Past events

UMN Machine Learning Seminar

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Thursday from 12 p.m. - 1 p.m. during the Summer 2021 semester.

This week's speaker, Chiyuan Zhang (Google Brain) will be giving a talk titled "Characterizing Structural Regularities of Labeled Data in Overparameterized Models."

Abstract

Humans are accustomed to environments that contain both regularities and exceptions. For example, at most gas stations, one pays prior to pumping, but the occasional rural station does not accept payment in advance. Likewise, deep neural networks can generalize across instances that share common patterns or structures, yet have the capacity to memorize rare or irregular forms. We analyze how individual instances are treated by a model via a consistency score. The score characterizes the expected accuracy for a held-out instance given training sets of varying size sampled from the data distribution. We obtain empirical estimates of this score for individual instances in multiple data sets, and we show that the score identifies out-of-distribution and mislabeled examples at one end of the continuum and strongly regular examples at the other end. We identify computationally inexpensive proxies to the consistency score using statistics collected during training. We show examples of potential applications to the analysis of deep-learning systems.

Biography

Chiyuan Zhang is a research scientist at Google Research, Brain Team. He is interested in analyzing and understanding the foundations behind the effectiveness of deep learning, as well as its connection to the cognition and learning mechanisms of the human brain. Chiyuan Zhang holds a Ph.D. from MIT (2017, advised by Tomaso Poggio), and a Bachelor (2009) and a Master (2012) degrees in computer science from Zhejiang University, China. His work was recognized by INTERSPEECH best student paper award in 2014, and ICLR best paper award in 2017.

Graduate Programs Information Session

Prospective students can RSVP for an information session to learn about the following graduate programs:

  • Computer Science M.S.
  • Computer Science MCS
  • Computer Science Ph.D.
  • Data Science M.S.
  • Data Science Post-Baccalaureate Certificate

During the information session, we will go over the following:

  • Requirements (general)
  • Applying
  • Prerequisite requirements
  • What makes a strong applicant
  • Funding
  • Resources
  • Common questions
  • Questions from attendees

UMN Machine Learning Seminar

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Thursday from 12 p.m. - 1 p.m. during the Summer 2021 semester.

This week's speaker, Xiwei Tang (University of Virginia) will be giving a talk titled "Multivariate Temporal Point Process Regression with Applications in Calcium Imaging Analysis."

Abstract

Point process modeling is gaining increasing attention, as point process type data are emerging in a large variety of scientific applications. In this article, motivated by a neuronal spike trains study, we propose a novel point process regression model, where both the response and the predictor can be a high-dimensional point process. We model the predictor effects through the conditional intensities using a set of basis transferring functions in a convolutional fashion. We organize the corresponding transferring coefficients in the form of a three-way tensor, then impose the low-rank, sparsity, and subgroup structures on this coefficient tensor. These structures help reduce the dimensionality, integrate information across different individual processes, and facilitate the interpretation. We develop a highly scalable optimization algorithm for parameter estimation. We derive the large sample error bound for the recovered coefficient tensor, and establish the subgroup identification consistency, while allowing the dimension of the multivariate point process to diverge. We demonstrate the efficacy of our method through both simulations and a cross-area neuronal spike trains analysis in a sensory cortex study.

Biography

Coming soon

MSSE Online Information Session

Have all your questions about the Master of Science in Software Engineering (MSSE) program answered by attending this online information session.

RSVP now to reserve your spot.

Attendees will be sent a link prior to the event.

UMN Machine Learning Seminar

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Thursday from 12 p.m. - 1 p.m. during the Summer 2021 semester.

This week's speaker, Zhaoran Wang (Northwestern University) will be giving a talk titled "Demystifying (Deep) Reinforcement Learning with Optimism and Pessimism."

Abstract

Coupled with powerful function approximators such as deep neural networks, reinforcement learning (RL) achieves tremendous empirical successes. However, its theoretical understandings lag behind. In particular, it remains unclear how to provably attain the optimal policy with a finite regret or sample complexity. In this talk, we will present the two sides of the same coin, which demonstrates an intriguing duality between optimism and pessimism.

– In the online setting, we aim to learn the optimal policy by actively interacting with the environment. To strike a balance between exploration and exploitation, we propose an optimistic least-squares value iteration algorithm, which achieves a \sqrt{T} regret in the presence of linear, kernel, and neural function approximators.

– In the offline setting, we aim to learn the optimal policy based on a dataset collected a priori. Due to a lack of active interactions with the environment, we suffer from the insufficient coverage of the dataset. To maximally exploit the dataset, we propose a pessimistic least-squares value iteration algorithm, which achieves a minimax-optimal sample complexity.

