Events

Thursday, April 11, 2019 — 10:30 AM EDT

Svetlana Obraztsova
Nanyang Technological University

Traditionally, computational social choice focuses on evaluating voting rules by their resistance to strategic behaviours, and uses computational complexity as a barrier to them. In contrast, recent works (counting from 2010) take another natural approach and analyse voting scenarios from a game-theoretic perspective, viewing strategic parties as players and examining possible stable outcomes of their interaction (i.e., equilibria). The main problem of this approach is multiple unrealistic Nash equilibria. Fortunately, several refinements have been developed that allow to filter out some undesirable Nash Equilibria. 

Friday, April 5, 2019 — 10:00 AM EDT

Wei Yang, Master’s candidate
David R. Cheriton School of Computer Science

In recent years, we have witnessed many successes of neural networks in the information retrieval community with lots of labeled data. Yet it remains unknown whether the same techniques can be easily adapted to search social media posts where the text is much shorter. In addition, we find that most neural information retrieval models are compared against weak baselines. 

In this thesis, we build an end-to-end neural information retrieval system using two toolkits: Anserini and MatchZoo. In addition, we also propose a novel neural model to capture the relevance of short and varied tweet text, named MP-HCNN. 

Wednesday, March 27, 2019 — 4:30 PM EDT

Shrinu Kushagra, PhD candidate
David R. Cheriton School of Computer Science

Data de-duplication is the task of detecting multiple records that correspond to the same real-world entity in a database. In this work, we view de-duplication as a clustering problem. We introduce a framework which we call promise correlation clustering. Given a complete graph \(G\) with the edges labeled \(0\) and \(1\), the goal is to find a clustering that minimizes the number of \(0\) edges within a cluster plus the number of \(1\) edges across different clusters (or correlation loss). The optimal clustering can also be viewed as a complete graph \(G^*\) with edges corresponding to points in the same cluster being labeled \(0\) and other edges being labeled \(1\).

Thursday, February 28, 2019 — 10:30 AM EST

Aishwarya Agrawal, PhD candidate
School of Interactive Computing, Georgia Tech

In this talk, I will present our work on a multi-modal AI task called Visual Question Answering (VQA) — given an image and a natural language question about the image (e.g., “What kind of store is this?”, “Is it safe to cross the street?”), the machine’s task is to automatically produce an accurate natural language answer (“bakery”, “yes”).

Monday, February 25, 2019 — 10:30 AM EST

Yuxin Chen, Postdoctoral scholar, Department of Computing and Mathematical Sciences
California Institute of Technology

How can we intelligently acquire information for decision making, when facing a large volume of data? 

In this talk, I will focus on learning and decision making problems that arise in robotics, scientific discovery and human-centered systems, and present how we can develop principled approaches that actively extract information, identify the most relevant data for the learning tasks and make effective decisions under uncertainty.

Tuesday, February 19, 2019 — 3:00 PM EST

Nabiha Asghar, PhD candidate
David R. Cheriton School of Computer Science

Most of the existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content. We take a step in this direction by proposing three novel ways to incorporate affective/emotional aspects into long short term memory (LSTM) encoder-decoder neural conversation models: (1) affective word embeddings, which are cognitively engineered, (2) affect-based objective functions that augment the standard cross-entropy loss, and (3) affectively diverse beam search for decoding.

Thursday, February 14, 2019 — 11:00 AM EST

Adam Schunk, Master’s candidate
David R. Cheriton School of Computer Science

Over the past years online social networks have become a major target for marketing strategies, generating a need for methods to efficiently spread information through these networks. Close-knit communities have developed on these platforms through groups of users connecting with likeminded individuals. 

Thursday, February 14, 2019 — 10:30 AM EST

Andrew Delong, Head of Computational Research
Deep Genomics

Genomics focuses on the sequences in our genomes and how they encode for function in our cells. Predicting how sequences will be interpreted by the cell is important for identifying disease-causing mutations and for designing therapies. 

Thursday, January 24, 2019 — 4:00 PM EST

Brandon Alcox, Master’s candidate
David R. Cheriton School of Computer Science

This thesis investigates the application of various fields of artificial intelligence to the domain of sports management and analysis. The research in this thesis is primarily focused on the entry draft for the National Hockey League, though many of the models proposed may be applied to other sports and leagues with minimal adjustments. 

Thursday, January 24, 2019 — 10:30 AM EST

Eunsol Choi, Paul G. Allen School of Computer Science
University of Washington

Real world entities such as people, organizations and countries play a critical role in text. Reading offers rich explicit and implicit information about these entities, such as the categories they belong to, relationships they have with other entities, and events they participate in. 

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