117 citations · 704 across the 174 of their papers we have counts for
11 papers · 1 filter
Causal inference with bipartite designs: A generalized propensity score approach
Nick Doudchenko, Minzhengxiong Zhang, Evgeni Drynkin +5
Bipartite experiments, in which one set of units receives a treatment while outcomes are measured on another set, have recently garnered attention for their ability to capture inte…
The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems
Santiago Balseiro, Haihao Lu, Vahab Mirrokni
Online allocation problems with resource constraints are central problems in revenue management and online advertising. In these problems, requests arrive sequentially during a fin…
Optimal Approximation -- Smoothness Tradeoffs for Soft-Max Functions
Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni +1
A soft-max function has two main efficiency measures: (1) approximation - which corresponds to how well it approximates the maximum function, (2) smoothness - which shows how sensi…
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems
Benjamin Grimmer, Haihao Lu, Pratik Worah +1
Unlike nonconvex optimization, where gradient descent is guaranteed to converge to a local optimizer, algorithms for nonconvex-nonconcave minimax optimization can have topologicall…
Parallel Graph Algorithms in Constant Adaptive Rounds: Theory meets Practice
Soheil Behnezhad, Laxman Dhulipala, Hossein Esfandiari +3
We study fundamental graph problems such as graph connectivity, minimum spanning forest (MSF), and approximate maximum (weight) matching in a distributed setting. In particular, we…
Regularized Online Allocation Problems: Fairness and Beyond
Santiago Balseiro, Haihao Lu, Vahab Mirrokni
Online allocation problems with resource constraints have a rich history in operations research. In this paper, we introduce the \emph{regularized online allocation problem}, a var…
The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization
Benjamin Grimmer, Haihao Lu, Pratik Worah +1
Minimax optimization has become a central tool in machine learning with applications in robust optimization, reinforcement learning, GANs, etc. These applications are often nonconv…
Bandits with adversarial scaling
Thodoris Lykouris, Vahab Mirrokni, Renato Paes Leme
We study "adversarial scaling", a multi-armed bandit model where rewards have a stochastic and an adversarial component. Our model captures display advertising where the "click-thr…
Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions
Negin Golrezaei, Adel Javanmard, Vahab Mirrokni
Motivated by pricing in ad exchange markets, we consider the problem of robust learning of reserve prices against strategic buyers in repeated contextual second-price auctions. Buy…
Dual Mirror Descent for Online Allocation Problems
Haihao Lu, Santiago Balseiro, Vahab Mirrokni
We consider online allocation problems with concave revenue functions and resource constraints, which are central problems in revenue management and online advertising. In these se…
Contextual Reserve Price Optimization in Auctions via Mixed-Integer Programming
Joey Huchette, Haihao Lu, Hossein Esfandiari +1
We study the problem of learning a linear model to set the reserve price in an auction, given contextual information, in order to maximize expected revenue from the seller side. Fi…