Learning Robust Options by Conditional Value at Risk Optimization

NeurIPS 2019 1 code implementation

While there are several methods to learn options that are robust against the uncertainty of model parameters, these methods only consider either the worst case or the average (ordinary) case for learning options.

Learning Robust Options by Conditional Value at Risk Optimization

NeurIPS 2019 1 code implementation

While there are several methods to learn options that are robust against the uncertainty of model parameters, these methods only consider either the worst case or the average (ordinary) case for learning options.

Random Erasing Data Augmentation

16 Aug 20176 code implementations

In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).

IMAGE AUGMENTATION IMAGE CLASSIFICATION OBJECT DETECTION PERSON RE-IDENTIFICATION

A Distributional Perspective on Reinforcement Learning

ICML 2017 11 code implementations

We obtain both state-of-the-art results and anecdotal evidence demonstrating the importance of the value distribution in approximate reinforcement learning.

ATARI GAMES

Fair Regression: Quantitative Definitions and Reduction-based Algorithms

30 May 20192 code implementations

Our schemes only require access to standard risk minimization algorithms (such as standard classification or least-squares regression) while providing theoretical guarantees on the optimality and fairness of the obtained solutions.

Market Making via Reinforcement Learning

11 Apr 20181 code implementation

Market making is a fundamental trading problem in which an agent provides liquidity by continually offering to buy and sell a security.

Probabilistic Face Embeddings

ICCV 2019 1 code implementation

Embedding methods have achieved success in face recognition by comparing facial features in a latent semantic space.

FACE RECOGNITION

Coupling Adaptive Batch Sizes with Learning Rates

15 Dec 20161 code implementation

The batch size significantly influences the behavior of the stochastic optimization algorithm, though, since it determines the variance of the gradient estimates.

IMAGE CLASSIFICATION STOCHASTIC OPTIMIZATION

The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA

8 Mar 20181 code implementation

During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with a difficult decision when defending against the Cavaliers: "Do you double and risk giving up easy shots, or stay at home and do the best you can?"

RAIL: Risk-Averse Imitation Learning

20 Jul 20171 code implementation

Generative Adversarial Imitation Learning (GAIL) is a state-of-the-art algorithm for learning policies when the expert's behavior is available as a fixed set of trajectories.

AUTONOMOUS DRIVING CONTINUOUS CONTROL IMITATION LEARNING