What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Differential privacy, Artificial neural network and Algorithm.
His work on Supervised learning as part of general Machine learning research is often related to Disadvantaged, thus linking different fields of science.
His Differential privacy research is multidisciplinary, incorporating elements of Classifier and Theoretical computer science.
His study on Generalization error is often connected to Continuous optimization as part of broader study in Artificial neural network.
His Algorithm research is multidisciplinary, incorporating perspectives in Matrix norm and Data set.
He usually deals with Deep learning and limits it to topics linked to Contextual image classification and Data point.
His most cited work include:
- Understanding deep learning requires rethinking generalization. (1483 citations)
- Fairness through awareness (1211 citations)
- Equality of opportunity in supervised learning (822 citations)
What are the main themes of his work throughout his whole career to date?
Moritz Hardt mainly focuses on Artificial intelligence, Machine learning, Algorithm, Theoretical computer science and Upper and lower bounds.
His study in Deep learning, Artificial neural network, Robustness, Benchmark and Range are all subfields of Artificial intelligence.
In his work, Data point is strongly intertwined with Regularization, which is a subfield of Deep learning.
His Machine learning study incorporates themes from Contextual image classification and Training set.
His Algorithm research focuses on subjects like Matrix completion, which are linked to Condition number.
As part of the same scientific family, he usually focuses on Theoretical computer science, concentrating on Differential privacy and intersecting with Randomized response.
He most often published in these fields:
- Artificial intelligence (32.79%)
- Machine learning (26.23%)
- Algorithm (18.03%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (32.79%)
- Machine learning (26.23%)
- Overfitting (11.48%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Machine learning, Overfitting, Mathematical optimization and Test set are his primary areas of study.
His work in Range, Benchmark, Sequence learning, Recurrent neural network and Inference are all subfields of Artificial intelligence research.
As a part of the same scientific family, Moritz Hardt mostly works in the field of Benchmark, focusing on Gradient descent and, on occasion, Applied mathematics.
His Machine learning research incorporates elements of Contextual image classification and Robustness.
His Contextual image classification research is multidisciplinary, incorporating perspectives in Data point and Deep learning.
Moritz Hardt carries out multidisciplinary research, doing studies in Overfitting and Generalization.
Between 2017 and 2021, his most popular works were:
- Sanity Checks for Saliency Maps (482 citations)
- Delayed Impact of Fair Machine Learning (110 citations)
- Gradient Descent Learns Linear Dynamical Systems (86 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of investigation include Artificial intelligence, Machine learning, Range, Harm and Gradient descent.
His Artificial intelligence research incorporates themes from Identity function and Memorization.
Moritz Hardt combines subjects such as Contextual image classification, Image, Training set and Outlier with his study of Machine learning.
His biological study spans a wide range of topics, including Regularization and Robustness.
His study in Gradient descent is interdisciplinary in nature, drawing from both Stability, Recurrent neural network, Inference and Sequence learning.
His Generalization research overlaps with other disciplines such as Sample, Deep learning, Data point, Artificial neural network and Field.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
