10 research outputs found
A near-optimal change-detection based algorithm for piecewise-stationary combinatorial semi-bandits
We investigate the piecewise-stationary combinatorial semi-bandit problem. Compared to the original combinatorial semi-bandit problem, our setting assumes the reward distributions of base arms may change in a piecewise-stationary manner at unknown time steps. We propose an algorithm, GLR-CUCB, which incorporates an efficient combinatorial semi-bandit algorithm, CUCB, with an almost parameter-free change-point detector, the Generalized Likelihood Ratio Test (GLRT). Our analysis shows that the regret of GLR-CUCB is upper bounded by O(√NKT log T), where N is the number of piecewise-stationary segments, K is the number of base arms, and T is the number of time steps. As a complement, we also derive a nearly matching regret lower bound on the order of Ω(√NKT), for both piecewise-stationary multi-armed bandits and combinatorial semi-bandits, using information-theoretic techniques and judiciously constructed piecewise-stationary bandit instances. Our lower bound is tighter than the best available regret lower bound, which is Ω(√T). Numerical experiments on both synthetic and real-world datasets demonstrate the superiority of GLR-CUCB compared to other state-of-the-art algorithms
A Definition of Non-Stationary Bandits
Despite the subject of non-stationary bandit learning having attracted much
recent attention, we have yet to identify a formal definition of
non-stationarity that can consistently distinguish non-stationary bandits from
stationary ones. Prior work has characterized non-stationary bandits as bandits
for which the reward distribution changes over time. We demonstrate that this
definition can ambiguously classify the same bandit as both stationary and
non-stationary; this ambiguity arises in the existing definition's dependence
on the latent sequence of reward distributions. Moreover, the definition has
given rise to two widely used notions of regret: the dynamic regret and the
weak regret. These notions are not indicative of qualitative agent performance
in some bandits. Additionally, this definition of non-stationary bandits has
led to the design of agents that explore excessively. We introduce a formal
definition of non-stationary bandits that resolves these issues. Our new
definition provides a unified approach, applicable seamlessly to both Bayesian
and frequentist formulations of bandits. Furthermore, our definition ensures
consistent classification of two bandits offering agents indistinguishable
experiences, categorizing them as either both stationary or both
non-stationary. This advancement provides a more robust framework for
non-stationary bandit learning
Tracking Most Significant Shifts in Nonparametric Contextual Bandits
We study nonparametric contextual bandits where Lipschitz mean reward
functions may change over time. We first establish the minimax dynamic regret
rate in this less understood setting in terms of number of changes and
total-variation , both capturing all changes in distribution over context
space, and argue that state-of-the-art procedures are suboptimal in this
setting.
Next, we tend to the question of an adaptivity for this setting, i.e.
achieving the minimax rate without knowledge of or . Quite importantly,
we posit that the bandit problem, viewed locally at a given context ,
should not be affected by reward changes in other parts of context space . We therefore propose a notion of change, which we term experienced
significant shifts, that better accounts for locality, and thus counts
considerably less changes than and . Furthermore, similar to recent work
on non-stationary MAB (Suk & Kpotufe, 2022), experienced significant shifts
only count the most significant changes in mean rewards, e.g., severe best-arm
changes relevant to observed contexts.
Our main result is to show that this more tolerant notion of change can in
fact be adapted to
Learning to Price Supply Chain Contracts against a Learning Retailer
The rise of big data analytics has automated the decision-making of companies
and increased supply chain agility. In this paper, we study the supply chain
contract design problem faced by a data-driven supplier who needs to respond to
the inventory decisions of the downstream retailer. Both the supplier and the
retailer are uncertain about the market demand and need to learn about it
sequentially. The goal for the supplier is to develop data-driven pricing
policies with sublinear regret bounds under a wide range of possible retailer
inventory policies for a fixed time horizon.
To capture the dynamics induced by the retailer's learning policy, we first
make a connection to non-stationary online learning by following the notion of
variation budget. The variation budget quantifies the impact of the retailer's
learning strategy on the supplier's decision-making. We then propose dynamic
pricing policies for the supplier for both discrete and continuous demand. We
also note that our proposed pricing policy only requires access to the support
of the demand distribution, but critically, does not require the supplier to
have any prior knowledge about the retailer's learning policy or the demand
realizations. We examine several well-known data-driven policies for the
retailer, including sample average approximation, distributionally robust
optimization, and parametric approaches, and show that our pricing policies
lead to sublinear regret bounds in all these cases.
At the managerial level, we answer affirmatively that there is a pricing
policy with a sublinear regret bound under a wide range of retailer's learning
policies, even though she faces a learning retailer and an unknown demand
distribution. Our work also provides a novel perspective in data-driven
operations management where the principal has to learn to react to the learning
policies employed by other agents in the system