We develop a reinforcement learning (RL) framework for applications that...
We provide a framework for accelerating reinforcement learning (RL)
algo...
We study session-based recommendation scenarios where we want to recomme...
Batch reinforcement learning (RL) is important to apply RL algorithms to...
Robustness against image perturbations bounded by a ℓ_p ball have been
w...
Transformers have increasingly outperformed gated RNNs in obtaining new
...
We study the important and challenging problem of controllable generatio...
Assemblies of modular subsystems are being pressed into service to perfo...
We study the problem of off-policy policy optimization in Markov decisio...
Policy gradient algorithms typically combine discounted future rewards w...
Interactive Fiction (IF) games are complex textual decision making probl...
The ability to perform effective off-policy learning would revolutionize...
This paper studies the evaluation of policies that recommend an ordered ...
Most data for evaluating and training recommender systems is subject to
...
We develop a learning principle and an efficient algorithm for batch lea...