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Found 8 papers, 2 papers with code
ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference
Aligning language models to human expectations, e. g., being helpful and harmless, has become a pressing challenge for large language models.
Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning
We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation.
Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems
Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards.
Generalized Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses
\emph{Classification with rejection} (CwR) refrains from making a prediction to avoid critical misclassification when encountering test samples that are difficult to classify.
A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning
To apply value-based methods to CRL, a recent groundbreaking line of game-theoretic approaches uses the mixed policy that randomizes among a set of carefully generated policies to converge to the desired constraint-satisfying policy.
LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign
In this paper, we analyze the network A/B testing problem under a real-world online marketing campaign, describe our proposed LinkLouvain method, and evaluate it on real-world data.
A Reduction Approach to Constrained Reinforcement Learning
In this paper, we present a reduction approach to find sparse policies that randomize among a constant number of policies for the constrained RL problem.
Robust Offline Reinforcement Learning from Low-Quality Data
We theoretically show that AdaPT produces a tight upper bound on the distributional deviation between the learned policy and the behavior policy, and this upper bound is the minimum requirement to guarantee policy improvement at each iteration.
