106 citations · 636 across the 106 of their papers we have counts for
12 papers · 1 filter
Accurate Inference for Adaptive Linear Models
Yash Deshpande, Lester Mackey, Vasilis Syrgkanis +1
Estimators computed from adaptively collected data do not behave like their non-adaptive brethren. Rather, the sequential dependence of the collection policy can lead to severe dis…
Combinatorial Assortment Optimization
Nicole Immorlica, Brendan Lucier, Jieming Mao +2
Assortment optimization refers to the problem of designing a slate of products to offer potential customers, such as stocking the shelves in a convenience store. The price of each…
Learning to Bid Without Knowing your Value
Zhe Feng, Chara Podimata, Vasilis Syrgkanis
We address online learning in complex auction settings, such as sponsored search auctions, where the value of the bidder is unknown to her, evolving in an arbitrary manner and obse…
Optimal Data Acquisition for Statistical Estimation
Yiling Chen, Nicole Immorlica, Brendan Lucier +2
We consider a data analyst's problem of purchasing data from strategic agents to compute an unbiased estimate of a statistic of interest. Agents incur private costs to reveal their…
Orthogonal Machine Learning: Power and Limitations
Lester Mackey, Vasilis Syrgkanis, Ilias Zadik
Double machine learning provides -consistent estimates of parameters of interest even when high-dimensional or nonparametric nuisance parameters are estimated at an $n^{-…
Training GANs with Optimism
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis +1
We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs. Rece…
Inference on Auctions with Weak Assumptions on Information
Vasilis Syrgkanis, Elie Tamer, Juba Ziani
Given a sample of bids from independent auctions, this paper examines the question of inference on auction fundamentals (e.g. valuation distributions, welfare measures) under weak…
Robust Optimization for Non-Convex Objectives
Robert Chen, Brendan Lucier, Yaron Singer +1
We consider robust optimization problems, where the goal is to optimize in the worst case over a class of objective functions. We develop a reduction from robust improper optimizat…
A Proof of Orthogonal Double Machine Learning with -Estimators
Vasilis Syrgkanis
We consider two stage estimation with a non-parametric first stage and a generalized method of moments second stage, in a simpler setting than (Chernozhukov et al. 2016). We give a…
A Sample Complexity Measure with Applications to Learning Optimal Auctions
Vasilis Syrgkanis
We introduce a new sample complexity measure, which we refer to as split-sample growth rate. For any hypothesis and for any sample of size , the split-sample growth rate…
Optimal and Myopic Information Acquisition
Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis
We consider the problem of optimal dynamic information acquisition from many correlated information sources. Each period, the decision-maker jointly takes an action and allocates a…
Simple vs Optimal Mechanisms in Auctions with Convex Payments
Amy Greenwald, Takehiro Oyakawa, Vasilis Syrgkanis
We investigate approximately optimal mechanisms in settings where bidders' utility functions are non-linear; specifically, convex, with respect to payments (such settings arise, fo…