1 code implementation • 7 Feb 2024 • Zitong Yang, Emmanuel Candès, Lihua Lei
We introduce Bellman Conformal Inference (BCI), a framework that wraps around any time series forecasting models and provides approximately calibrated prediction intervals.
no code implementations • 12 Dec 2023 • Lihua Lei, Brad Ross
We develop a new, spectral approach for identifying and estimating average counterfactual outcomes under a low-rank factor model with short panel data and general outcome missingness patterns.
no code implementations • 23 Oct 2023 • Davide Viviano, Lihua Lei, Guido Imbens, Brian Karrer, Okke Schrijvers, Liang Shi
This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers.
1 code implementation • 12 Oct 2023 • Wenlong Ji, Lihua Lei, Asher Spector
Finally, we propose an efficient computational framework, enabling implementation on many practical problems in causal inference.
no code implementations • 23 Apr 2023 • Lihua Lei, Roshni Sahoo, Stefan Wager
Practitioners often use data from a randomized controlled trial to learn a treatment assignment policy that can be deployed on a target population.
no code implementations • 29 Dec 2022 • Lihua Lei, Jeffrey Wooldridge
Despite the elegant proof, it was shown by the authors and other researchers that the main result in the earlier version of Hansen's paper does not extend the classic Gauss-Markov theorem because no nonlinear unbiased estimator exists under his conditions.
1 code implementation • 5 Sep 2022 • Roshni Sahoo, Lihua Lei, Stefan Wager
Applying the distributionally robust optimization framework, we propose a method for learning a decision rule that minimizes the worst-case risk incurred under a family of test distributions that can generate the training distribution under $\Gamma$-biased sampling.
1 code implementation • 13 Aug 2022 • Ariane Marandon, Lihua Lei, David Mary, Etienne Roquain
This paper studies the semi-supervised novelty detection problem where a set of "typical" measurements is available to the researcher.
1 code implementation • 4 Aug 2022 • Anastasios N. Angelopoulos, Stephen Bates, Adam Fisch, Lihua Lei, Tal Schuster
We extend conformal prediction to control the expected value of any monotone loss function.
no code implementations • 10 Feb 2022 • Isaiah Andrews, Drew Fudenberg, Lihua Lei, Annie Liang, Chaofeng Wu
Economists often estimate models using data from a particular domain, e. g. estimating risk preferences in a particular subject pool or for a specific class of lotteries.
1 code implementation • 3 Oct 2021 • Anastasios N. Angelopoulos, Stephen Bates, Emmanuel J. Candès, Michael I. Jordan, Lihua Lei
We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees.
2 code implementations • 29 Jul 2021 • Dmitry Arkhangelsky, Guido W. Imbens, Lihua Lei, Xiaoman Luo
We propose a new estimator for average causal effects of a binary treatment with panel data in settings with general treatment patterns.
1 code implementation • 16 Apr 2021 • Stephen Bates, Emmanuel Candès, Lihua Lei, Yaniv Romano, Matteo Sesia
We then introduce a new method to compute p-values that are both valid conditionally on the training data and independent of each other for different test points; this paves the way to stronger type-I error guarantees.
2 code implementations • 17 Mar 2021 • Emmanuel J. Candès, Lihua Lei, Zhimei Ren
Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors.
3 code implementations • 7 Jan 2021 • Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, Michael I. Jordan
While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making.
2 code implementations • 11 Jun 2020 • Lihua Lei, Emmanuel J. Candès
At the moment, much emphasis is placed on the estimation of the conditional average treatment effect via flexible machine learning algorithms.
no code implementations • 30 Apr 2020 • Lihua Lei, XiaoDong Li, Xingmei Lou
We study the hierarchy of communities in real-world networks under a generic stochastic block model, in which the connection probabilities are structured in a binary tree.
1 code implementation • 13 Feb 2020 • Samuel Horváth, Lihua Lei, Peter Richtárik, Michael. I. Jordan
Adaptivity is an important yet under-studied property in modern optimization theory.
no code implementations • ICLR 2020 • Melih Elibol, Lihua Lei, Michael. I. Jordan
Variance reduction methods such as SVRG and SpiderBoost use a mixture of large and small batch gradients to reduce the variance of stochastic gradients.
no code implementations • 9 Apr 2019 • Lihua Lei, Michael. I. Jordan
Stochastic-gradient-based optimization has been a core enabling methodology in applications to large-scale problems in machine learning and related areas.
no code implementations • 2 Oct 2018 • Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Koen Van den Berge, Purnamrita Sarkar, Peter J. Bickel, Elizaveta Levina
This can be done with a simple top-down recursive partitioning algorithm, starting with a single community and separating the nodes into two communities by spectral clustering repeatedly, until a stopping rule suggests there are no further communities.
no code implementations • 12 Sep 2016 • Lihua Lei, Michael. I. Jordan
We develop and analyze a procedure for gradient-based optimization that we refer to as stochastically controlled stochastic gradient (SCSG).