Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2605.30509 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911729498193920 |
|---|---|
| author | Cohen, Doron Kontorovich, Aryeh Livshitz, Yonatan |
| author_facet | Cohen, Doron Kontorovich, Aryeh Livshitz, Yonatan |
| contents | We present improved bounds for estimating discrete probability distributions under the $\ell_\infty$ norm. These include minimax bounds in expectation and high-probability tail bounds. We resolve some of the open questions posed in Kontorovich and Painsky (JMLR, 2025) -- including a fully empirical version of the tightest risk bound they presented and identifying the form of the worst-case extremal distribution. Encouraging empirical results are reported as well. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30509 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Improved Distribution Estimation in $\ell_\infty$ Cohen, Doron Kontorovich, Aryeh Livshitz, Yonatan Machine Learning Artificial Intelligence We present improved bounds for estimating discrete probability distributions under the $\ell_\infty$ norm. These include minimax bounds in expectation and high-probability tail bounds. We resolve some of the open questions posed in Kontorovich and Painsky (JMLR, 2025) -- including a fully empirical version of the tightest risk bound they presented and identifying the form of the worst-case extremal distribution. Encouraging empirical results are reported as well. |
| title | Improved Distribution Estimation in $\ell_\infty$ |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.30509 |