Decision Trees for Decision-Making under the Predict-then-Optimize Framework. - Adam N. Elmachtoub; Jason Cheuk Nam Liang; Ryan McNellis (ICML, 2020)

No-regret Learning in Price Competitions under Consumer Reference Effects. - Negin Golrezaei; Patrick Jaillet; Jason Cheuk Nam Liang (NeurIPS, 2020)

Bidding and Pricing in Budget and ROI Constrained Markets. - Negin Golrezaei; Patrick Jaillet; Jason Cheuk Nam Liang; Vahab S. Mirrokni (CoRR, 2021)

Fairness in the Autobidding World with Machine-learned Advice. - Yuan Deng; Negin Golrezaei; Patrick Jaillet; Jason Cheuk Nam Liang; Vahab Mirrokni (CoRR, 2022)

Incentive-aware Contextual Pricing with Non-parametric Market Noise. - Negin Golrezaei; Patrick Jaillet; Jason Cheuk Nam Liang (AISTATS, 2023)

Pricing against a Budget and ROI Constrained Buyer. - Negin Golrezaei; Patrick Jaillet; Jason Cheuk Nam Liang; Vahab Mirrokni (AISTATS, 2023)

Multi-channel Autobidding with Budget and ROI Constraints. - Yuan Deng; Negin Golrezaei; Patrick Jaillet; Jason Cheuk Nam Liang; Vahab Mirrokni (ICML, 2023)

Online Ad Procurement in Non-stationary Autobidding Worlds. - Jason Cheuk Nam Liang; Haihao Lu; Baoyu Zhou (NeurIPS, 2023)

Interpolating Item and User Fairness in Recommendation Systems. - Qinyi Chen; Jason Cheuk Nam Liang; Negin Golrezaei; Djallel Bouneffouf 001 (CoRR, 2023)

Interpolating Item and User Fairness in Multi-Sided Recommendations. - Qinyi Chen; Jason Cheuk Nam Liang; Negin Golrezaei; Djallel Bouneffouf 001 (NeurIPS, 2024)

Individual Welfare Guarantees in the Autobidding World with Machine-learned Advice. - Yuan Deng; Negin Golrezaei; Patrick Jaillet; Jason Cheuk Nam Liang; Vahab Mirrokni (WWW, 2024)