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#2Muhammad Usama (Information Technology University)H-Index: 3
Last.Dusit NiyatoH-Index: 68
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Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. Thus, this paper provides a comprehensive survey that discusses emerging...
#1Xi LiuH-Index: 3
Last.Rui ChenH-Index: 3
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With the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experience, and boost business revenue. In recent years, we have observed a new desideratum of considering long-term rewards of multiple related recommendation tasks simultaneously. The consideration of long-term rewards is strongly tied to business revenue and growth. Learning multiple tasks simultaneously could generally im...
#1Xinshao WangH-Index: 1
#2Elyor KodirovH-Index: 8
Last.Neil R. RobertsonH-Index: 68
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In this work, we study robust deep learning against abnormal training data from the perspective of example weighting built in empirical loss functions, i.e., gradient magnitude with respect to logits, an angle that is not thoroughly studied so far. Consequently, we have two key findings: (1) Mean Absolute Error (MAE) Does Not Treat Examples Equally. We present new observations and insightful analysis about MAE, which is theoretically proved to be noise-robust. First, we reveal its underfitting p...
#1Changjian Li (UW: University of Waterloo)H-Index: 2
A lifelong reinforcement learning system is a learning system that has the ability to learn through trail-and-error interaction with the environment over its lifetime. In this paper, I give some arguments to show that the traditional reinforcement learning paradigm fails to model this type of learning system. Some insights into lifelong reinforcement learning are provided, along with a simplistic prototype lifelong reinforcement learning system.
Explanations in Machine Learning come in many forms, but a consensus regarding their desired properties is yet to emerge. In this paper we introduce a taxonomy and a set of descriptors that can be used to characterise and systematically assess explainable systems along five key dimensions: functional, operational, usability, safety and validation. In order to design a comprehensive and representative taxonomy and associated descriptors we surveyed the eXplainable Artificial Intelligence literatu...
1 CitationsSource
#1Melih Elibol (University of California, Berkeley)H-Index: 1
#2Lihua Lei (University of California, Berkeley)H-Index: 8
Last.Michael I. Jordan (Stanford University)H-Index: 128
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Variance reduction methods such as SVRG and SpiderBoost use a mixture of large and small batch gradients to reduce the variance of stochastic gradients. Compared to SGD, these methods require at least double the number of operations per update to model parameters. To reduce the computational cost of these methods, we introduce a new sparsity operator: The random-top-k operator. Our operator reduces computational complexity by estimating gradient sparsity exhibited in a variety of applications by...
#1Kacper Sokol (UoB: University of Bristol)H-Index: 2
#2Peter A. Flach (UoB: University of Bristol)H-Index: 42
The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-box algorithmic predictions influence human affairs, the inner workings of these algorithms should be scrutinised and their decisions explained to the relevant stakeholders, including the system engineers, the system's operators and the individuals whose case is being decided. While a variety of interpretability and ex...
#1Rohan R. Paleja (Georgia Institute of Technology)
#2Andrew Silva (Georgia Institute of Technology)H-Index: 2
Last.Matthew C. Gombolay (Georgia Institute of Technology)H-Index: 7
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Advances in learning from demonstration (LfD) have enabled intelligent agents to learn decision-making strategies through observation. However, humans exhibit heterogeneity in their decision-making criteria, leading to demonstrations with significant variability. We propose a personalized apprenticeship learning framework that automatically infers an interpretable representation of all human task demonstrators by extracting latent, human-specific decision-making criteria specified by an inferred...
#1Chang Ye (NYU: New York University)
#2Ahmed KhalifaH-Index: 9
Last.Julian TogeliusH-Index: 43
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Deep Reinforcement Learning (DRL) has shown impressive performance on domains with visual inputs, in particular various games. However, the agent is usually trained on a fixed environment, e.g. a fixed number of levels. A growing mass of evidence suggests that these trained models fail to generalize to even slight variations of the environments they were trained on. This paper advances the hypothesis that the lack of generalization is partly due to the input representation, and explores how rota...
In the present paper, we propose the model of {\it structural information learning machines} (SiLeM for short), leading to a mathematical definition of learning by merging the theories of computation and information. Our model shows that the essence of learning is {\it to gain information}, that to gain information is {\it to eliminate uncertainty} embedded in a data space, and that to eliminate uncertainty of a data space can be reduced to an optimization problem, that is, an {\it information o...
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Top fields of study
Machine learning
Mathematical optimization
Mathematics
Artificial neural network
Reinforcement learning