Biography

Zhaoran Wang is an assistant professor at Northwestern University, working at the interface of machine learning, statistics, and optimization. He is the recipient of the AISTATS (Artificial Intelligence and Statistics Conference) notable paper award, Microsoft Ph.D. Fellowship, Simons-Berkeley/J.P. Morgan AI Research Fellowship, Amazon Machine Learning Research Award, and NSF CAREER Award.

Priority deadline for 2021 Grace Hopper Celebration tickets

Every year, the Department of Computer Science & Engineering has a major presence at the Grace Hopper Celebration (GHC), the world’s largest gathering of women technologists.

The 2021 GHC event will be held from September 27 to October 1. We invite current students to request a department funded ticket to attend this year's event.

Interested students should fill out the interest form by Friday, July 9 at noon. Keep in mind that filling out the form does not guarantee a ticket. Also, please note that students who are provided departmental tickets to the virtual Grace Hopper Celebration 2021 will still have the opportunity to request funding for a future in-person Grace Hopper Celebration.

Departmental staff will contact students who filled out the form about their ticket status sometime after July 9, 2021.

Please feel free to reach out to Allison Small if you have any questions.

MSSE Online Information Session

Have all your questions about the Master of Science in Software Engineering (MSSE) program answered by attending this online information session.

RSVP now to reserve your spot.

Attendees will be sent a link prior to the event.

UMN Machine Learning Seminar

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Thursday from 12 p.m. - 1 p.m. during the Summer 2021 semester.

This week's speaker, Rohan Anil (Google Brain) will be giving a talk titled "Scalable Second-Order Optimization for Deep Learning."

Abstract

Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent. Second-order optimization methods, that involve second derivatives and/or second order statistics of the data, are far less prevalent despite strong theoretical properties, due to their prohibitive computation, memory and communication costs. In an attempt to bridge this gap between theoretical and practical optimization, we present a scalable implementation of a second-order preconditioned method (concretely, a variant of full-matrix Adagrad), that along with several critical algorithmic and numerical improvements, provides significant convergence and wall-clock time improvements compared to conventional first-order methods on state-of-the-art deep models. Our novel design effectively utilizes the prevalent heterogeneous hardware architecture for training deep models, consisting of a multicore CPU coupled with multiple accelerator units. We demonstrate superior performance compared to state-of-the-art on very large learning tasks such as machine translation with Transformers, language modeling with BERT, click-through rate prediction on Criteo, and image classification on ImageNet with ResNet-50.

Biography

Rohan Anil is a Senior Staff Software Engineer, Google Research, Brain Team. Lately, he has been working on scalable and practical optimization techniques for efficient training of neural networks in various regimes.

University closed

The University of Minnesota will be closed in observance of Independence Day.

View the full schedule of University holidays.
 

UMN Machine Learning Seminar

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Thursday from 12 p.m. - 1 p.m. during the Summer 2021 semester.

This week's speaker, Brian Kulis (Boston University) will be giving a talk titled "New Directions in Metric Learning."

Abstract

Metric learning is a supervised machine learning problem concerned with learning a task-specific distance function from supervised data. It has found numerous applications in problems such as similarity search, clustering, and ranking. Much of the foundational work in this area focused on the class of so-called Mahalanobis metrics, which may be viewed as Euclidean distances after linear transformations of the data. This talk will describe two recent directions in metric learning: deep metric learning and divergence learning. The first replaces the linear transformations with the output of a neural network, while the second considers a broader class than Mahalanobis metrics. I will discuss some of my recent work along both of these fronts, as well as ongoing attempts to combine these approaches together using a novel framework called deep divergences.

Biography

Brian Kulis is an associate professor at Boston University, with appointments in the Department of Electrical and Computer Engineering, the Department of Computer Science, the Faculty of Computing and Data Sciences, and the Division of Systems Engineering. He also is an Amazon Scholar, working with the Alexa team. Previously he was the Peter J. Levine Career Development assistant professor at Boston University. Before joining Boston University, he was an assistant professor in Computer Science and in Statistics at Ohio State University, and prior to that was a postdoctoral fellow at UC Berkeley EECS. His research focuses on machine learning, statistics, computer vision, and large-scale optimization. He obtained his PhD in computer science from the University of Texas in 2008, and his BA degree from Cornell University in computer science and mathematics in 2003. For his research, he has won three best paper awards at top-tier conferences---two at the International Conference on Machine Learning (in 2005 and 2007) and one at the IEEE Conference on Computer Vision and Pattern Recognition (in 2008). He is also the recipient of an NSF CAREER Award in 2015, an MCD graduate fellowship from the University of Texas (2003-2007), and an Award of Excellence from the College of Natural Sciences at the University of Texas.