SEC 1.413, & streamed via Zoom at: https://harvard.zoom.us/j/95184948637?pwd=bXBIc2U5MEZ0QmRUb01WQ0o0SXRCdz09
Shiri Ron (Weizmann Institute) will be speaking in-person:
On the Hardness of Dominant Strategy Mechanism Design
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...
The Smoothed and Semi-Random Possibilities of Social Choice
Abstract:
Social choice studies how to aggregate agents' preferences to make a collective decision. It plays a critical role in many group decision making scenarios in human society as well as in multi-agent systems. A prominent challenge in designing desirable social choice mechanisms is the wide presence of worst-case paradoxes and impossibility theorems. While there is a large body of literature on using average-case analysis to...
SEC 1.413, & streamed via Zoom at: https://harvard.zoom.us/j/95184948637?pwd=bXBIc2U5MEZ0QmRUb01WQ0o0SXRCdz09
Yaonan Jin from Columbia university giving an in-person talk. He is a 4th year PhD student in Columbia University advised by Prof Xi Chen and Prof. Rocco Servedio. Here is the talk: Title: "First Price Auction is 1-1/e^2 Efficient https://arxiv.org/abs/2207.01761...
SEC 1.413, and streamed via Zoom at: https://harvard.zoom.us/j/95184948637?pwd=bXBIc2U5MEZ0QmRUb01WQ0o0SXRCdz09
Speaker: Moon Duchin from the MGGG Redistricting Lab at Tufts. She will be speaking in-person on:
Sampling partitions, with applications to redistricting
Abstract: In the world of gerrymandering, there are many reasons to want to take a “representative sample” of the space of balanced graph partitions — that is, a sample pulled from a known distribution which is clearly relevant to the political districting problem. There are numerous Markov...
SEC 1.413, and streamed via Zoom: https://harvard.zoom.us/j/95184948637?pwd=bXBIc2U5MEZ0QmRUb01WQ0o0SXRCdz09
Abstract:
We study the problem of fairly allocating a set of indivisible goods among agents with matroid rank valuations. We present a simple framework that efficiently computes any fairness objective that satisfies some mild assumptions. Along with maximizing a fairness objective, the framework is guaranteed to run in polynomial time, maximize utilitarian social welfare and ensure strategyproofness. Our framework can be used to achieve four different fairness objectives: (a) Prioritized Lorenz dominance, (b) Maxmin fairness, (c) Weighted...
in SEC 1.413, and streamed via Zoom here: https://harvard.zoom.us/j/95184948637?pwd=bXBIc2U5MEZ0QmRUb01WQ0o0SXRCdz09
Gerdus Benade (BU) will be speaking in-person on his paper:
Abstract:
Top-k recommendations are ubiquitous, but are they stable? We study whether, given complete information, buyers and sellers prefer to continue participating in a platform using top-k recommendations rather than pursuing off-platform transactions. When there are no constraints on the number of exposures, top-k recommendations are stable. However, stable k-recommendations may not exist when exposures are constrained, e.g...
SEC 1.413 & on Zoom here: https://harvard.zoom.us/j/97692964231?pwd=K05BMEhDNTZtbUhHYkZ5S21qZ2FKQT09#success
Speaker: Martino Banchio
Title: Learning-Robust Mechanisms and the Coordination Bias
Abstract: We develop a theoretical model to study strategic interactions between adaptive learning algorithms. Applying continuous-time techniques, we uncover the mechanisms responsible for recently documented experimental evidence of such algorithms learning to charge supracompetitive...
We will have our long-awaited student back-to-back talk after two years. It will be in-person and each talk takes 25 minutes including the Q&A. For this week we have:
Speaker: Jessie Finocchiaro, Harvard
Title: Designing convex surrogates for discrete prediction tasks via embeddings
Abstract: We formalize and study the natural approach of designing convex surrogate loss functions via embeddings, for problems such as classification, ranking, or structured prediction. In this...