Education
Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context
In this paper, we develop algorithms that support the deployment of contextual linear bandits in distributed settings.
Online Learning in MDPs with Linear Function Approximation and Bandit Feedback
Consequently, the state of the environment changes according to the transition function of the underlying MDP, as a function of the previous state and the action taken by the learner.
Memory Efficient Continual Learning with Transformers
To address the issue of incremental fine-tuning of pre-trained Transformers in the sequential learning setting without CF, we propose Adaptive Distillation of Adapters (ADA).
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees
Inverse reinforcement learning (IRL) aims to recover the reward function and the associated optimal policy that best fits observed sequences of states and actions implemented by an expert.
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees
Inverse reinforcement learning (IRL) aims to recover the reward function and the associated optimal policy that best fits observed sequences of states and actions implemented by an expert.
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
The main idea is to use a generator to synthesize data for training the student.
