IFDS

IFDS Ideas Forum: Zhiyan Ding
2021-04-19 13:30 - 14:30 (America/Chicago)
Person: Zhiyan Ding
Title: Connection between optimization and sampling
Abstract:
Bio: I am a third-year PhD student in the Department of Mathematics, University of Wisconsin-Madison. My advisor is Qin Li.

SILO: Emmanuel Abbe
2021-04-21 12:30 - 13:30 (America/Chicago)
Title: TBD
UNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo

ML-Opt@UW: Yue Sun
2021-04-23 13:30 - 14:30 (America/Los_Angeles)
Title: Subspace Based Meta-learning
Abstract:
Meta-learning typically involves two phases. First, one learns a suitable representation from the previously seen tasks. Secondly, this representation is used for learning a new task using only a few samples (i.e., few-shot learning). In this talk I will discuss:
1. Linear meta learning: sample complexity of representation learning with general covariance
2. Linear meta learning: algorithm & analysis for overparameterized few-shot learning
3. Generalization to nonlinear meta-learning

Bio:
Yue Sun is a 5th year PhD student from University of Washington, Seattle. He is interested in theoretical understanding of optimization, ML and control. His research works are about:
1. Nonconvex optimization on Riemannian manifolds (UW)
2. Low order linear system identification (UW)
3. Subspace based meta-learning (UW)
4. Nonconvex optimization applied in optimal control (UW)

5. Online optimization for video coding (Google, 2019)
6. Compressive sensing and phase retrieval (Ohio State U, 2015; Nokia Bell Labs, 2021)

IFDS Ideas Forum: Shi Chen
2021-04-26 13:30 - 14:00 (America/Chicago) | Webex
Title:
Abstract:
Shi Chen (Mathematics), advised by Qin Li (Mathematics) and Stephen J. Wright (Computer Science), works on problems in the interdisciplinary area of applied math and machine learning. He is interested in connecting machine learning to various mathematical physics problems, including the homogenization of PDEs and inverse problems. His recent work focuses on applying deep learning to PDE inverse problems.

IFDS Ideas Forum: Shashank Rajput
2021-04-26 14:00 - 14:30 (America/Chicago) | Webex
Title:
Abstract:
Shashank Rajput (Computer Science), advised by Dimitris Papailiopoulos (Electrical and Computer Engineering), Kangwook Lee (Electrical and Computer Engineering) and Stephen Wright (Computer Science), works on problems in distributed machine learning and optimization. He works on developing techniques which are fast and scalable for practical use, and have theoretical guarantees. Currently, he is working on finding better shuffling mechanisms that beat random reshuffling as well as developing algorithms for training neural networks by only pruning them.

SILO: Martin Wainwright
2021-04-28 12:30 - 13:30 (America/Chicago)

IFDS Ideas Forum: Changhun Jo
2021-05-03 13:30 - 14:00 (America/Chicago) | Webex
Title:
Abstract:
Changhun Jo (Mathematics), advised by Kangwook Lee (Electrical and Computer Engineering) and Sebastien Roch (Mathematics), is working on the theoretical understanding of machine learning. His recent work focuses on finding an optimal data poisoning algorithm against a fairness-aware learner. He also works on finding the fundamental limit on sample complexity of matrix completion in the presence of graph side information.

IFDS Ideas Forum: Shuqi Yu
2021-05-03 14:00 - 14:30 (America/Chicago)
Title: TBD
Abstract:
Shuqi Yu (Mathematics), advised by Sebastien Roch (Mathematics) and working with Karl Rohe (Statistics) on large scale network models. She aims to establish theoretical guarantees for a new estimator of the number of communities in a stochastic blockmodel. She is also interested in phylogenetics questions, in particular, she works on the identifiability of the species phylogeny under an horizontal gene transfer model.

SILO: Yuanzhi Li
2021-05-05 12:30 - 13:30 (America/Chicago)

IFDS All-Hands: Rina Foygel Barber
2021-05-07 13:30 - 14:30 (America/Los_Angeles)
Convergence for nonconvex ADMM, with applications to CT imaging
The alternating direction method of multipliers (ADMM) algorithm is a powerful and flexible tool for complex optimization problems of the form min{f(x)+g(y):Ax+By=c}. ADMM exhibits robust empirical performance across a range of challenging settings including nonsmoothness and nonconvexity of the objective functions f and g, and provides a simple and natural approach to the inverse problem of image reconstruction for computed tomography (CT) imaging. From the theoretical point of view, existing results for convergence in the nonconvex setting generally assume smoothness in at least one of the component functions in the objective. In this work, our new theoretical results provide convergence guarantees under a restricted strong convexity assumption without requiring smoothness or differentiability, while still allowing differentiable terms to be treated approximately if needed. We validate these theoretical results empirically, with a simulated example where both f and g are nondifferentiable (and thus outside the scope of existing theory), as well as a simulated CT image reconstruction problem.

