Vasilis Syrgkanis

research

∙ 07/25/2023

Source Condition Double Robust Inference on Functionals of Inverse Problems

We consider estimation of parameters defined as linear functionals of so...

0 Andrew Bennett, et al. ∙

research

∙ 03/08/2023

Inference on Optimal Dynamic Policies via Softmax Approximation

Estimating optimal dynamic policies from offline data is a fundamental p...

0 Qizhao Chen, et al. ∙

research

∙ 02/17/2023

Post-Episodic Reinforcement Learning Inference

We consider estimation and inference with data collected from episodic r...

0 Vasilis Syrgkanis, et al. ∙

research

∙ 02/10/2023

Minimax Instrumental Variable Regression and L_2 Convergence Guarantees without Identification or Closedness

In this paper, we study nonparametric estimation of instrumental variabl...

0 Andrew Bennett, et al. ∙

research

∙ 11/03/2022

Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation

We study the problem of model selection in causal inference, specificall...

0 Divyat Mahajan, et al. ∙

research

∙ 10/20/2022

Synthetic Blip Effects: Generalizing Synthetic Controls for the Dynamic Treatment Regime

We propose a generalization of the synthetic control and synthetic inter...

0 Anish Agarwal, et al. ∙

research

∙ 10/14/2022

Partial Identification of Treatment Effects with Implicit Generative Models

We consider the problem of partial identification, the estimation of bou...

0 Vahid Balazadeh, et al. ∙

research

∙ 06/03/2022

Debiased Machine Learning without Sample-Splitting for Stable Estimators

Estimation and inference on causal parameters is typically reduced to a ...

0 Qizhao Chen, et al. ∙

research

∙ 04/10/2022

Towards efficient representation identification in supervised learning

Humans have a remarkable ability to disentangle complex sensory inputs (...

0 Kartik Ahuja, et al. ∙

research

∙ 12/26/2021

Omitted Variable Bias in Machine Learned Causal Models

We derive general, yet simple, sharp bounds on the size of the omitted v...

4 Victor Chernozhukov, et al. ∙

research

∙ 10/06/2021

Robust Generalized Method of Moments: A Finite Sample Viewpoint

For many inference problems in statistics and econometrics, the unknown ...

0 Dhruv Rohatgi, et al. ∙

research

∙ 10/06/2021

RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests

Many causal and policy effects of interest are defined by linear functio...

9 Victor Chernozhukov, et al. ∙

research

∙ 07/21/2021

Incentivizing Compliance with Algorithmic Instruments

Randomized experiments can be susceptible to selection bias due to poten...

0 Daniel Ngo, et al. ∙

research

∙ 04/30/2021

Automatic Debiased Machine Learning via Neural Nets for Generalized Linear Regression

We give debiased machine learners of parameters of interest that depend ...

0 Victor Chernozhukov, et al. ∙

research

∙ 04/20/2021

Knowledge Distillation as Semiparametric Inference

A popular approach to model compression is to train an inexpensive stude...

0 Tri Dao, et al. ∙

research

∙ 03/15/2021

Estimating the Long-Term Effects of Novel Treatments

Policy makers typically face the problem of wanting to estimate the long...

0 Keith Battocchi, et al. ∙

research

∙ 03/12/2021

Evidence-Based Policy Learning

The past years have seen seen the development and deployment of machine-...

0 Jann Spiess, et al. ∙

research

∙ 12/30/2020

Adversarial Estimation of Riesz Representers

We provide an adversarial approach to estimating Riesz representers of l...

9 Victor Chernozhukov, et al. ∙

research

∙ 11/23/2020

Asymptotics of the Empirical Bootstrap Method Beyond Asymptotic Normality

One of the most commonly used methods for forming confidence intervals f...

0 Morgane Austern, et al. ∙

research

∙ 07/26/2020

Bid Prediction in Repeated Auctions with Learning

We consider the problem of bid prediction in repeated auctions and evalu...

0 Gali Noti, et al. ∙

research

∙ 06/12/2020

Minimax Estimation of Conditional Moment Models

We develop an approach for estimating models described via conditional m...

0 Nishanth Dikkala, et al. ∙

research

∙ 02/17/2020

Double/Debiased Machine Learning for Dynamic Treatment Effects

We consider the estimation of treatment effects in settings when multipl...

