Search Results for author:
Found 15 papers, 2 papers with code
Try-On-Adapter: A Simple and Flexible Try-On Paradigm
A key challenge of try-on is to generate realistic images of the model wearing the garments while preserving the details of the garments.
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition
The MineRL competition is designed for the development of reinforcement learning and imitation learning algorithms that can efficiently leverage human demonstrations to drastically reduce the number of environment interactions needed to solve the complex \emph{ObtainDiamond} task with sparse rewards.
Safe Opponent-Exploitation Subgame Refinement
Search algorithms have been playing a vital role in the success of superhuman AI in both perfect information and imperfect information games.
DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis
Modern machine learning models (such as deep neural networks and boosting decision tree models) have become increasingly popular in financial market prediction, due to their superior capacity to extract complex non-linear patterns.
Deterministic Value-Policy Gradients
Based on this theoretical guarantee, we propose a class of the deterministic value gradient algorithm (DVG) with infinite horizon, and different rollout steps of the analytical gradients by the learned model trade off between the variance of the value gradients and the model bias.
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions
It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration.
Incremental training of multi-generative adversarial networks
Generative neural networks map a standard, possibly distribution to a complex high-dimensional distribution, which represents the real world data set.
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
We propose Meta-Embedding, a meta-learning-based approach that learns to generate desirable initial embeddings for new ad IDs.
Policy Optimization with Model-based Explorations
Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games.
Deterministic Policy Gradients With General State Transitions
Such a setting generalizes the widely-studied stochastic state transition setting, namely the setting of deterministic policy gradient (DPG).
Automated Mechanism Design via Neural Networks
We then apply our framework to a number of multi-item revenue optimal design settings, for a few of which the theoretically optimal mechanisms are unknown.
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module.
Policy Gradients for Contextual Recommendations
We evaluate PGCR on toy datasets as well as a real-world dataset of personalized music recommendations.
Reinforcement Mechanism Design for e-commerce
We study the problem of allocating impressions to sellers in e-commerce websites, such as Amazon, eBay or Taobao, aiming to maximize the total revenue generated by the platform.
Optimal Vehicle Dispatching Schemes via Dynamic Pricing
To this end, we use a so-called "ironing" technique to convert the problem into an equivalent convex optimization one via a clean Markov decision process (MDP) formulation, where the states are the driver distributions and the decision variables are the prices for each pair of locations.
