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Explaining Black-Box Classifiers by Implicitly Learning Decision Trees

Lange, Jane (Massachusetts Institute of Technology, 2025-05)

We present algorithms for finding two types of objects that explain the classification of a black-box model f : {±1}^d → {±1} on an instance x ∈ {±1}^d . The first is a certificate: a small set of x’s features that in ...

Analog On-chip Training and Inference with Non-volatile Memory Devices

Lee, Jungsoo (Massachusetts Institute of Technology, 2025-05)

As the demand for computation in neural networks continues to rise, conventional computing resources are increasingly constrained by their limited energy efficiency. One promising solution to this challenge is analog ...

CMOS-Compatible Wafer-Scale Synthesis and Rapid Characterization of Two-Dimensional Transition Metal Dichalcogenides

Jiao, Yixuan (Massachusetts Institute of Technology, 2025-05)

Two-dimensional transition metal dichalcogenides (TMDs) such as monolayer MoS₂ offer great promise for next generation nanoelectronics due to their atomic thickness, tunable bandgaps, and excellent electrostatic control. ...

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Graduate Theses: Recent submissions

Explaining Black-Box Classifiers by Implicitly Learning Decision Trees

Analog On-chip Training and Inference with Non-volatile Memory Devices

CMOS-Compatible Wafer-Scale Synthesis and Rapid Characterization of Two-Dimensional Transition Metal Dichalcogenides

Graduate Theses: Recent submissions

Explaining Black-Box Classifiers by Implicitly Learning Decision Trees ﻿

Analog On-chip Training and Inference with Non-volatile Memory Devices ﻿

CMOS-Compatible Wafer-Scale Synthesis and Rapid Characterization of Two-Dimensional Transition Metal Dichalcogenides ﻿

Explaining Black-Box Classifiers by Implicitly Learning Decision Trees

Analog On-chip Training and Inference with Non-volatile Memory Devices

CMOS-Compatible Wafer-Scale Synthesis and Rapid Characterization of Two-Dimensional Transition Metal Dichalcogenides