Bio: Rina Foygel Barber is a Louis Block Professor in the Department of Statistics at the University of Chicago. She was a NSF postdoctoral fellow during 2012-13 in the Department of Statistics at Stanford University, supervised by Emmanuel Candès. She received her PhD in Statistics at the University of Chicago in 2012, advised by Mathias Drton and Nati Srebro, and a MS in Mathematics at the University of Chicago in 2009. Prior to graduate school, she was a mathematics teacher at the Park School of Baltimore from 2005 to 2007.

IDFS Ideas Forum: Subjoyoti Mukherjee
2021-05-10 13:30 - 14:30 (America/Los_Angeles)

SILO: Rashmi Vinayak
2021-05-12 12:30 - 13:30 (America/Chicago)
Title: Convertible Codes: Efficient Conversion of Coded Data in Large-scale Storage Systems
Abstract:
In large-scale data storage systems, failures are the norm in day-to-day operations. To protect data in the face of such failures, erasure codes (a tool from coding theory) are employed to store data in a redundant fashion. In this setting, a set of k data blocks to be stored is encoded using an [n, k] code to generate n blocks that are then stored on distinct storage devices. In a recent work, we showed that the failure rate of storage devices vary considerably over time, and that dynamically tuning the parameters n and k of the code provides significant reduction in storage cost. However, traditional codes suffer from prohibitively high resource overheads in changing the code parameters on already encoded data.
Motivated by this application, in this talk, we:
1. Present a new theoretical framework to formalize the notion of “code conversion”—the process of converting data encoded using an [n, k] code into data encoded using a code with different parameters [n’, k’], while maintaining desired decodability properties,
2. Introduce “convertible codes”, a new class of codes that enable resource-efficient conversion,
3. Prove tight bounds on two important metrics for code conversion (a) the number of nodes accessed, and (b) bandwidth consumed,
4. Present practical constructions of convertible codes for a broad range of parameters.
Bio:
Rashmi Vinayak is an assistant professor in the Computer Science department at Carnegie Mellon University. Her research interests broadly lie in computer/networked systems and information/coding theory, and the wide spectrum of intersection between the two areas. Her current focus is on fault tolerance and resource efficiency in data systems. Rashmi is a recipient of NSF CAREER Award, Tata Institute of Fundamental Research Memorial Lecture Award 2020, Facebook Distributed Systems Research Award 2019, Google Faculty Research Award 2018, Facebook Communications and Networking Research Award 2017, UC Berkeley Eli Jury Award 2016 for “outstanding achievement in the area of systems, communications, control, or signal processing”. Her work has received USENIX NSDI 2021 Community (Best Paper) Award, and IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012. Rashmi received her Ph.D. from UC Berkeley in 2016, and was a postdoctoral scholar at UC Berkeley’s AMPLab/RISELab from 2016-17. During her Ph.D. studies, Rashmi was a recipient of Facebook Fellowship 2012-13, the Microsoft Research PhD Fellowship 2013-15, and the Google Anita Borg Memorial Scholarship 2015-16.
Webpage: http://www.cs.cmu.edu/~rvinayak/

UNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo

Data Science Day
2021-05-14 08:45 - 15:00 (America/Chicago)
Uses and Abuses of Data in Higher Education
Virtual Event via Zoom with Opportunities for Engagement
Details and registration: https://citl.ucsc.edu/data-science-day-2021/

