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7 changes: 3 additions & 4 deletions _library/2026-tmlr-bandit.md
Original file line number Diff line number Diff line change
Expand Up @@ -6,9 +6,9 @@ venue: "Transactions on Machine Learning Research"
org_unit: "University of Waterloo Partnership"
domains: ["advice"]
authors: "Wei Min Loh, Sajib Kumer Sinha, Ankur Agarwal, Pascal Poupart"
external_url: https://arxiv.org/abs/your-paper-id # TBU
code_url: https://github.com/manulife/your-repo # TBU
pdf_url: https://example.com/paper.pdf # TBU
external_url: https://openreview.net/forum?id=tRbwfej9uY
code_url: https://github.com/wmloh/c3
pdf_url: https://openreview.net/pdf?id=tRbwfej9uY
summary: "This paper introduces conditionally coupled contextual (C3) Thompson Sampling, a method for non‑linear, time‑varying contextual bandits that improves regret and click‑through performance over existing algorithms."
---

Expand All @@ -20,4 +20,3 @@ This paper introduces conditionally coupled contextual (C3) Thompson Sampling, a

**Results:** Empirical results show that C3 outperforms the next best algorithm by 5.7% lower average cumulative regret on four OpenML tabular datasets as well as demonstrating a 12.4% click lift on Microsoft News Dataset (MIND) compared to other algorithms.

*To be published soon*
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