Authorship attribution aims to determine whether a query text and a candidate document are written by the same author. %While dense embeddings are effective, recent work suggests that low-dimensional representations limit encoding capacity. We investigate high-dimensional learned sparse representations (LSR) and multi-vector retrieval for authorship attribution. We further propose Sparse ColBERT (SparCol), a late-interaction sparse lexical reranking model. To address the memory cost of vocabulary-size embeddings, we develop fused Triton kernels that can reduce peak memory usage by 5 11 times over the baseline torch implementation. Extensive experiments show that LSR can outperform dense embeddings in both single-genre and cross-genre settings, whereas improves reranking performance over single-vector models only in single-genre settings due to lexical matching bias. In addition to token-based embeddings, we also explore Lexicon-based embeddings that score clusters of tokens, which is competitive with the vocabulary-based LSR with reduced dimensions. We conduct interpretability analysis of LSRs, showing that they allow for a more transparent scoring process.