound construction.
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Proxy Compression for Language Modeling
Proxy compression trains language models on both raw bytes and compressed sequences to enable efficient training with raw-byte inference at test time.
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The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?
Performance gaps in multilingual LMs frequently arise from modeling choices such as tokenization and data exposure rather than intrinsic linguistic complexity.