4 citations · 33 across the 44 of their papers we have counts for
47 papers
DLB: Distributed Load Balancing at Scale for Generative AI Inference
Santiago R. Balseiro, Bartek Wydrowski, Sameer Agarwal +11
The reliance on scarce and expensive accelerators such as GPUs and TPUs in modern datacenters places unprecedented demands on backend infrastructure. For workloads characterized by…
Resource Allocation and Conversion along the Org Chart
Yuan Deng, Giannis Fikioris, Chido Onyeze +3
We consider the allocation of multiple heterogeneous resources to agents who are organized according to an organizational hierarchy. In a company those correspond to business units…
Quota Marketplace: Dynamic Pricing for Efficient Allocation of ML Training Resources
Balasubramanian Sivan, Renato Paes Leme, Mihai Tiuca +6
The escalating demand for Machine Learning (ML) training resources in recent years has resulted in a substantial gap between the high demand and the available supply. Efficient all…
Learning vs. Optimizing Bidders in Budgeted Auctions
Giannis Fikioris, Balasubramanian Sivan, Éva Tardos
The study of repeated interactions between a learner and a utility-maximizing optimizer has yielded deep insights into the manipulability of learning algorithms. However, existing…
Swap Regret and Correlated Equilibria Beyond Normal-Form Games
Eshwar Ram Arunachaleswaran, Natalie Collina, Yishay Mansour +3
Swap regret is a notion that has proven itself to be central to the study of general-sum normal-form games, with swap-regret minimization leading to convergence to the set of corre…
Approximately Efficient Bilateral Trade with Samples
Yuan Deng, Jieming Mao, Balasubramanian Sivan +2
We study the social efficiency of bilateral trade between a seller and a buyer. In the classical Bayesian setting, the celebrated Myerson-Satterthwaite impossibility theorem states…
Distributed Load Balancing with Workload-Dependent Service Rates
Wenxin Zhang, Santiago R. Balseiro, Robert Kleinberg +3
Modern service systems, including cloud platforms and large language model inference endpoints, must distribute jobs across servers whose processing speeds depend on current worklo…
Auto-bidding and Auctions in Online Advertising: A Survey
Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro +23
In this survey, we summarize recent developments in research fueled by the growing adoption of automated bidding strategies in online advertising. We explore the challenges and opp…
Rate-Preserving Reductions for Blackwell Approachability
Christoph Dann, Yishay Mansour, Mehryar Mohri +2
Abernethy et al. (2011) showed that Blackwell approachability and no-regret learning are equivalent, in the sense that any algorithm that solves a specific Blackwell approachabilit…
Revenue Variance Minimization: Beyond First Price Auctions
Marek Bojko, Preston McAfee, Renato Paes Leme +2
We study revenue variance in the sale of homogeneous items to risk-neutral, unit-demand bidders with independent private values. Although the Revenue Equivalence Theorem implie…
Strategically-Robust Learning Algorithms for Bidding in First-Price Auctions
Rachitesh Kumar, Jon Schneider, Balasubramanian Sivan
Learning to bid in repeated first-price auctions is a fundamental problem at the interface of game theory and machine learning, which has seen a recent surge in interest due to the…
Description Complexity of Regular Distributions
Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng +1
Myerson's regularity condition of a distribution is a standard assumption in economics. In this paper, we study the complexity of describing a regular distribution within a small s…
Robust Budget Pacing with a Single Sample
Santiago Balseiro, Rachitesh Kumar, Vahab Mirrokni +2
Major Internet advertising platforms offer budget pacing tools as a standard service for advertisers to manage their ad campaigns. Given the inherent non-stationarity in an adverti…
Pseudonorm Approachability and Applications to Regret Minimization
Christoph Dann, Yishay Mansour, Mehryar Mohri +2
Blackwell's celebrated approachability theory provides a general framework for a variety of learning problems, including regret minimization. However, Blackwell's proof and implici…
A Field Guide for Pacing Budget and ROS Constraints
Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng +4
Budget pacing is a popular service that has been offered by major internet advertising platforms since their inception. Budget pacing systems seek to optimize advertiser returns su…
Strategizing against Learners in Bayesian Games
Yishay Mansour, Mehryar Mohri, Jon Schneider +1
We study repeated two-player games where one of the players, the learner, employs a no-regret learning strategy, while the other, the optimizer, is a rational utility maximizer. We…
Price Manipulability in First-Price Auctions
Johannes Brustle, Paul Dütting, Balasubramanian Sivan
First-price auctions have many desirable properties, including uniquely possessing some, like credibility. However, first-price auctions are also inherently non-truthful, and non-t…
Analysis of Dual-Based PID Controllers through Convolutional Mirror Descent
Santiago R. Balseiro, Haihao Lu, Vahab Mirrokni +1
Dual-based proportional-integral-derivative (PID) controllers are often employed in practice to solve online allocation problems with global constraints, such as budget pacing in o…
Pricing Query Complexity of Revenue Maximization
Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng +1
The common way to optimize auction and pricing systems is to set aside a small fraction of the traffic to run experiments. This leads to the question: how can we learn the most wit…
Approximately Efficient Bilateral Trade
Yuan Deng, Jieming Mao, Balasubramanian Sivan +1
We study bilateral trade between two strategic agents. The celebrated result of Myerson and Satterthwaite states that in general, no incentive-compatible, individually rational and…
Online Bipartite Matching with Reusable Resources
Steven Delong, Alireza Farhadi, Rad Niazadeh +2
We study the classic online bipartite matching problem with a twist: offline vertices, called resources, are . In particular, when a resource is matched to an on…
Online Allocation and Display Ads Optimization with Surplus Supply
Melika Abolhassani, Hossein Esfandiari, Yasamin Nazari +3
In this work, we study a scenario where a publisher seeks to maximize its total revenue across two sales channels: guaranteed contracts that promise to deliver a certain number of…
Learning to Price Against a Moving Target
Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng +1
In the Learning to Price setting, a seller posts prices over time with the goal of maximizing revenue while learning the buyer's valuation. This problem is very well understood whe…
Optimal Pricing Schemes for an Impatient Buyer
Yuan Deng, Jieming Mao, Balasubramanian Sivan +1
A patient seller aims to sell a good to an impatient buyer (i.e., one who discounts utility over time). The buyer will remain in the market for a period of time , and her privat…