The AI seminar will meet weekly for lectures by graduate students,
faculty, and researchers emphasizing work-in-progress and recent
results in AI research. Lunch will be served starting at noon, with
the talks running between 12:15 and 1:15. The new format is designed
to allow AI chit-chat before the talks begin. Also, we're trying to
make some of the presentations less formal so that students and
faculty will feel comfortable using the seminar to give presentations
about work in progress or practice talks for conferences.
Art Munson, Cornell University
Abstract: We examine the mechanism by which feature selection improves the accuracy of supervised learning. An empirical bias/variance analysis as feature selection progresses indicates that the most accurate feature set corresponds to the best bias-variance trade-off point for the learning algorithm. Often, this is *not* the point separating relevant from irrelevant features, but where increasing variance outweighs the gains from adding more (weakly) relevant features. In other words, feature selection can be viewed as a variance reduction method that trades off the benefits of decreased variance (from the reduction in dimensionality) with the harm of increased bias (from eliminating some of the relevant features). If a variance reduction method like bagging is used, more (weakly) relevant features can be exploited and the most accurate feature set is usually larger. In many cases, the best performance is obtained by using all available features.
This is a joint work with Rich Caruana.
Haym Hirsh, NSF and Rutgers
Ping Li, Cornell
Abstract: Friedman's MART (multiple additive regression trees) algorithm is widely used in industry for classification and ranking (e.g., search engines). This talk mainly presents ABC-LogitBoost, which considerably outperforms ABC-MART (Ping Li, 2009), MART (Friedman, 2001), and LogitBoost (Friedman et al, 2000) for multi-class classification, on a wide variety of large-scale datasets. The talk will also present Robust LogitBoost, which fixed a known numerical problem in the original LogitBoost and outperformed MART and other classification algorithms for binary classification on many datasets. ABC-Boost stands for the general framework of "adaptive base class boost" and both ABC-LogitBoost and ABC-MART are specific implementations of ABC-Boost.
Carla Brodley, Tufts
Host: Claire Cardie
No AI-Seminar at the regular time, but talk in the afternoon at MLDG.
Ashutosh Saxena, Cornell
Robot Learning: Single Image Depth Perception and Robotic Grasping
|October 9||* No Seminar *|
Gaurav Pandey, University of Minnesota, Twin Cities
Host: Ashutosh Saxena
Data Mining Techniques for Enhancing Protein Function Prediction
Andy Ruina, Cornell
Host: Ashutosh Saxena
Regina Barzilay, MIT
Host: Lillian Lee
Learning to follow orders: Reinforcement learning for mapping instructions to actions
In this talk, I will address the problem of relating linguistic
analysis and control --- specifically, mapping natural language
instructions to executable actions. This technique has enabled
automation of tasks that until now have required human
participation --- for example, automatically configuring
software by consulting how-to guides. Our results demonstrate
that this method can rival supervised techniques while requiring
few or no annotated training examples.
Hadas Kress-Gazit, Cornell University
Host: Ashutosh Saxena
Abstract: High-level tasks to correct, low-level robot control
Robots today can mop the floor, assist surgeons and explore space; however, there is no robot that could be trusted to drive autonomously in a real city. Robots either perform simple or hard-coded tasks fully autonomously or they operate with close human supervision. While most of the sensing and actuation technology required for high-level operation exists, what is lacking is the ability to plan at a high-level while providing guarantees for safety and correctness of a robot's autonomous behavior.
In this talk I will present a formal approach to creating robot controllers that ensure the robot satisfies a given high level task. I will describe a framework in which a user specifies a complex and reactive task in Structured English. This task is then automatically translated, using temporal logic and tools from the formal methods world, into a hybrid controller. This controller is guaranteed to control the robot such that its motion and actions satisfy the intended task, in a variety of different environments.
Theodoros Damoulas, Cornell University
Host: Thorsten Joachims
Probabilistic Multiple Kernel Learning
The integration of multiple and possibly heterogeneous information sources for an overall decision-making process has been an open and unresolved research direction in computing science since its very beginning. This talk will summarize research that addresses parts of this direction by proposing probabilistic data integration algorithms for multiclass decisions, where an observation of interest is assigned to one of many categories based on a plurality of information channels.
The adopted Bayesian probabilistic framework is motivated by the requirements for assessing decision-making costs, formal inclusion of prior knowledge and principled model selection. Requirements that are common across many fields, such as bioinformatics and robotics, where multiple sources of information are available for a multiclass classification decision.
|November 20||Rob Fergus, NYU
Host: Dan Huttenlocher
****** TIME CHANGE: 1:00PM - 2:15PM
****** ROOM CHANGE: 315 Upson Hall
****ALADDINS Serving lunch
Title: Semi-Supervised Learning in Gigantic Image Collections.
|November 27||*No Seminar*|
|December 4||*No Seminar*|
See also the AI graduate study brochure.
Please contact any of the faculty below if you'd like to give a talk this semester. We especially encourage graduate students to sign up!
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