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The RAIN seminar is held on Wednesdays between 12-1pm PT this year. You can subscribe to the seminar mailing list by visiting here.

RAIN schedule for Fall 2022

Date Speaker Topic Comment
Oct. 12
Diyi Yang More Civility and Positivity for Socially Responsible Language Understanding
Oct. 26
Jamie Morgenstern Shifts in Distributions and Preferences in Response to Learning
Nov. 9
Shahar Dobzinski On the Hardness of Dominant Strategy Mechanism Design
Nov. 16
Nynke Niezink Networks in 3D: Inference for Three-way Social Networks
Nov. 30
Michael Jordan On Dynamics-Informed, Learning-Aware Mechanism Design
Dec. 7
Thodoris Lykouris Decentralized multi-agent learning in queuing systems

Google Calendar for RAIN

Previous year's talks

Archived talks can be accessed here.

Talk Abstracts

More Civility and Positivity for Socially Responsible Language Understanding
Diyi Yang

Abstract: Natural language processing (NLP) has had increasing success and produced extensive industrial applications. Despite being sufficient to enable these applications, current NLP systems often ignore the social part of language, e.g., who says it, in what context, for what goals, which severely limits the functionality of these applications and the growth of the field. Our research focuses on the social part of language, towards building more socially responsible language technologies. In this talk, I will take a closer look at social factors in language and share two recent works for promoting more civility and positivity in language use. The first one studies hate speech by introducing a benchmark corpus on implicit hate speech and computational models to detect and explain latent hatred in language. The second examines positive reframing by neutralizing a negative point of view and generating a more positive perspective without contradicting the original meaning. Joint work with Alex Teytelboym
Bio: Diyi Yang is an assistant professor in the Computer Science Department at Stanford University. Her research interests are computational social science and natural language processing. Her research goal is to understand the social aspects of language and to build socially aware NLP systems to better support human-human and human-computer interaction. Her work has received multiple paper awards or nominations at ACL, ICWSM, EMNLP, SIGCHI, and CSCW. She is a recipient of Forbes 30 under 30 in Science (2020), IEEE “AI 10 to Watch” (2020), the Intel Rising Star Faculty Award (2021), Microsoft Research Faculty Fellowship (2021), and NSF CAREER Award (2022).

Shifts in Distributions and Preferences in Response to Learning
Jamie Morgenstern

Abstract: Prediction systems face exogenous and endogenous distribution shift -- the world constantly changes, and the predictions the system makes change the environment in which it operates. For example, a music recommender observes exogeneous changes in the user distribution as different communities have increased access to high speed internet. If users under the age of 18 enjoy their recommendations, the proportion of the user base comprised of those under 18 may endogeneously increase. Most of the study of endogenous shifts has focused on the single decision-maker setting, where there is one learner that users either choose to use or not. In this talk, I'll describe several settings where user preferences may cause changes in distributions over the life of an ML system, and how these changes will affect the long-term performance of such systems. Joint work with Sarah Dean, Mihaela Curmei, Maryam Fazhel and Lillian Ratliff.
Bio: Jamie Morgenstern is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She was previously an assistant professor in the School of Computer Science at Georgia Tech. Prior to starting as faculty, she was fortunate to be hosted by Michael Kearns, Aaron Roth, and Rakesh Vohra as a Warren Center fellow at the University of Pennsylvania. She completed her PhD working with Avrim Blum at Carnegie Mellon University. She studies the social impact of machine learning and the impact of social behavior on ML's guarantees. How should machine learning be made robust to behavior of the people generating training or test data for it? How should ensure that the models we design do not exacerbate inequalities already present in society?

