IFDS

IFDS Monthly All-Hands: Rebecca Willett
2022-02-04 12:30 - 13:30 (America/Los_Angeles)
Speaker: Prof. Rebecca Willett, Statistics and CS, University of Chicago
Title: The Role of Linear Layers in Nonlinear Interpolating Networks
Abstract: In this discussion, we will explore the implicit bias of overparameterized neural networks of depth greater than two layers. Our framework considers a family of networks of varying depth that all have the same capacity but different implicitly defined representation costs. The representation cost of a function induced by a neural network architecture is the minimum sum of squared weights needed for the network to represent the function; it reflects the function space bias associated with the architecture. Our results show that adding linear layers to a ReLU network yields a representation cost that reflects a complex interplay between the alignment and sparsity of ReLU units. Specifically, using a neural network to fit training data with minimum representation cost yields an interpolating function that is constant in directions perpendicular to a low-dimensional subspace on which a parsimonious interpolant exists. This is joint work with Greg Ongie.

SILO: Kassem Fawaz
2022-02-02 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

IFDS Ideas Forum
2022-01-31 14:00 (America/Chicago)
Lightning talks for ICML+COLT submissions

IFDS Ideas Forum
2022-01-31 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

SILO: Csaba Szepesvari
2022-01-26 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

ML Opt@ UW: Lang Liu
2022-01-21 12:30 - 13:30 (America/Chicago)
Speaker: Lang Liu
Title: The Sample Complexity of Statistical Comparison Between Generative Models
Abstract: The spectacular success of deep generative models calls for quantitative tools to measure their statistical performance. Divergence frontiers have recently been proposed as an evaluation framework for generative models. Although practically successful, the sample complexity of the empirical estimator of divergence frontiers is unknown. We establish non-asymptotic bounds on the sample complexity of divergence frontiers, providing theoretical guidance on their estimation procedure.

E & A SIG:
2022-01-19 12:00 - 13:00, 2021-12-15 12:00 - 13:00, 2021-11-17 12:00 - 13:00 (America/Los_Angeles)

ML Opt@ UW: Yue Sun
2022-01-14 12:30 - 13:30 (America/Los_Angeles)
Speaker: Yue Sun
Title: Analysis of Policy Gradient Descent for Control: Global Optimality via Convex Parameterization
Abstract: Policy gradient descent is a popular approach in reinforcement learning due to its simplicity. Recent work has investigated the optimality and convergence properties of this method when applied in certain control problems. In this work, we connect policy gradient descent (applied to a nonconvex problem formulation) with classical convex parameterizations in control theory, to show the gradient dominance property for the nonconvex cost function. Such a connection between nonconvex and convex landscapes holds for continuous/discrete time LQR, distributed optimal control, minimizing the $cL_2$ gain, among others. To the best of our knowledge, this work offers the first result unifying the landscape analysis of a broad class of control problems.

IFDS All-Hands: Sébastien Roche
2022-01-07 15:30 - 16:30 (America/Chicago) | Zoom
Title: Phylogenomics: “Inverting” Random Trees

Speaker: Sebastien Roch, UW-Madison, Department of Mathematics

Abstract: The estimation of species phylogenies from genome-scale data is an important step in modern evolutionary studies. This estimation is complicated by the fact that genes evolve under biological processes that produce discordant trees. Such processes include horizontal gene transfer, incomplete lineage sorting, and gene duplication and loss, all of which can be modeled using certain random tree distributions. I will discuss recent results on the identifiability, or “invertibility”, of these probabilistic models. I will also consider the large-sample properties of species tree estimation methods in this context. Based partly on joint works with Max Bacharach, Brandon Legried, Erin Molloy, Elchanan Mossel, Allan Sly, Tandy Warnow, Shuqi Yu.

Bio: Sebastien Roch is a Professor in the Department of Mathematics at University of Wisconsin-Madison, where he is also affiliated with the Department of Statistics and the Theory of Computing. He earned his Ph.D. in Statistics from the University of California, Berkeley under the guidance of Elchanan Mossel. From 2007-2009, he was a Postdoctoral Researcher at Microsoft Research. From 2009-2012, he was a tenure-track Assistant Professor in the Department of Mathematics at the University of California-Los Angeles. He is the recipient of an NSF CAREER Award and of an Alfred P. Sloan Fellowship. He was a Kavli Fellow of the National Academy of Sciences in 2014 and 2017, and was a 2018 Simons Fellow. He also received the Best Paper Award at RECOMB 2018. His research interests lie at the interface of applied probability, statistics, and theoretical computer science with an emphasis on biological applications.

IFDS Monthly All-Hands: Sebastien Roch
2022-01-07 12:30 - 13:30 (America/Los_Angeles)
Speaker: Prof. Sebastien Roch, UW-Madison, Department of Mathematics
Title: Phylogenomics: “Inverting” Random Trees
Abstract: The estimation of species phylogenies from genome-scale data is an important step in modern evolutionary studies. This estimation is complicated by the fact that genes evolve under biological processes that produce discordant trees. Such processes include horizontal gene transfer, incomplete lineage sorting, and gene duplication and loss, all of which can be modeled using certain random tree distributions. I will discuss recent results on the identifiability, or “invertibility”, of these probabilistic models. I will also consider the large-sample properties of species tree estimation methods in this context. Based partly on joint works with Max Bacharach, Brandon Legried, Erin Molloy, Elchanan Mossel, Allan Sly, Tandy Warnow, Shuqi Yu.

