Low-rank Matrix Bandits with Heavy-tailed Rewards
This work addresses a more realistic scenario in online learning and recommendation systems by handling heavy-tailed noise, which is incremental as it extends prior sub-Gaussian assumptions.
The paper tackles the problem of low-rank matrix bandits with heavy-tailed rewards, where rewards have finite moments instead of sub-Gaussian noise, and proposes the LOTUS algorithm achieving a regret bound of order ˜O(d^(3/2)r^(1/2)T^(1/(1+δ))/˜D_rr) that matches state-of-the-art bounds under sub-Gaussian conditions and is nearly optimal in T, with a lower bound of Ω(d^(δ/(1+δ))r^(δ/(1+δ))T^(1/(1+δ))).
In stochastic low-rank matrix bandit, the expected reward of an arm is equal to the inner product between its feature matrix and some unknown $d_1$ by $d_2$ low-rank parameter matrix $Θ^*$ with rank $r \ll d_1\wedge d_2$. While all prior studies assume the payoffs are mixed with sub-Gaussian noises, in this work we loosen this strict assumption and consider the new problem of \underline{low}-rank matrix bandit with \underline{h}eavy-\underline{t}ailed \underline{r}ewards (LowHTR), where the rewards only have finite $(1+δ)$ moment for some $δ\in (0,1]$. By utilizing the truncation on observed payoffs and the dynamic exploration, we propose a novel algorithm called LOTUS attaining the regret bound of order $\tilde O(d^\frac{3}{2}r^\frac{1}{2}T^\frac{1}{1+δ}/\tilde{D}_{rr})$ without knowing $T$, which matches the state-of-the-art regret bound under sub-Gaussian noises~\citep{lu2021low,kang2022efficient} with $δ= 1$. Moreover, we establish a lower bound of the order $Ω(d^\fracδ{1+δ} r^\fracδ{1+δ} T^\frac{1}{1+δ}) = Ω(T^\frac{1}{1+δ})$ for LowHTR, which indicates our LOTUS is nearly optimal in the order of $T$. In addition, we improve LOTUS so that it does not require knowledge of the rank $r$ with $\tilde O(dr^\frac{3}{2}T^\frac{1+δ}{1+2δ})$ regret bound, and it is efficient under the high-dimensional scenario. We also conduct simulations to demonstrate the practical superiority of our algorithm.