Speaker: Stefano V. Albrecht (University of Edinburgh)

Title: Deep Reinforcement Learning for Multi-Agent Interaction

Abstract: Our group specialises in developing machine learning algorithms for autonomous systems control, with a particular focus on deep reinforcement learning and multi-agent reinforcement learning. We have a focus on problems of optimal decision making, prediction, and coordination in multi-agent systems. In this talk, I will give an overview of our research agenda along with some recent published papers in these areas, including... Read more about Deep Reinforcement Learning for Multi-Agent Interaction

Speaker: Jessie Finocchiaro (Harvard postdoc)

Talk Title: Loss function design for improved decision-making

Time & Location: 12/1, Friday, 1:30 - 2:30pm, SEC 1.413. 

Abstract: Algorithmic predictions are pervasive in our society, and these predictions are used to make decisions in settings ranging from banking to public health. This talk examines the relationship between the structure of downstream decision tasks and the design of algorithms: in particular, on the design of loss functions in...

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Speaker: Hanna Halaburda (NYU)

Logistics: Friday, 11/17 from 1:30 - 2:30 pm in SEC 1.413

Talk Title: Will Blockchains Disintermediate Platforms? The Problem of Credible Decentralization in Daos

 

Talk Abstract: Blockchain technologies are designed to promote decentralization and self-governance in economic and social settings. In the context of platforms, an early claim of some proponents of these technologies was that blockchains would promote disintermediation, replacing intermediaries with decentralized...

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Speaker: Stephen McAleer (CMU postdoc)

Title: Game-Theoretic Reinforcement Learning

Abstract: Game-theoretic reinforcement learning studies reinforcement learning algorithms that have guarantees of convergence to equilibrium. In this talk I give an overview of game-theoretic reinforcement learning, categorizing it into three main classes: double oracle-based methods, counterfactual regret minimization-based methods, and policy-gradient-based methods. I then introduce state-of-the-art approaches within each algorithmic class, and show how these...

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Speaker: Benjamin Laufer (Cornell Tech)

Talk Title: Fine-tuning Games

Abstract: Major advances in Machine Learning (ML) and Artificial Intelligence (AI) increasingly take the form of developing and releasing general-purpose...

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Speaker: Stephen Bates (MIT)

Title: Incentive-theoretic Hypothesis Testing

Abstract: Contemporary scientific research is a distributed, collaborative endeavor, carried out by teams of researchers, regulatory institutions, funding agencies, commercial partners, and scientific bodies, all interacting with each other and facing different incentives. To maintain scientific rigor, statistical methods should acknowledge this state of affairs. To this end, we study hypothesis testing when there is an...

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Speaker: Hongyao Ma (Columbia Business School)

Time & location: 11:00 - 12:00 on Thursday, Oct 26, at

Title: Iterative Network Pricing for Ridesharing Platforms

 

Abstract: Ridesharing platforms match riders and drivers, using dynamic pricing to balance supply and demand. The origin-based “surge pricing”, however, does not depend on the market condition of trip destinations, leading to inefficient trip flows in space and incentivizes drivers to strategize. In this work, we introduce the Iterative Network...

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Speaker: Kangning Wang (stanford postdoc)

Title: Fair Price Discrimination

Abstract: A seller is pricing identical copies of a good to a stream of unit-demand buyers. Each buyer has a value on the good as his private information. The seller only knows the empirical value distribution of the buyer population and chooses the revenue-optimal price. We consider a widely studied third-degree price discrimination model where an information intermediary with perfect knowledge of the arriving buyer'...

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Speaker: Sruthi Gorantla (IISc Bangalore)

Title: Ex-Post Group Fairness and Individual Fairness in Ranking

Abstract: Fair ranking tasks, which ask to rank a set of items to maximize utility subject to satisfying group-fairness constraints, have gained significant interest in algorithmic fairness, information retrieval, and machine learning literature. Recent works identify uncertainty in the utilities of items as a primary cause of unfairness and propose randomized rankings that achieve ex-ante fairer exposure and better robustness than...

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Speaker: Wei Tang (Columbia)

Title: Learning to Leverage the Information Lever: Online Recommendation and Dynamic Pricing

Abstract:
Modern customers' decisions are intricately shaped not only by their own preferences but also by the associated information with the products being offered. For online marketplaces that aim to maximize the revenue, it is important to learn how to effectively leverage the information as a lever to induce desired customers’ decisions.

In this talk, I will present an algorithmic framework, using a...

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The EconCS Group holds an Economics & Computer Science research seminar each semester. Fall '23 meetings are at 1:30 - 2:30 PM on Fridays in SEC 1.413. Seminar Coordinators are Shirley Zhang and Tao Lin. SEC 1.413 is on ground level at the NW corner of SEC, which is open to the public. The seminar is in-person only. Speaker: Juan Perdomo (Harvard CRCS postdoc)

...

Read more about The Value of Individual Risk Prediction in Wisconsin Public Schools