0 Greg Lewis, et al. ∙

research

∙ 02/16/2020

Simple, Credible, and Approximately-Optimal Auctions

We identify the first static credible mechanism for multi-item additive ...

0 Constantinos Daskalakis, et al. ∙

research

∙ 10/15/2019

Dynamically Aggregating Diverse Information

An agent has access to multiple data sources, each of which provides inf...

0 Annie Liang, et al. ∙

research

∙ 05/24/2019

Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments

We consider the estimation of heterogeneous treatment effects with arbit...

0 Vasilis Syrgkanis, et al. ∙

research

∙ 05/24/2019

Semi-Parametric Efficient Policy Learning with Continuous Actions

We consider off-policy evaluation and optimization with continuous actio...

0 Mert Demirer, et al. ∙

research

∙ 01/25/2019

Orthogonal Statistical Learning

We provide excess risk guarantees for statistical learning in the presen...

0 Dylan J. Foster, et al. ∙

research

∙ 01/11/2019

Non-Parametric Inference Adaptive to Intrinsic Dimension

We consider non-parametric estimation and inference of conditional momen...

0 Khashayar Khosravi, et al. ∙

research

∙ 06/13/2018

Plug-in Regularized Estimation of High-Dimensional Parameters in Nonlinear Semiparametric Models

We develop a theory for estimation of a high-dimensional sparse paramete...

0 Victor Chernozhukov, et al. ∙

research

∙ 06/09/2018

Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation

We study the problem of estimating heterogeneous treatment effects from ...

0 Miruna Oprescu, et al. ∙

research

∙ 03/19/2018

Adversarial Generalized Method of Moments

We provide an approach for learning deep neural net representations of m...

0 Greg Lewis, et al. ∙

research

∙ 03/13/2018

Expert identification of visual primitives used by CNNs during mammogram classification

This work interprets the internal representations of deep neural network...

0 Jimmy Wu, et al. ∙

research

∙ 03/12/2018

Semiparametric Contextual Bandits

This paper studies semiparametric contextual bandits, a generalization o...

0 Akshay Krishnamurthy, et al. ∙

research

∙ 01/30/2018

Low-rank Bandit Methods for High-dimensional Dynamic Pricing

We consider high dimensional dynamic multi-product pricing with an evolv...

0 Jonas Mueller, et al. ∙

research

∙ 12/18/2017

Accurate Inference for Adaptive Linear Models

Estimators computed from adaptively collected data do not behave like th...

0 Yash Deshpande, et al. ∙

research

∙ 11/07/2017

Combinatorial Assortment Optimization

Assortment optimization refers to the problem of designing a slate of pr...

0 Nicole Immorlica, et al. ∙

research

∙ 11/03/2017

Learning to Bid Without Knowing your Value

We address online learning in complex auction settings, such as sponsore...

0 Zhe Feng, et al. ∙

research

∙ 11/03/2017

Optimal Data Acquisition for Statistical Estimation

We consider a data analyst's problem of purchasing data from strategic a...

0 Yiling Chen, et al. ∙

research

∙ 11/01/2017

Orthogonal Machine Learning: Power and Limitations

Double machine learning provides √(n)-consistent estimates of parameters...

0 Lester Mackey, et al. ∙

research

∙ 10/31/2017

Training GANs with Optimism

We address the issue of limit cycling behavior in training Generative Ad...

0 Constantinos Daskalakis, et al. ∙

research

∙ 10/10/2017

Inference on Auctions with Weak Assumptions on Information

Given a sample of bids from independent auctions, this paper examines th...

0 Vasilis Syrgkanis, et al. ∙

research

∙ 07/04/2017

Robust Optimization for Non-Convex Objectives

We consider robust optimization problems, where the goal is to optimize ...

0 Robert Chen, et al. ∙

research

∙ 04/12/2017

A Proof of Orthogonal Double Machine Learning with Z-Estimators

We consider two stage estimation with a non-parametric first stage and a...

0 Vasilis Syrgkanis, et al. ∙

research

∙ 07/26/2016

The Price of Anarchy in Auctions

This survey outlines a general and modular theory for proving approximat...

0 Tim Roughgarden, et al. ∙

research

∙ 11/04/2015

Learning in Auctions: Regret is Hard, Envy is Easy

A line of recent work provides welfare guarantees of simple combinatoria...

0 Constantinos Daskalakis, et al. ∙

Vasilis Syrgkanis

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