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Jason Cheuk Nam Liang

7 accepted papers

2024

Interpolating Item and User Fairness in Multi-Sided Recommendations

NeurIPS 2024poster

Today's online platforms heavily lean on algorithmic recommendations for bolstering user engagement and driving revenue. However, these recommendations can impact multiple stakeholders simultaneously---the platform, items (sellers), and users (customers)---each with their unique objectives, making i…

Cited by 3SourcePDFScholar
2023

Incentive-aware Contextual Pricing with Non-parametric Market Noise

AISTATS 2023poster

We consider a dynamic pricing problem for repeated contextual second-price auctions with multiple strategic buyers who aim to maximize their long-term time discounted utility. The seller has limited information on buyers’ overall demand curves which depends on a non-parametric market-noise distribut…

Cited by 38SourcePDFScholar
2023

Multi-channel Autobidding with Budget and ROI Constraints

ICML 2023poster

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 which consists of numerous ad auctions. We study how an advertiser maximizes total conversion (e.g. ad clicks) while sati…

Cited by 31SourcePDFScholar
2023

Pricing against a Budget and ROI Constrained Buyer

AISTATS 2023poster

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 and return-on-investment (ROI) constraints for efficient utilization of limited monetary resources. Facing such a constra…

Cited by 7SourcePDFScholar
2020

Decision Trees for Decision-Making under the Predict-then-Optimize Framework

ICML 2020poster

We consider the use of decision trees for decision-making problems under the predict-then-optimize framework. That is, we would like to first use a decision tree to predict unknown input parameters of an optimization problem, and then make decisions by solving the optimization problem using the pred…

2020

No-regret Learning in Price Competitions under Consumer Reference Effects

NeurIPS 2020poster

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 reference prices. Consumers' reference prices vary over time according to a memory-based dynamic, which is a weighted a…

Cited by 15SourcePDFScholar