SILO: Dimitris Tsipras
2021-05-19 12:30 - 13:30 (America/Chicago)
Title: Robust Machine Learning: The Worst-Case and Beyond
Abstract:
One of the key challenges in the real-world deployment of machine learning models is their brittleness: their performance significantly degrades when exposed to even small variations of their training environments.
How can we build ML models that are more robust?
In this talk, I will present a methodology for training models that are invariant to a broad family of worst-case input perturbations. I will then describe how such robust learning leads to models that learn fundamentally different data representations, and how this can be useful even outside the adversarial context. Finally, I will discuss model robustness beyond the worst-case: ways in which our models fail to generalize and how we can guide further progress on this front.”
Bio:
“Dimitris Tsipras is a PhD student in the MIT EECS Department, advised by Aleksander Mądry. His work revolves around the reliability and robustness of machine learning systems, as well as the science of modern machine learning. He is currently being supported by a Facebook PhD Fellowship
UNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo

ML-Opt: Romain Camilleri and Swati Padmanabhan
2021-06-04 13:30 - 14:30 (America/Los_Angeles)
The final talks of the ML-OPT seminar of the spring quarter will be given Friday (6/4) at 1:30pm PST by Romain Camilleri and Swati Padmanabhan.

Title: High-Dimensional Experimental Design and Kernel Bandits

Abstract: I will talk about high-dimensional bandits. First I want to review how the classical approach to solving linear bandits motivates an experimental design problem. Then I plan to justify why common rounding techniques cannot be applied in a potentially infinite-dimensional space. Lastly, I will show that one can avoid relying on rounding techniques by using a Catoni estimator.

Bio: Romain Camilleri is a 3rd year Ph.D. student at the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he is advised by Kevin Jamieson.

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Title: Computing Lewis Weights to High Precision

Abstract: We present an algorithm for computing high-precision approximate L_p Lewis weights for p > 2. Given an m x n real full-rank matrix A and p>=3, our algorithm computes epsilon-approximate L_p-Lewis weights using O(p^3 log (m p / epsilon)) iterations, where each iteration takes time linear in the sparsity of the input matrix plus the time to compute the leverage scores of a diagonal rescaling of A. Previously, such iteration complexities were known only for 0< p < 4 [CohenPeng2015]. Consequently, our result helps complete the picture on near-optimal reduction from leverage scores to L_p-Lewis weights for all p>0.
[Joint work with Maryam Fazel, Yin Tat Lee, and Aaron Sidford]

Bio: Swati is a graduate student working on problems in convex optimization, advised by Yin Tat Lee.

TRIPODS PI meeting
2021-06-10 - 2021-06-11

IFDS Summer School
2021-07-26 - 2021-07-30

MadLab Workshop
2021-08-02 - 2021-08-04

SILO-Shivani Agrawal
2021-09-08 12:30 (America/Chicago)

IFDS Ideas Forum-Ahmet Alacaoglu
2021-09-13 12:30 - 13:00 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
UNTIL FURTHER NOTICE: Seminars are hybrid in-person and via zoom.

E & A SIG: Krishna Pillutla
2021-09-15 12:00 - 13:00 (America/Los_Angeles)

SILO-Greg Canal
2021-09-15 12:30 (America/Chicago)

IFDS Ideas Forum-Jasper Lee
2021-09-20 12:30 - 13:00 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

SILO-Michael Unser
2021-09-22 12:30 (America/Chicago)
New representer theorems for inverse problems and machine learning

IFDS Ideas Forum-Greg Canal
2021-09-27 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

SILO-Jeff Linderoth
2021-09-29 12:30 (America/Chicago)
Subspace cluster with missing data via integer programming

IFDS Ideas Forum-Chaobing Song
2021-10-04 13:30 - 14:00 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

SILO-TBA
2021-10-06 12:30 (America/Chicago)

IFDS All-Hands: Qin Li
2021-10-08 13:30 - 14:30 (America/Los_Angeles)
Title:
Mean field theory in Inverse Problems from Bayesian inference to overparameterization of networks

Abstract:
Bayesian sampling and neural networks are seemingly two different machine learning areas, but they both deal with many particle systems. In sampling, one evolves a large number of samples (particles) to match a target distribution function, and in optimizing over-parameterized neural networks, one can view neurons particles that feed each other information in the DNN flow. These perspectives allow us to employ mean-field theory, a powerful tool that translates dynamics of many particle system into a partial differential equation (PDE), so rich PDE analysis techniques can be used to understand both the convergence of sampling methods and the zero-loss property of over-parameterization of ResNets. We showcase the use of mean-field theory in these two machine learning areas, and we also invite the audience to brainstorm other possible applications.

IFDS Ideas Forum-Multilingual NLP: Zero-shot Cross-lingual Transfer Learning
2021-10-11 12:30 - 13:00 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
