| 2026 | COLT | Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise. | Rafael Hanashiro, Abhishek Shetty, Patrick Jaillet |
| 2026 | COLT | Efficient Learning and Symmetry Discovery under Exact Invariances. | Ashkan Soleymani, Behrooz Tahmasebi, Patrick Jaillet, Stefanie Jegelka |
| 2025 | AISTATS | A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries. | Ashkan Soleymani, Behrooz Tahmasebi, Stefanie Jegelka, Patrick Jaillet |
| 2025 | COLT | Non-Monetary Mechanism Design without Distributional Information: Using Scarce Audits Wisely (Extended Abstract). | Yan Dai, Mose Blanchard, Patrick Jaillet |
| 2025 | ICLR | Neural Dueling Bandits: Preference-Based Optimization with Human Feedback. | Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian Hsiang Low |
| 2025 | ICML | Learning with Exact Invariances in Polynomial Time. | Ashkan Soleymani, Behrooz Tahmasebi, Stefanie Jegelka, Patrick Jaillet |
| 2024 | ICLR | Optimistic Bayesian Optimization with Unknown Constraints. | Quoc Phong Nguyen, Wan Theng Ruth Chew, Le Song, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2024 | ICLR | Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes. | Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2024 | ICML | Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers. | Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low |
| 2024 | ICML | Deletion-Anticipative Data Selection with a Limited Budget. | Rachael Hwee Ling Sim, Jue Fan, Xiao Tian, Patrick Jaillet, Bryan Kian Hsiang Low |
| 2024 | ICML | A Universal Class of Sharpness-Aware Minimization Algorithms. | Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri, Stefanie Jegelka, Patrick Jaillet |
| 2024 | WWW | Individual Welfare Guarantees in the Autobidding World with Machine-learned Advice. | Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni |
| 2023 | AISTATS | Incentive-aware Contextual Pricing with Non-parametric Market Noise. | Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang |
| 2023 | AISTATS | Pricing against a Budget and ROI Constrained Buyer. | Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni |
| 2023 | COLT | Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal. | Mose Blanchard, Junhui Zhang, Patrick Jaillet |
| 2023 | ICLR | Federated Neural Bandits. | Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2023 | ICLR | Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation. | Thanh Lam, Arun Verma, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2023 | ICLR | Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation. | Yao Shu, Zhongxiang Dai, Weicong Sng, Arun Verma, Patrick Jaillet, Bryan Kian Hsiang Low |
| 2023 | ICML | Multi-channel Autobidding with Budget and ROI Constraints. | Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni |
| 2023 | ICML | DRCFS: Doubly Robust Causal Feature Selection. | Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Cristian R. Rojas, Stefan Bauer |
| 2022 | UAI | On provably robust meta-Bayesian optimization. | Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | AAAI | An Information-Theoretic Framework for Unifying Active Learning Problems. | Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | AAAI | Top-k Ranking Bayesian Optimization. | Quoc Phong Nguyen, Sebastian Tay, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | ICML | Model Fusion for Personalized Learning. | Thanh Chi Lam, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | ICML | Value-at-Risk Optimization with Gaussian Processes. | Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | ICML | Collaborative Bayesian Optimization with Fair Regret. | Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | IJCNN | Convolutional Normalizing Flows for Deep Gaussian Processes. | Haibin Yu, Dapeng Liu, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | UAI | Learning to learn with Gaussian processes. | Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | UAI | Trusted-maximizers entropy search for efficient Bayesian optimization. | Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2020 | ICML | R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games. | Zhongxiang Dai, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho |
| 2020 | ICML | Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion. | Trong Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2019 | EC | Edge Weighted Online Windowed Matching. | Itai Ashlagi, Maximilien Burq, Chinmoy Dutta, Patrick Jaillet, Amin Saberi, Chris Sholley |
| 2019 | ICAART | The Price of Anarchy: Centralized versus Distributed Resource Allocation Trade-offs. | Jinhong K. Guo, Alexander Karlovitz, Patrick Jaillet, Martin O. Hofmann |
| 2019 | ICML | Bayesian Optimization Meets Bayesian Optimal Stopping. | Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2019 | IJCAI | Improving Customer Satisfaction in Bike Sharing Systems through Dynamic Repositioning. | Supriyo Ghosh, Jing Yu Koh, Patrick Jaillet |
| 2019 | IJCNN | Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression. | Haibin Yu, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2017 | IPCO | Discrete Newton's Algorithm for Parametric Submodular Function Minimization. | Michel X. Goemans, Swati Gupta, Patrick Jaillet |
| 2016 | AAAI | Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond. | Chun Kai Ling, Kian Hsiang Low, Patrick Jaillet |
| 2016 | AAAI | Online Spatio-Temporal Matching in Stochastic and Dynamic Domains. | Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet |
| 2015 | AAAI | Solving Uncertain MDPs with Objectives that Are Separable over Instantiations of Model Uncertainty. | Yossiri Adulyasak, Pradeep Varakantham, Asrar Ahmed, Patrick Jaillet |
| 2015 | AAAI | Dynamic Redeployment to Counter Congestion or Starvation in Vehicle Sharing Systems. | Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet |
| 2015 | AAAI | Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation. | Kian Hsiang Low, Jiangbo Yu, Jie Chen, Patrick Jaillet |
| 2015 | ISAAC | Randomized Minmax Regret for Combinatorial Optimization Under Uncertainty. | Andrew Mastin, Patrick Jaillet, Sang Chin |
| 2015 | SODA | On the Quickest Flow Problem in Dynamic Networks - A Parametric Min-Cost Flow Approach. | Maokai Lin, Patrick Jaillet |
| 2015 | SoCS | Dynamic Redeployment to Counter Congestion or Starvation in Vehicle Sharing Systems. | Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet |
| 2014 | AAAI | Decentralized Stochastic Planning with Anonymity in Interactions. | Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet |
| 2014 | ICARCV | Predicting traffic speed in urban transportation subnetworks for multiple horizons. | Justin Dauwels, Aamer Aslam, Muhammad Tayyab Asif, Xinyue Zhao, Nikola Mitrovic, Andrzej Cichocki, Patrick Jaillet |
| 2014 | ICARCV | Extracting commuting patterns in railway networks through matrix decompositions. | Shashank Jere, Justin Dauwels, Muhammad Tayyab Asif, Nikola Mitrovic, Andrzej Cichocki, Patrick Jaillet |
| 2014 | ICASSP | Compressed prediction of large-scale urban traffic. | Nikola Mitrovic, Muhammad Tayyab Asif, Justin Dauwels, Patrick Jaillet |
| 2014 | ICML | Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes. | Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli |
| 2013 | ICASSP | Low-dimensional models for missing data imputation in road networks. | Muhammad Tayyab Asif, Nikola Mitrovic, Lalit Garg, Justin Dauwels, Patrick Jaillet |
| 2013 | IPCO | Advances on Matroid Secretary Problems: Free Order Model and Laminar Case. | Patrick Jaillet, Jos A. Soto, Rico Zenklusen |
| 2013 | UAI | Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations. | Jie Chen, Nannan Cao, Kian Hsiang Low, Ruofei Ouyang, Colin Keng-Yan Tan, Patrick Jaillet |
| 2012 | UAI | Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena. | Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan, Ali Oran, Patrick Jaillet, John M. Dolan, Gaurav S. Sukhatme |