SILO-TBA
2021-12-15 12:30 (America/Chicago)

IFDS Ideas Forum-Max Bacharach
2021-12-13 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.

SILO-Jose Miguel Ponciano
2021-12-08 12:30 (America/Chicago)

IFDS Ideas Forum-Julian Katz-Samuels
2021-12-06 12:30 - 13:00 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

IFDS All-Hands: Marcella Gomez, UCSC Applied Math
2021-12-03 13:30 - 14:30 (America/Los_Angeles)
Title: Open problems in mathematical modeling and control of wound healing
Abstract: Wound healing consists of a series of overlapping biological process with sophisticated coordination to promote wound closure. Our research work currently focuses on the task of accelerating wound healing by targeting and artificially manipulating key biological processes. In particular, we aim to develop models and control architectures for real-time feedback control for accelerated wound closure. In this talk, I discuss the progress and challenges of working with a complex biological process with limited data, as well as, developing feedback control algorithms for systems interfaced with a bioelectronic device and limited observable states.
Bio: Marcella M. Gomez is an associate professor at UC Santa Cruz in the department of Applied Mathematics. She received her PhD from Caltech in 2015 and a B.S. from UC Berkeley in 2009; both degrees in Mechanical Engineering. Her research interests are in synthetic and systems biology. In particular, she is interested in developing data-driven methods and foundations for modeling and control of complex biological systems.
Zoom link via email or contact organizer.

SILO-Jerry Zhu
2021-12-01 12:30 (America/Chicago)

IFDS Ideas Forum-Sijia Fang & Yuetian Luo
2021-11-29 12:30 - 13:00 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

SILO-Yudong Chen
2021-11-24 12:30 (America/Chicago)

IFDS Ideas Forum-Yuchen Zeng & Liu Yang
2021-11-22 12:30 - 13:00 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

SILO-Keith Levin
2021-11-17 12:30 (America/Chicago)

IFDS Ideas Forum-Jeffrey Covington
2021-11-15 12:30 - 13:00 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

SILO-Qiaomin Xie
2021-11-10 12:30 (America/Chicago)

IFDS Ideas Forum-Sushrut Karmalkar
2021-11-08 12:30 - 13:00 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

IFDS All-Hands: Kevin Jamieson
2021-11-05 13:30 - 14:30 (America/Los_Angeles)
Title: Instance Dependent Sample Complexity Bounds for Interactive Learning

Abstract: The sample complexity of an interactive learning problem, such as multi-armed bandits or reinforcement learning, is the number of interactions with nature required to output an answer (e.g., a recommended arm or policy) that is approximately close to optimal with high probability. While minimax guarantees can be useful rules of thumb to gauge the difficulty of a problem class, algorithms optimized for this worst-case metric often fail to adapt to “easy” instances where fewer samples suffice. In this talk, I will highlight some of my group’s work on algorithms that obtain optimal, finite time, instance dependent sample complexities that scale with the true difficulty of the particular instance, versus just the worst-case. In particular, I will describe a unifying experimental design based approach used to obtain such algorithms for best-arm identification for linear bandits, contextual bandits with arbitrary policy classes, and smooth losses for linear dynamical systems.

Kevin’s website: https://homes.cs.washington.edu/~jamieson/about.html

Zoom link: contact organizer

(The talk will not be recorded, we hope you can join us live!)

SILO-Sebastien Roch
2021-11-03 12:30 (America/Chicago)

IFDS Ideas Forum-Hanbaek Lyu
2021-11-01 12:30 - 13:00 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

ML-Opt@UW Stephen Mussmann
2021-10-29 13:30 - 14:30 (America/Los_Angeles)
Understanding and analyzing the effectiveness of uncertainty sampling

Active learning techniques attempt to reduce the amount of data required to learn a classifier by leveraging adaptivity. In particular, an algorithm iteratively selects and labels points from an unlabeled pool of data points. Over the history of active learning, many algorithms have been developed, though one heuristic algorithm, uncertainty sampling, stands out by its popularity, effectiveness, simplicity, and intuitiveness. Despite this, uncertainty sampling has known failure modes and lacks the theoretical underpinnings of some other algorithms such as those based on disagreement. Here, we present a few analyses of uncertainty sampling. First, we find that uncertainty sampling iterations implicitly optimizes the (generally non-convex) zero-one loss, explaining how uncertainty sampling can achieve lower error than labeling the entire unlabeled pool and highlighting the importance of a good initialization. Second, for logistic regression, we show that the extent to which uncertainty sampling outperforms random sampling is inversely proportional to the asymptotic error, both theoretically and empirically. Finally, we use the previous insights to show uncertainty sampling works very well on a particular NLP task due to extreme label imbalance. Taken together, these results provide a sturdier foundation for understanding and using uncertainty sampling.

https://washington.zoom.us/j/99919016373?pwd=[REDACTED]

SILO-Hanie Sedghi
2021-10-27 12:30 (America/Chicago)
