Past events

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m.

This week's speaker, Qiaomin Xie (Cornell University), will be giving a talk titled "Reinforcement Learning for Complex Environments: Tree Search, Function Approximators and Markov Games.

Abstract

Recent literature has witnessed much progress on the algorithmic and theoretical foundations of Reinforcement Learning (RL), particularly for single-agent problems with small state/action spaces. Our understanding and algorithm toolbox for RL under complex environments, however, remain relatively limited. In this talk, I will discuss some of my work on scalable and probably efficient RL for the challenging settings with large spaces and multiple strategic agents.

First, I will focus on simulation-based methods, as exemplified by Monte-Carlo Tree Search (MCTS). MCTS is a powerful paradigm for online planning that enjoys remarkable empirical success, but lacks theoretical understanding. We provide a complete and rigorous non-asymptotic analysis of MCTS. Our analysis develops a general framework based on a hierarchy of bandits, and highlights the importance of using a non-standard confidence bound (also used by AlphaGo) for convergence. I will further discuss combining MCTS with supervised learning and its generalization to continuous action space.

In the second part of the talk, I will discuss on-policy RL for zero-sum Markov games, which generalizes Markov decision processes to multi-agent settings. We consider function approximation to deal with continuous and unbounded state spaces. Based on a fruitful marriage with algorithmic game theory, we develop the first computational efficient algorithm for this setting, with a provable regret bound that is independent of the cardinality and ambient dimension of the state space.  

Biography

Qiaomin Xie is a visiting assistant professor in the School of Operations Research and Information Engineering (ORIE) at Cornell. Prior to that, she was a postdoctoral researcher with LIDS at MIT, and was a research fellow at the Simons Institute during Fall 2016. Qiaomin received her Ph.D. degree in Electrical and Computing Engineering from University of Illinois Urbana Champaign, and her B.E. degree in Electronic Engineering from Tsinghua University. Her research interests lie in the fields of stochastic networks, reinforcement learning, computer and network systems. She is the recipient of Google System Research Award 2020, UIUC CSL PhD Thesis Award 2017 and the best paper award from IFIP Performance Conference 2011.

For this spring 2021 seminar series, GroupLens has invited the author of a recent human-computer interaction paper to come chat about their work.

• This week's paper: Does Transparency in Moderation Really Matter? User Behavior After Content Removal Explanations on Reddit 
• Author: Shagun Jhaver, Post Doctoral scholar,  Allen School of Computer Science & Engineering at the University of Washington

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.
 

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m.

This week's speaker, Biwei Huang (Carnegie Mellon University), will be giving a talk titled "Learning and Using Causal Knowledge: A Further Step Towards Machine Intelligence".

Abstract

Causality has recently attracted much interest across research communities in machine learning, computer science, and statistics. One of the fundamental problems in causality is how to find the underlying causal structure or causal model. One focus of this talk, accordingly, is how to find causal relationships from observational data, known as causal discovery. Specifically, I will show recent methodological developments in causal discovery in the presence of distribution shifts, together with their theoretical guarantees and other related issues in causal discovery in complex environments. Besides learning causality, another problem of interest is how causality is able to help understand and advance machine learning. I will show how a causal perspective benefits domain adaptation and forecasting in nonstationary environments. With causal representations, one can naturally make predictions under active interventions and achieve the goal by changing the system properly. Even without interventions involved, they help characterize how the data distribution changes from passively observed data, so that knowledge can be transferred in an interpretable, principled, and efficient way.

Biography

Biwei Huang is a Ph.D. candidate in the program of Logic, Computation and Methodology at Carnegie Mellon University. Her research interests are mainly in three aspects: (1) automated causal discovery in complex environments with theoretical guarantees, (2) advancing machine learning from the causal perspective, and (3) scientific applications of causal discovery approaches. On the causality side, her research has delivered more reliable and practical causal discovery algorithms by considering the property of distribution shifts and allowing nonlinear relationships, general data distributions, selection bias, and latent confounders. On the machine learning side, her work has shown how the causal view helps in understanding and solving machine learning problems, including classification, clustering, forecasting in nonstationary environments, reinforcement learning, and domain adaptation. Her research contributions have been published in JMLR, ICML, NeurIPS, KDD, AAAI, IJCAI, and UAI. She recently successfully led a NeurIPS’20 workshop on causal discovery and causality-inspired machine learning. She is a recipient of the Presidential Fellowship at CMU and the Apple PhD fellowship in AI/ML.

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m.

This week's speaker, Hao Li (Amazon Web Services AI, Seattle), will be giving a talk titled "Seeking Efficiency and Interpretability in Deep Learning".

Abstract

The empirical success of deep learning in many fields, especially Convolutional Neural Networks (CNNs) for computer vision tasks, is often accompanied with significant computational cost for training, inference and hyperparameter optimization (HPO).  Meanwhile, the mystery of why deep neural networks can be effectively trained with good generalization remains not fully unveiled. With the pervasive application of deep neural networks for critical applications on both cluster and edge devices, there is a surge in demand for efficient, automatic and interpretable model inference, optimization, and adaptation.

In this talk, I will present techniques we developed for reducing neural nets’ inference and training cost, with better understanding about their training dynamics and generalization ability. I will begin by introducing the filter pruning approach for accelerating the inference of CNNs and exploring the possibility of training quantized networks on devices with hardware constraints. Then, I will present how the loss surface of neural networks can be properly visualized with filter-normalized directions, which enables meaningful side-by-side comparisons of generalization ability of neural nets trained in different architectures or hyperparameters. Finally, I will revisit the common practices of HPO for transfer learning tasks. By identifying the correlation among hyperparameters and the connection between task similarity and optimal hyperparameters, the black-box hyperparameter search process can be whitened and expedited.

