Patrick Jaillet

Distribution-Dependent Rates for Multi-Distribution Learning ALT 2026 A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries AISTATS 2025 Learning with Exact Invariances in Polynomial Time ICML 2025 Neural Dueling Bandits: Preference-Based Optimization with Human Feedback ICLR 2025 Non-Monetary Mechanism Design without Distributional Information: Using Scarce Audits Wisely (Extended Abstract) COLT 2025 A Universal Class of Sharpness-Aware Minimization Algorithms ICML 2024 Active Set Ordering NIPS 2024 Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars NIPS 2024 Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes ICLR 2024 Optimistic Bayesian Optimization with Unknown Constraints ICLR 2024 Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers ICML 2024 Deletion-Anticipative Data Selection with a Limited Budget ICML 2024 Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal COLT 2023 Memory-Constrained Algorithms for Convex Optimization NIPS 2023 Incentives in Private Collaborative Machine Learning NIPS 2023 Quantum Bayesian Optimization NIPS 2023 Batch Bayesian Optimization For Replicable Experimental Design NIPS 2023 DRCFS: Doubly Robust Causal Feature Selection ICML 2023 Multi-channel Autobidding with Budget and ROI Constraints ICML 2023 Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation ICLR 2023 Federated Neural Bandits ICLR 2023 Pricing against a Budget and ROI Constrained Buyer AISTATS 2023 Incentive-aware Contextual Pricing with Non-parametric Market Noise AISTATS 2023 Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation ICLR 2023 Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning NIPS 2022 On provably robust meta-Bayesian optimization UAI 2022 Effective Dimension in Bandit Problems under Censorship NIPS 2022 Sample-Then-Optimize Batch Neural Thompson Sampling NIPS 2022 An Information-Theoretic Framework for Unifying Active Learning Problems AAAI 2021 Learning to learn with Gaussian processes UAI 2021 Trusted-maximizers entropy search for efficient Bayesian optimization UAI 2021 Model Fusion for Personalized Learning ICML 2021 Value-at-Risk Optimization with Gaussian Processes ICML 2021 Collaborative Bayesian Optimization with Fair Regret ICML 2021 Differentially Private Federated Bayesian Optimization with Distributed Exploration NIPS 2021 Optimizing Conditional Value-At-Risk of Black-Box Functions NIPS 2021 Top-k Ranking Bayesian Optimization AAAI 2021 No-regret Learning in Price Competitions under Consumer Reference Effects NIPS 2020 Federated Bayesian Optimization via Thompson Sampling NIPS 2020 R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games ICML 2020 Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion ICML 2020 Variational Bayesian Unlearning NIPS 2020 Implicit Posterior Variational Inference for Deep Gaussian Processes NIPS 2019 Bayesian Optimization Meets Bayesian Optimal Stopping ICML 2019 Improving Customer Satisfaction in Bike Sharing Systems through Dynamic Repositioning IJCAI 2019 Real-Time Bidding with Side Information NIPS 2017 Online Learning with a Hint NIPS 2017 Inverse Reinforcement Learning with Locally Consistent Reward Functions NIPS 2015 Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes ICML 2014 Regret based Robust Solutions for Uncertain Markov Decision Processes NIPS 2013