1,805 research outputs found
Incorporating Behavioral Constraints in Online AI Systems
AI systems that learn through reward feedback about the actions they take are
increasingly deployed in domains that have significant impact on our daily
life. However, in many cases the online rewards should not be the only guiding
criteria, as there are additional constraints and/or priorities imposed by
regulations, values, preferences, or ethical principles. We detail a novel
online agent that learns a set of behavioral constraints by observation and
uses these learned constraints as a guide when making decisions in an online
setting while still being reactive to reward feedback. To define this agent, we
propose to adopt a novel extension to the classical contextual multi-armed
bandit setting and we provide a new algorithm called Behavior Constrained
Thompson Sampling (BCTS) that allows for online learning while obeying
exogenous constraints. Our agent learns a constrained policy that implements
the observed behavioral constraints demonstrated by a teacher agent, and then
uses this constrained policy to guide the reward-based online exploration and
exploitation. We characterize the upper bound on the expected regret of the
contextual bandit algorithm that underlies our agent and provide a case study
with real world data in two application domains. Our experiments show that the
designed agent is able to act within the set of behavior constraints without
significantly degrading its overall reward performance.Comment: 9 pages, 6 figure
Selling to a No-Regret Buyer
We consider the problem of a single seller repeatedly selling a single item
to a single buyer (specifically, the buyer has a value drawn fresh from known
distribution in every round). Prior work assumes that the buyer is fully
rational and will perfectly reason about how their bids today affect the
seller's decisions tomorrow. In this work we initiate a different direction:
the buyer simply runs a no-regret learning algorithm over possible bids. We
provide a fairly complete characterization of optimal auctions for the seller
in this domain. Specifically:
- If the buyer bids according to EXP3 (or any "mean-based" learning
algorithm), then the seller can extract expected revenue arbitrarily close to
the expected welfare. This auction is independent of the buyer's valuation ,
but somewhat unnatural as it is sometimes in the buyer's interest to overbid. -
There exists a learning algorithm such that if the buyer bids
according to then the optimal strategy for the seller is simply
to post the Myerson reserve for every round. - If the buyer bids according
to EXP3 (or any "mean-based" learning algorithm), but the seller is restricted
to "natural" auction formats where overbidding is dominated (e.g. Generalized
First-Price or Generalized Second-Price), then the optimal strategy for the
seller is a pay-your-bid format with decreasing reserves over time. Moreover,
the seller's optimal achievable revenue is characterized by a linear program,
and can be unboundedly better than the best truthful auction yet simultaneously
unboundedly worse than the expected welfare
Hierarchical Exploration for Accelerating Contextual Bandits
Contextual bandit learning is an increasingly popular approach to optimizing
recommender systems via user feedback, but can be slow to converge in practice
due to the need for exploring a large feature space. In this paper, we propose
a coarse-to-fine hierarchical approach for encoding prior knowledge that
drastically reduces the amount of exploration required. Intuitively, user
preferences can be reasonably embedded in a coarse low-dimensional feature
space that can be explored efficiently, requiring exploration in the
high-dimensional space only as necessary. We introduce a bandit algorithm that
explores within this coarse-to-fine spectrum, and prove performance guarantees
that depend on how well the coarse space captures the user's preferences. We
demonstrate substantial improvement over conventional bandit algorithms through
extensive simulation as well as a live user study in the setting of
personalized news recommendation.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
We develop a learning principle and an efficient algorithm for batch learning
from logged bandit feedback. This learning setting is ubiquitous in online
systems (e.g., ad placement, web search, recommendation), where an algorithm
makes a prediction (e.g., ad ranking) for a given input (e.g., query) and
observes bandit feedback (e.g., user clicks on presented ads). We first address
the counterfactual nature of the learning problem through propensity scoring.
Next, we prove generalization error bounds that account for the variance of the
propensity-weighted empirical risk estimator. These constructive bounds give
rise to the Counterfactual Risk Minimization (CRM) principle. We show how CRM
can be used to derive a new learning method -- called Policy Optimizer for
Exponential Models (POEM) -- for learning stochastic linear rules for
structured output prediction. We present a decomposition of the POEM objective
that enables efficient stochastic gradient optimization. POEM is evaluated on
several multi-label classification problems showing substantially improved
robustness and generalization performance compared to the state-of-the-art.Comment: 10 page
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