On the Hardness of Dominant Strategy Mechanism Design
Shahar Dobinski

Abstract: We study the communication complexity of dominant strategy implementations of combinatorial auctions. We start with two domains that are generally considered “easy”: multi-unit auctions with decreasing marginal values and combinatorial auctions with gross substitutes valuations. For both domains we have fast algorithms that find the welfare-maximizing allocation with communication complexity that is poly-logarithmic in the input size. This immediately implies that welfare maximization can be achieved in an ex-post equilibrium with no significant communication cost, by using VCG payments. In contrast, we show that in both domains the communication complexity of any dominant strategy implementation that achieves the optimal welfare is polynomial in the input size. We then study the approximation ratios achievable by dominant strategy mechanisms. For combinatorial auctions with general valuations, we show that no dominant-strategy mechanism achieves an approximation ratio of m^{1−\eps}, where m is the number of items. In contrast, a randomized dominant strategy mechanism that achieves an O(\sqrt m) approximation. This proves the first gap between computationally efficient deterministic dominant strategy mechanisms and randomized ones. Joint work with Shiri Ron and Jan Vondrak.
Bio: Shahar Dobzinski is a faculty member of Weizmann's applied math and computer science department. His general research area is algorithmic game theory, an area on the intersection of computer science, game theory, and economics. Specifically, he usually studies the theory of computer science aspects of problems in algorithmic mechanism design. He is also interested in other related areas of theoretical computer science, such as approximation algorithms.

Networks in 3D: Inference for Three-way Social Networks
Nynke Niezink

Abstract: Most statistical methods for social network analysis are developed for social structures that consist of directed or undirected ties between two actors. Yet, in many social contexts, relations inherently involve three actors. For example, different people are known to perceive and cognitively represent the networks they are embedded in differently. Understanding differences in perceptions, and how perceptions drive behavior, requires an explicit model of network perception (sender-receiver-peceiver) data. Gossip networks too require a three-way network perspective. In this talk, I will introduce statistical models and inference for static and dynamic three-way network analysis based on incomplete observations.
Bio: Nynke Niezink is an assistant professor in the Department of Statistics and Data Science at Carnegie Mellon University. Her research focuses on developing statistical methodology and software for the social sciences, with an emphasis on social network analysis. Much of her work is inspired by interdisciplinary collaborations. She received her PhD in Sociology, her MSc’s in Applied Mathematics and Social and Behavioral Sciences, and her BSc’s in Mathematics and Pedagogical and Educational Sciences from the University of Groningen. Her work has been supported by the NSF, NIH, and the Russell Sage and Richard King Mellon Foundation. She was granted a Provost Inclusive Teaching Fellowship for her project targeting diversity, equity, and inclusion in Statistics education at CMU.

Decentralized multi-agent learning in queuing systems
Thodoris Lykouris

Abstract: Learning in multi-agent systems often poses significant challenges due to interference between agents. In particular, unlike classical stochastic systems, the performance of an agent's action is not drawn i.i.d. from some distribution but is directly affected by the (unobserved) actions of the other agents. This is the reason why most collaborative multi-agent learning approaches aim to globally coordinate all agents' actions to evade this interference. In this talk, we focus on bipartite queuing networks, a common model for two-sided platforms, where N agents request service from K servers. Prior decentralized multi-agent learning approaches have the aforementioned "global coordination" flavor and therefore suffer from significant shortcomings: they are restricted to symmetric systems, have performance that degrades exponentially in the number of servers, require communication through shared randomness and unique identifiers, and are computationally demanding. In contrast, we provide a simple learning algorithm that, when run decentrally by each agent, avoids the shortcomings of "global coordination" and leads to efficient performance in general asymmetric bipartite queuing networks while also having additional robustness properties. Along the way, we provide the first UCB-based algorithm for the centralized case of the problem, which resolves an open question by Krishnasamy, Sen, Johari, and Shakkottai (NeurIPS'16 / OR'21). The paper on which this talk is based is joint work with Daniel Freund and Wentao Weng and can be found here: A preliminary version appeared at COLT'22 and Wentao was selected as a finalist in the Applied Probability Society student paper competition for this work.
Bio: Thodoris Lykouris is an assistant professor at the MIT Sloan School of Management, affiliated with the Operations Management group and the Operations Research Center. His research focuses on data-driven sequential decision-making and spans across the areas of machine learning, dynamic optimization, and economics. Before joining MIT, he received his Ph.D. from Cornell University and spent two years at Microsoft Research NYC as a postdoctoral researcher. He is the recipient of a Google Ph.D. Fellowship and a Cornell University Fellowship and was selected as a finalist in the Dantzig Dissertation award as well as the George Nicholson and Applied Probability Society student paper competitions.