37 citations · 90 across the 31 of their papers we have counts for
35 papers
MACS: A Hybrid Multi-Agent Framework for Reliable Conversational E-Commerce Recommendation
Juli Huang, Hannah Clay, Sajjad Beygi +3
Conversational recommendation for e-commerce is increasingly mediated by large language models (LLMs), yet many real-world deployments operate under a stricter requirement: recomme…
Learning to Bid in Discriminatory Auctions with Budget Constraints
Negin Golrezaei, Sourav Sahoo
We study repeated bidding in multi-unit discriminatory (pay-as-bid) auctions for a single bidder with per-round utility equal to value minus times payment, where is…
Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Experts
Yan Dai, Negin Golrezaei, Patrick Jaillet
Modern recommendation systems increasingly rely on dynamically routing diverse queries to multiple embedding models. Despite its practical significance, this problem remains poorly…
Market Design for AI: Beyond the Copyright Binary
Yan Dai, Maryam Farboodi, Negin Golrezaei +1
How can we design a market of human-generated content for use in training AI models that both enables technological progress and preserves individual incentives for high-quality co…
Optimal Contest Beyond Convexity
Negin Golrezaei, MohammadTaghi Hajiaghayi, Suho Shin
In the contest design problem, there are strategic contestants, each of whom decides an effort level. A contest designer with a fixed budget must then design a mechanism that a…
Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems
Dat Tran, Yongce Li, Hannah Clay +3
Users on e-commerce platforms can be uncertain about their preferences early in their search. Queries to recommendation systems are frequently ambiguous, incomplete, or weakly spec…
Efficiency, Feasibility, and Incentive-Awareness in Constrained Online Resource Allocation
Yan Dai, Negin Golrezaei, Patrick Jaillet
We study the dynamic allocation of indivisible resources to strategic agents under long-term constraints, where the planner aims to maximize social welfare, satisfy multiple constr…
Learning Safe Strategies for Value Maximizing Buyers in Uniform Price Auctions
Negin Golrezaei, Sourav Sahoo
We study the bidding problem in repeated uniform price multi-unit auctions from the perspective of a value-maximizing buyer. The buyer aims to maximize their cumulative value over…
Learning in Repeated Multi-Unit Pay-As-Bid Auctions
Rigel Galgana, Negin Golrezaei
Motivated by Carbon Emissions Trading Schemes, Treasury Auctions, Procurement Auctions, and Wholesale Electricity Markets, which all involve the auctioning of homogeneous multiple…
Multi-Platform Budget Management in Ad Markets with Non-IC Auctions
Fransisca Susan, Negin Golrezaei, Okke Schrijvers
In online advertising markets, budget-constrained advertisers acquire ad placements through repeated bidding in auctions on various platforms. We present a strategy for bidding opt…
Online Resource Allocation with Convex-set Machine-Learned Advice
Negin Golrezaei, Patrick Jaillet, Zijie Zhou
Decision-makers often have access to machine-learned predictions about future demand that can help guide online resource allocation decisions. However, such predictions may be inac…
Interpolating Item and User Fairness in Multi-Sided Recommendations
Qinyi Chen, Jason Cheuk Nam Liang, Negin Golrezaei +1
Today's online platforms heavily lean on algorithmic recommendations for bolstering user engagement and driving revenue. However, these recommendations can impact multiple stakehol…
Learning and Collusion in Multi-unit Auctions
Simina Brânzei, Mahsa Derakhshan, Negin Golrezaei +1
We consider repeated multi-unit auctions with uniform pricing, which are widely used in practice for allocating goods such as carbon licenses. In each round, identical units of…
Multi-channel Autobidding with Budget and ROI Constraints
Yuan Deng, Negin Golrezaei, Patrick Jaillet +2
In digital online advertising, advertisers procure ad impressions simultaneously on multiple platforms, or so-called channels, such as Google Ads, Meta Ads Manager, etc., each of w…
Online Resource Allocation with Samples
Negin Gorlezaei, Patrick Jaillet, Zijie Zhou
We study an online resource allocation problem under uncertainty about demand and about the reward of each type of demand (agents) for the resource. Even though dealing with demand…
Non-Stationary Bandits with Auto-Regressive Temporal Dependency
Qinyi Chen, Negin Golrezaei, Djallel Bouneffouf
Traditional multi-armed bandit (MAB) frameworks, predominantly examined under stochastic or adversarial settings, often overlook the temporal dynamics inherent in many real-world a…
Individual Welfare Guarantees in the Autobidding World with Machine-learned Advice
Yuan Deng, Negin Golrezaei, Patrick Jaillet +2
Online advertising channels have commonly focused on maximizing total advertiser value (or welfare) to enhance long-run retention and channel healthiness. Previous literature has s…
Fair Assortment Planning
Qinyi Chen, Negin Golrezaei, Fransisca Susan
Many online platforms, ranging from online retail stores to social media platforms, employ algorithms to optimize their offered assortment of items (e.g., products and contents). T…
Active Learning for Non-Parametric Choice Models
Fransisca Susan, Negin Golrezaei, Ehsan Emamjomeh-Zadeh +1
We study the problem of actively learning a non-parametric choice model based on consumers' decisions. We present a negative result showing that such choice models may not be ident…
Upfront Commitment in Online Resource Allocation with Patient Customers
Negin Golrezaei, Evan Yao
In many on-demand online platforms such as ride-sharing, grocery delivery, or shipping, some arriving agents are patient and willing to wait a short amount of time for the resource…
Learning to Price against a Budget and ROI Constrained Buyer
Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang +1
Internet advertisers (buyers) repeatedly procure ad impressions from ad platforms (sellers) with the aim to maximize total conversion (i.e. ad value) while respecting both budget a…
Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization
Rad Niazadeh, Negin Golrezaei, Joshua Wang +2
Motivated by online decision-making in time-varying combinatorial environments, we study the problem of transforming offline algorithms to their online counterparts. We focus on of…
No-regret Learning in Price Competitions under Consumer Reference Effects
Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang
We study long-run market stability for repeated price competitions between two firms, where consumer demand depends on firms' posted prices and consumers' price expectations called…
Learning Product Rankings Robust to Fake Users
Negin Golrezaei, Vahideh Manshadi, Jon Schneider +1
In many online platforms, customers' decisions are substantially influenced by product rankings as most customers only examine a few top-ranked products. Concurrently, such platfor…