Mehran Sahami

NeurIPS Conference 2015 Conference Paper

Deep Knowledge Tracing

• Chris Piech
• Jonathan Bassen
• Jonathan Huang
• Surya Ganguli
• Mehran Sahami
• Leonidas Guibas
• Jascha Sohl-Dickstein

Knowledge tracing, where a machine models the knowledge of a student as they interact with coursework, is an established and significantly unsolved problem in computer supported education. In this paper we explore the benefit of using recurrent neural networks to model student learning. This family of models have important advantages over current state of the art methods in that they do not require the explicit encoding of human domain knowledge, and have a far more flexible functional form which can capture substantially more complex student interactions. We show that these neural networks outperform the current state of the art in prediction on real student data, while allowing straightforward interpretation and discovery of structure in the curriculum. These results suggest a promising new line of research for knowledge tracing.

ICML Conference 2015 Conference Paper

Learning Program Embeddings to Propagate Feedback on Student Code

• Chris Piech
• Jonathan Huang
• Andy Nguyen
• Mike Phulsuksombati
• Mehran Sahami
• Leonidas J. Guibas

Providing feedback, both assessing final work and giving hints to stuck students, is difficult for open-ended assignments in massive online classes which can range from thousands to millions of students. We introduce a neural network method to encode programs as a linear mapping from an embedded precondition space to an embedded postcondition space and propose an algorithm for feedback at scale using these linear maps as features. We apply our algorithm to assessments from the Code. org Hour of Code and Stanford University’s CS1 course, where we propagate human comments on student assignments to orders of magnitude more submissions.

AAAI Conference 1997 Conference Paper

Applications of Machine Learning to Information Access

• Mehran Sahami

ICML Conference 1997 Conference Paper

Hierarchically Classifying Documents Using Very Few Words

• Daphne Koller
• Mehran Sahami

AAAI Conference 1997 Short Paper

Real-Time Full-Text Clustering of Networked Documents

• Mehran Sahami

ICML Conference 1996 Conference Paper

Applying the Multiple Cause Mixture Model to Text Categorization

• Mehran Sahami
• Marti A. Hearst
• Eric Saund

EAAI Journal 1996 Journal Article

Generating neural networks through the induction of threshold logic unit trees

• Mehran Sahami

This paper investigates the generation of neural networks through the induction of binary trees of threshold logic units (TL Us Initially, it describes the framework for a tree construction algorithm devised by the author, and shows how it helps to bridge the gap between pure connectionist (neural network) and symbolic (decision tree) paradigms. It also shows how the trees of threshold units that are induced can be transformed into an isomorphic neural-network topology. Several methods for learning the linear discriminant functions at each node of the tree structure are examined, and are shown to produce accuracy results that are comparable to classical information-theoretic methods for constructing decision trees (which use single feature tests at each node), but produce trees that are smaller and thus easier to understand. Moreover, the results also show that it is possible to simultaneously learn both the topology and weight settings of a neural network simply using the training data set that is initially given.

ICML Conference 1996 Conference Paper

Toward Optimal Feature Selection

• Daphne Koller
• Mehran Sahami

ICML Conference 1995 Conference Paper

Supervised and Unsupervised Discretization of Continuous Features

• James Dougherty
• Ron Kohavi
• Mehran Sahami

AAAI Conference 1993 Conference Paper

Learning Non-Linearly Separable Boolean Functions With Linear Threshold Unit Trees and Madaline-Style Networks

• Mehran Sahami