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Found 7 papers, 3 papers with code
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables
To address the challenge of backpropagating the gradient through categorical variables, we propose the augment-REINFORCE-swap-merge (ARSM) gradient estimator that is unbiased and has low variance.
Implicit Distributional Reinforcement Learning
To improve the sample efficiency of policy-gradient based reinforcement learning algorithms, we propose implicit distributional actor-critic (IDAC) that consists of a distributional critic, built on two deep generator networks (DGNs), and a semi-implicit actor (SIA), powered by a flexible policy distribution.
Discrete Action On-Policy Learning with Action-Value Critic
Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension, making it challenging to apply existing on-policy gradient based deep RL algorithms efficiently.
A Unified Framework for Tuning Hyperparameters in Clustering Problems
In this paper, we provide a framework with provable guarantees for selecting hyperparameters in a number of distinct models.
Semi-supervised Learning using Adversarial Training with Good and Bad Samples
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training.
On hyperparameter tuning in general clustering problemsm
Tuning hyperparameters for unsupervised learning problems is difficult in general due to the lack of ground truth for validation.
Learning to Rank For Push Notifications Using Pairwise Expected Regret
Listwise ranking losses have been widely studied in recommender systems.
