News & EventsDepartment Events & Announcements
Events
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Apr9
EVENT DETAILS
April 9th and 10th
Description:Real-world networks often exhibit a hidden structure, which we wish to infer. For example, many networks exhibit community structure. Inferring communities is a valuable tool in network analysis; community detection has been used in a wide array of applications including recommender systems (e.g. Netflix), webpage sorting, fraud detection, and neurobiology. Inspired by these real-world networks, researchers in probability, statistics, information theory, and machine learning have studied structure recovery problems in random graph models. In addition to community detection, problems of this type include graph matching, recovery of planted subgraphs, and inference of graph properties. This workshop will bring together leading experts in the field, and both local and external participants, with the goal of sharing the latest advances and launching new collaborations.
Form to register:
https://docs.google.com/forms/d/e/1FAIpQLSfZoMhFJMR00yQDSmYQYg33qQ-lpKtnGBm3jyV3XMXzq7Sgrg/viewform
Speakers:
Elchanan Mossel (MIT)
Tselil Schramm (Stanford University)
Alex Wein (UC Davis)
Jiaming Xu (Duke University)
Logistics:
Date: April 9th -10th
In-person Location: Northwestern University: Mudd Library 3rd floor, 2233 Tech Drive, EvanstonTIME Tuesday, April 9, 2024
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Apr10
EVENT DETAILS
April 9th and 10th
Description:Real-world networks often exhibit a hidden structure, which we wish to infer. For example, many networks exhibit community structure. Inferring communities is a valuable tool in network analysis; community detection has been used in a wide array of applications including recommender systems (e.g. Netflix), webpage sorting, fraud detection, and neurobiology. Inspired by these real-world networks, researchers in probability, statistics, information theory, and machine learning have studied structure recovery problems in random graph models. In addition to community detection, problems of this type include graph matching, recovery of planted subgraphs, and inference of graph properties. This workshop will bring together leading experts in the field, and both local and external participants, with the goal of sharing the latest advances and launching new collaborations.
Form to register:
https://docs.google.com/forms/d/e/1FAIpQLSfZoMhFJMR00yQDSmYQYg33qQ-lpKtnGBm3jyV3XMXzq7Sgrg/viewform
Speakers:
Elchanan Mossel (MIT)
Tselil Schramm (Stanford University)
Alex Wein (UC Davis)
Jiaming Xu (Duke University)
Logistics:
Date: April 9th -10th
In-person Location: Northwestern University: Mudd Library 3rd floor, 2233 Tech Drive, EvanstonTIME Wednesday, April 10, 2024
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Apr15
EVENT DETAILS
In collaboration with the Kellogg Operations Department
Monday / CS Seminar
April 15th / 12:15 PM
In Person / Kellogg Global Hub 1120Speaker
Cynthia Rudin, Duke UniversityTalk Title
Simpler Machine Learning Models for a Complicated WorldAbstract
While the trend in machine learning has tended towards building more complicated (black box) models, such models have not shown any performance advantages for many real-world datasets, and they are more difficult to troubleshoot and use. For these datasets, simpler models (sometimes small enough to fit on an index card) can be just as accurate. However, the design of interpretable models is quite challenging due to the "interaction bottleneck" where domain experts must interact with machine learning algorithms.I will present a new paradigm for interpretable machine learning that solves the interaction bottleneck. In this paradigm, machine learning algorithms are not focused on finding a single optimal model, but instead capture the full collection of good (i.e., low-loss) models, which we call "the Rashomon set." Finding Rashomon sets is extremely computationally difficult, but the benefits are massive. I will present the first algorithm for finding Rashomon sets for a nontrivial function class (sparse decision trees) called TreeFARMS. TreeFARMS, along with its user interface TimberTrek, mitigate the interaction bottleneck for users. TreeFARMS also allows users to incorporate constraints (such as fairness constraints) easily.
I will also present a "path," that is, a mathematical explanation, for the existence of simpler-yet-accurate models and the circumstances under which they arise. In particular, problems where the outcome is uncertain tend to admit large Rashomon sets and simpler models. Hence, the Rashomon set can shed light on the existence of simpler models for many real-world high-stakes decisions. This conclusion has significant policy implications, as it undermines the main reason for using black box models for decisions that deeply affect people's lives.
This is joint work with my colleagues Margo Seltzer and Ron Parr, as well as our exceptional students Chudi Zhong, Lesia Semenova, Jiachang Liu, Rui Xin, Zhi Chen, and Harry Chen. It builds upon the work of many past students and collaborators over the last decade.
Here are papers I will discuss in the talk:
Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo Seltzer, Cynthia Rudin
Exploring the Whole Rashomon Set of Sparse Decision Trees, NeurIPS (oral), 2022.
https://arxiv.org/abs/2209.08040Zijie J. Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, Margo Seltzer
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization, IEEE VIS, 2022.
https://poloclub.github.io/timbertrek/Lesia Semenova, Cynthia Rudin, and Ron Parr
On the Existence of Simpler Machine Learning Models. ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2022.
https://arxiv.org/abs/1908.01755Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin
A Path to Simpler Models Starts With Noise, NeurIPS, 2023.
https://arxiv.org/abs/2310.19726Biography
TBATIME Monday, April 15, 2024 at 12:15 PM - 1:15 PM
LOCATION KGH 1120, Kellogg Global Hub map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Apr25
EVENT DETAILS
TBA
TIME Thursday, April 25, 2024 at 9:00 AM - 11:00 AM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Apr29
EVENT DETAILS
TBA
TIME Monday, April 29, 2024 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May1
EVENT DETAILS
TBA
TIME Wednesday, May 1, 2024 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May30
EVENT DETAILS
TBA
TIME Thursday, May 30, 2024 at 9:00 AM - 11:00 AM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Jun10
EVENT DETAILSmore info
McCormick School of Engineering PhD Hooding and Master’s Degree Recognition Ceremony
TIME Monday, June 10, 2024 at 9:00 AM - 11:00 AM
LOCATION Welsh-Ryan Arena
CONTACT Amy Pokrass amy.pokrass@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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Jun10
TIME Monday, June 10, 2024 at 2:00 PM - 4:00 PM
LOCATION Welsh-Ryan Arena
CONTACT Amy Pokrass amy.pokrass@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science