Biography

Hao Li is an Applied Scientist at Amazon Web Services AI, Seattle, where he researches and develops efficient and automatic machine learning for the cloud-based image and video analysis service - Rekognition. He has contributed to the launch of new vertical services including Custom Labels, Content Moderation and Lookout. Before joining AWS, he received his PhD in Computer Science from University of Maryland, College Park, advised by Prof. Hanan Samet and Prof. Tom Goldstein. His research lies at the intersection of machine learning, computer vision and distributed computing, with a focus on efficient, interpretable and automatic machine learning on platforms ranging from high performance cluster to edge devices. His notable research contribution includes the first filter pruning method for accelerating CNNs/ResNets and the loss surface visualization for understanding the generalization of neural nets.

His work on the trainability of quantized networks received Best Student Paper Award at ICML’17 Workshop on Principled Approaches to Deep Learning.

In honor of Women’s History Month in March, the College of Science and Engineering invites you to a free virtual screening of the critically-acclaimed film “Picture a Scientist” and an optional discussion of the film on Zoom.

All CSE students, faculty, and staff are invited. Register by Monday, March 15.

The 2020 Tribeca Film Festival Official Selection is not currently available for screening to the general public.

Details

“Picture a Scientist” Virtual Film Screening
Sunday, March 21, 5 p.m. to Wednesday, March 24, 5 p.m. Central Time
Vimeo

“Picture a Scientist” Optional Discussion
Thursday, March 25, 2021
3-4:30 p.m. Central Time
Zoom

The day after the screenings conclude, the college will host a virtual discussion of the issues raised in the film.

Register for the film screening and discussion by Monday, March 15.

Registrants will receive the links to the Vimeo screening room and the Zoom discussion section a day or two before the film starts.

About the film

“Picture a Scientist” chronicles the groundswell of researchers who are writing a new chapter for women scientists. Biologist Nancy Hopkins, chemist Raychelle Burks, and geologist Jane Willenbring lead viewers on a journey deep into their own experiences in the sciences, ranging from brutal harassment to years of subtle slights. Along the way, from cramped laboratories to spectacular field stations, we encounter scientific luminaries—including social scientists, neuroscientists, and psychologists—who provide new perspectives on how to make science itself more diverse, equitable, and open to all.

For more information, visit the “Picture a Scientist” website.

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m.

This week's speaker, Tao Yu (Yale University), will be giving a talk titled "Learning to Build Conversational Natural Language Interfaces".

Abstract

Natural language is a fundamental form of information and communication and is becoming the next frontier in computer interfaces. Natural Language Interfaces (NLIs) connect the data and the user, significantly promoting the possibility and efficiency of information access for many users besides data experts. All consumer-facing software will one day have a dialogue interface, the next vital leap in the evolution of search engines. Such intelligent dialogue systems should be able to understand the meaning of language grounded in various contexts and generate effective language responses in different formats for information requests and human-computer communication.

In this talk, I will cover three key developments that present opportunities and challenges for the development of deep learning technologies for conversational natural language interfaces. First, I will discuss the design and curation of large datasets to drive advancements towards neural-based conversational NLIs. Second, I will describe the development of scalable algorithms to parse complex and sequential questions to formal programs (e.g. mapping questions to SQL queries that can execute against databases). Third, I will discuss the general advances of language model pre-training methods to understand the meaning of language grounded in various contexts (e.g. databases and knowledge graphs). Finally, I will conclude my talk by proposing future directions towards human-centered, universal, and trustworthy conversational NLIs.

Biography

Tao Yu is a fourth-year Ph.D. candidate in Computer Science at Yale University. His research aims to build conversational natural language interfaces (NLIs) that can help humans explore and reason over data in any application (e.g., relational databases and mobile apps) in a robust and trusted manner. Tao’s work has been published at top-tier conferences in NLP and Machine Learning (ACL, EMNLP, NAACL, and ICLR). Tao introduced and organized multiple popular shared tasks for building conversational NLIs, which have attracted more than 100 submissions from top research labs and which have become the standard evaluation benchmarks in the field. He designed and developed language models that achieve new state-of-the-art results for seven representative tasks on semantic parsing, dialogue, and question answering. He has worked closely with and mentored over 15 students and collaborated with about 20 researchers from Salesforce Research, Microsoft Research, Columbia University, UC Berkeley, the University of Michigan, and Cornell University. He has been on the program committee for about ten NLP and Machine Learning conferences and workshops, including one of the main organizers of the Workshop of Interactive and Executable Semantic Parsing at EMNLP 2020. For more details, see Tao’s website: https://taoyds.github.io.

For this spring 2021 seminar series, GroupLens has invited the author of a recent human-computer interaction paper to come chat about their work.

• This week's paper: Designing a Chatbot as a Mediator for Promoting Deep Self-Disclosure to a Real Mental Health Professional
• Author: Yi-Chieh Lee, PhD Candidate, University of Illinois Urbana-Champaign

The application deadline for the computer science integrated program (Bachelor's/Master's) is March 15.

This is exclusively available to students officially admitted to the College of Science & Engineering Bachelor’s of Science in Computer Science, Bachelor’s of Computer Engineering, the College of Liberal Arts Bachelor’s of Arts in Computer Science, and the College of Liberal Arts Second Major in Computer Science. The program allows students with strong academic performance records to take additional credits (up to 16 credits) at undergraduate tuition rates during their last few semesters which can be applied towards the Computer Science M.S. program.

Applicants must have at least 75 credits completed at the time of their application. Read more about the program eligibility requirements.

Applications must be submitted online. Before applying, students should review the application procedures.

Students will be notified of the outcome of their application via email by June 1 for a fall start. In some cases, an admission decision will be put on hold until semester grades are finalized. Students will be notified if their application is on hold.

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.