Patrick Jaillet
Dugald C. Jackson Professor of Electrical Engineering and Computer Science
Studies online optimization and learning, and sequential decision-making under uncertainty.
Patrick Jaillet's lab at MIT works on reinforcement learning and learning theory & optimization. 47 papers since 2023, 29 at top venues, mostly NeurIPS and ICLR. 5 PhD students. 42 alumni, most often to Amazon and Citadel. Applications close .
At a glance
- papers, 2023–2026
- 12/ year
- at T1 venues
- 62%
- PhD students
- 5
- MS students
- 0
- alumni listed
- 42
Papers by yearfrom the lab homepage, 2023–2026
- 16 papers2026 · T1 9 · collecting
- 10 papers2025 · T1 5
- 12 papers2024 · T1 8
- 9 papers2023 · T1 7
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, COLT, AISTATS
Research focuspaper titles that use each topic's words
- Learning theory & optimization18 papers
- Reinforcement learning10 papers
- online7 papers
- sequential0 papers
- decision-making0 papers
Paper list
From the lab homepage.
202616 papers
- NeurIPS (T1)A Single-Sample Polylogarithmic Regret Bound for Nonstationary Online Linear Programming (opens in a new tab)
- ICML (T1)A Two-Layer Framework for Joint Online Configuration and Admission Control (opens in a new tab)
- Accepted, Mathematics of Operations ResearchContextual Bandits and Optimistically Universal Learning (opens in a new tab)
- International Conference on Algorithmic Learning Theory, ALTDistribution-Dependent Rates for Multi-Distribution Learning (opens in a new tab)
- COLT (T1)Efficient Learning and Symmetry Discovery under Exact Invariances (opens in a new tab)
- NeurIPS (T1)Exact-Form Regret and Correlated Equilibria: The Hidden Power of Online Gradient and Mirror Descent (opens in a new tab)
- NeurIPS (T1)Homological Barriers to Stable Local Nash Dynamics in Quadratic Zero-Sum Games (opens in a new tab)
- Oral, International Conference on Machine Learning, ICMLIncentivizing Truthfulness and Collaborative Fairness in Bayesian Machine Learning (opens in a new tab)
- COLT (T1)Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise (opens in a new tab)
- ACM Transactions on Economics and Computation, 14(1), 2:1-47Market Design for Capacity Sharing in Networks (opens in a new tab)
- Accepted, Operations ResearchNear-Optimal Mechanisms for Resource Allocation Without Monetary Transfers (opens in a new tab)
- NeurIPS (T1)On Lipschitz Explosion in Deep Neural Networks with Normalization: Consequences for Optimization and Robustness (opens in a new tab)
202510 papers
- NeurIPS (T1)"Incentive-Aware Dynamic Resource Allocation under Long-Term Cost Constraints (opens in a new tab)
- Oral, International Conference on Artificial Intelligence and Statistics, AISTATA Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries (opens in a new tab)
- TMLR (T1)Double Machine Learning Based Structure Identification from Temporal Data (opens in a new tab)
- Spotlight, International Conference on Machine Learning, ICMLLearning with Exact Invariances in Polynomial Time (opens in a new tab)
- Accepted, Management ScienceMulti-channel Autobidding with Budget and ROI Constraints (opens in a new tab)
- European Journal of Operational Research, 324(3), 787-798Multi-Drone Rescue Search in a Large Network (opens in a new tab)
- ICLR (T1)Neural Dueling Bandits: Preference-Based Optimization with Human Feedback (opens in a new tab)
- COLT (T1)Non-Monetary Mechanism Design without Distributional Information: Using Scarce Audits Wisely (opens in a new tab)
- Management Science (T1)Portfolio Optimization Based on Almost Second-degree Stochastic Dominance (opens in a new tab)
- Mathematics of Operations Research, 50(4), 2941-2971Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal (opens in a new tab)
202412 papers
- ICML (T1)A Universal Class of Sharpness-Aware Minimization Algorithms (opens in a new tab)
- NeurIPS (T1)Active Set Ordering (opens in a new tab)
- ICML (T1)Deletion-Anticipative Data Selection with a Limited Budget (opens in a new tab)
- Conference on Web and Internet Economics, WINEGrace Period is All You Need: Individual Fairness without Revenue Loss in Revenue Management (opens in a new tab)
- WWW (T1)Individual Welfare Guarantees in the Autobidding World with Machine-learned Advice (opens in a new tab)
- ICLR (T1)Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes (opens in a new tab)
- Frontiers in Public Health, section Infectious Diseases: Epidemiology and PreventionOn the Impact of Mass Screening for SARS-CoV-2 through Self-Testing in Greece (opens in a new tab)
- ICLR (T1)Optimistic Bayesian Optimization with Unknown Constraints (opens in a new tab)
- Mathematics of Operations Research, 49(2), 1169-1191Probabilistic bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem (opens in a new tab)
- NeurIPS (T1)Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars (opens in a new tab)
- ICML (T1)Use Your INSTINCT: INStruction optimization usIng Neural bandits Coupled with Transformers (opens in a new tab)
- Conference on Web and Internet Economics, WINEWhen Should you Offer an Upgrade: Online Upgrading Mechanisms for Resource Allocation (opens in a new tab)
20239 papers
- ICML (T1)DRCFS: Doubly Robust Causal Feature Selection (opens in a new tab)
- Mathematics of Operations Research, 48(2), 999-1016Edge Weighted Online Windowed Matching (opens in a new tab)
- ICLR (T1)Federated Neural Bandit (opens in a new tab)
- AISTATS (T1)Incentive-aware Contextual Pricing with Non-parametric Market Noise (opens in a new tab)
- NeurIPS (T1)Incentives in Private Collaborative Machine Learning (opens in a new tab)
- CDCInformation Disclosure about Booster Efficacy in a Non-Stationary Environment (opens in a new tab)
- NeurIPS (T1)Memory-Constrained Algorithms for Convex Optimization (opens in a new tab)
- AISTATS (T1)Pricing against a Budget and ROI Constrained Buyer (opens in a new tab)
- ICLR (T1)Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation (opens in a new tab)
Where alumni went42 listed, 53% to industry
Industry 21Academia 19
Most often to
- Amazon
- Citadel
- Microsoft
Also to
- CUHK
- ETH Zurich
- Georgia Tech
Apply to MITFall 2027
MIT EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
- USD 90
Confirmed on the program page.
Similar labsnearest by research summary
- USUT Austin
Constantine Caramanis
- Learning theory & optimization
- CAToronto
Anthony Bonner
- Generative & foundation models
- USNYU
Sherry Yang
- Generative & foundation models
- GBOxford
Jakob Foerster
- Reinforcement learning
- USMIT
Jonathan Ragan-Kelley
- Rendering & appearance
- SGNTU
Mengmi Zhang
- Neuroscience & brain-computer interfaces
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
MITcloses