Ultra-marginal Feature Importance

Scientists frequently prioritize learning from data rather than training the best possible model; however, research in machine learning often prioritizes the latter. Marginal feature importance methods, such as marginal contribution feature importance (MCI), attempt to break this trend by providing a useful framework for quantifying the relationships in data in an interpretable fashion. In this work, we aim to improve upon the theoretical properties, performance, and runtime of MCI by introducing ultra-marginal feature importance (UMFI), which uses preprocessing methods from the AI fairness literature to remove dependencies in the feature set prior to model evaluation. We show on real and simulated data that UMFI performs at least as well as MCI, with significantly better performance in the presence of correlated interactions and unrelated features, while partially learning the structure of the causal graph and substantially reducing the exponential runtime of MCI to super-linear

Customizing Information Capture and Access

This article presents a customizable architecture for software agents that capture and access information in large, heterogeneous, distributed electronic repositories. The key idea is to exploit underlying structure at various levels of granularity to build high-level indices with task-specific interpretations. Information agents construct such indices and are configured as a network of reusable modules called structure detectors and segmenters. We illustrate our architecture with the design and implementation of smart information filters in two contexts: retrieving stock market data from Internet newsgroups and retrieving technical reports from Internet FTP sites

Design of programmable matter

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 115-119).Programmable matter is a proposed digital material having computation, sensing, actuation, and display as continuous properties active over its whole extent. Programmable matter would have many exciting applications, like paintable displays, shape-changing robots and tools, rapid prototyping, and sculpture-based haptic interfaces. Programmable matter would be composed of millimeter-scale autonomous microsystem particles, without internal moving parts, bound by electromagnetic forces or an adhesive binder. Particles can dissipate 10 mW heat, and store 6 J energy in an internal zinc-air battery. Photovoltaic cells provide 300 [mu]W outdoors and 3.0 [mu]W indoors. Painted systems can store battery reactants in the paint binder; 6 J / mm3 can be stored, and diffusion is fast enough to transport reactants to the particles. Capacitive power transfer is an efficient method to transfer power to sparse, randomly placed particles. Power from capacitive transfer is proportional to VDD 2: 100[mu]W at 3.3V and 12 mW at 35V. Inter-particle communication is possible via optical, near-field, and far-field electromagnetic systems. Optical systems allow communication with low area (sub-mm) particles, and 24 pJ/bit. Near-field electromagnetic gives precisely controlled neighborhoods, localization capability, and 37 pJ/bit. Far-field radio communication between widely spaced particles may be possible at 60 GHz; antennas that fit inside 1 mm3 exist; complete transceivers do not. A 32-bit CPU uses less than 0.26 mm2 die area, 256K x 8 SRAM uses 1.1 mm2, and 256K x 8 FLASH uses 0.32 mm2. Direct-drive electric and magnetic field systems allow actuation without moving parts inside the particles. Magnetic surface-drive motors designed for operation without bearings are not power-efficient, and parasitic interactions between permanent magnets may limit their usefulness at millimeter particle dimensions. Electrostatic surface-drive motors are power-efficient, but practical only at particle dimensions below a few millimeters. We constructed a prototype paintable display; a distributed PostScript rendering system with 1000 randomly-placed 3.4 cm nodes, each with a CPU, IR communications, and LED. The system is used to render the letter "A." We present a design, not yet constructed, for a literal paintable display, with 1.0 mm rendering particles, each with a microprocessor and memory, and 110 [mu]m display particles, with tri-color LED's and simpler circuitry. Storage of zinc-air battery reactants in the paint binder would provide an 8 hour battery life, and capacitive power distribution would allow continuous operation. We constructed a prototype sliding-cube modular robot, with 3.4 cm nodes. The system uses magnetic surface-drive actuation. We demonstrate horizontal lattice-unit translation. We describe a design, not yet constructed, for a sliding-cube modular robot with 2 mm nodes. The cubes use standard-process CMOS IC's, inserted into a cubic space frame and wire-bonded together. Arrays of passivated electrodes, 1 [mu]m from the surface of the cubes, are used for electrostatic surface-drive actuation, zero-power latching, power transfer, localization, and communication. The design allows actuation from any contacting position. Energy is stored in a standard SMT capacitor inside each node, which is recharged by power transfer through chains of contacting nodes.by Ara N. Knaian.S.M

2021-2022, University of Memphis bulletin

University of Memphis bulletin containing the graduate catalog for 2021-2022.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1441/thumbnail.jp

Minimalism + Distribution = Supermodularity

We have designed and implemented multi-agent strategies for manipulation tasks by distributing mechanically-based sequential algorithms across several autonomous spatially-separated agents, such as mobile robots. Our experience using mobile robots for the manipulation of large objects (couches, boxes, file cabinets, etc.) leads us to recommend a minimalist architecture for multi-agent programming. In particular, our methodology has led us to derive asynchronous distributed strategies that require no direct communication between agents, and very sparse geometric and dynamic models of the objects our robots manipulate. We argue for a design principle called supermodularity, which is orthogonal both to the notion of modularity in cognitive AI and also to horizontal decomposition (the non-modularity advocated in the subsumption /connectionist literature.) Finally, we discuss a simple mobot-Scheme infrastructure to implement supermodular architectures. In the past few years we have programm..