Fast Marching Trees: A Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions. - Lucas Janson; Marco Pavone 001 (ISRR, 2013)

Fast Marching Trees: a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions - Extended Version. - Lucas Janson; Marco Pavone 001 (CoRR, 2013)

Optimal sampling-based motion planning under differential constraints: The drift case with linear affine dynamics. - Edward Schmerling; Lucas Janson; Marco Pavone 001 (CDC, 2015)

Optimal sampling-based motion planning under differential constraints: The driftless case. - Edward Schmerling; Lucas Janson; Marco Pavone 001 (ICRA, 2015)

An asymptotically-optimal sampling-based algorithm for Bi-directional motion planning. - Joseph A. Starek; Javier V. Gómez; Edward Schmerling; Lucas Janson; Luis Moreno 001; Marco Pavone 001 (IROS, 2015)

Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty. - Lucas Janson; Edward Schmerling; Marco Pavone 001 (ISRR (2), 2015)

Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions. - Lucas Janson; Edward Schmerling; Ashley A. Clark; Marco Pavone 001 (Int. J. Robotics Res., 2015)

Risk-Constrained Reinforcement Learning with Percentile Risk Criteria. - Yinlam Chow; Mohammad Ghavamzadeh; Lucas Janson; Marco Pavone 001 (J. Mach. Learn. Res., 2017)

Safe Motion Planning in Unknown Environments: Optimality Benchmarks and Tractable Policies. - Lucas Janson; Tommy Hu; Marco Pavone 001 (Robotics: Science and Systems, 2018)

Deterministic sampling-based motion planning: Optimality, complexity, and performance. - Lucas Janson; Brian Ichter; Marco Pavone 001 (Int. J. Robotics Res., 2018)

Map-Predictive Motion Planning in Unknown Environments. - Amine Elhafsi; Boris Ivanovic; Lucas Janson; Marco Pavone 001 (ICRA, 2020)

Revisiting the Asymptotic Optimality of RRT. - Kiril Solovey; Lucas Janson; Edward Schmerling; Emilio Frazzoli; Marco Pavone 001 (ICRA, 2020)

Cross-validation Confidence Intervals for Test Error. - Pierre Bayle; Alexandre Bayle; Lucas Janson; Lester Mackey (NeurIPS, 2020)

Inference for Batched Bandits. - Kelly W. Zhang; Lucas Janson; Susan A. Murphy (NeurIPS, 2020)

Statistical Inference with M-Estimators on Adaptively Collected Data. - Kelly W. Zhang; Lucas Janson; Susan A. Murphy (NeurIPS, 2021)

Statistical Inference with M-Estimators on Bandit Data. - Kelly W. Zhang; Lucas Janson; Susan A. Murphy (CoRR, 2021)

Tactile Grasp Refinement using Deep Reinforcement Learning and Analytic Grasp Stability Metrics. - Alexander Koenig; Zixi Liu; Lucas Janson; Robert D. Howe (CoRR, 2021)

Exact Asymptotics for Linear Quadratic Adaptive Control. - Feicheng Wang; Lucas Janson (J. Mach. Learn. Res., 2021)

A statistical model for describing and simulating microbial community profiles. - Siyuan Ma; Boyu Ren; Himel Mallick; Yo Sup Moon; Emma H. Schwager; Sagun Maharjan; Timothy L. Tickle; Yiren Lu 003; Rachel N. Carmody; Eric A. Franzosa; Lucas Janson; Curtis Huttenhower (PLoS Comput. Biol., 2021)

The Role of Tactile Sensing in Learning and Deploying Grasp Refinement Algorithms. - Alexander Koenig; Zixi Liu; Lucas Janson; Robert D. Howe (IROS, 2022)

A Simple and Efficient Sampling-based Algorithm for General Reachability Analysis. - Thomas Lew; Lucas Janson; Riccardo Bonalli; Marco Pavone 001 (L4DC, 2022)

Statistical Inference After Adaptive Sampling in Non-Markovian Environments. - Kelly W. Zhang; Lucas Janson; Susan A. Murphy (CoRR, 2022)

Rate-Matching the Regret Lower-Bound in the Linear Quadratic Regulator with Unknown Dynamics. - Feicheng Wang; Lucas Janson (CDC, 2023)

Total Variation Floodgate for Variable Importance Inference in Classification. - Wenshuo Wang 002; Lucas Janson; Lihua Lei; Aaditya Ramdas (ICML, 2024)

Optimal ablation for interpretability. - Maximilian Li; Lucas Janson (NeurIPS, 2024)

A Bayesian Approach to Online Learning for Contextual Restless Bandits with Applications to Public Health. - Biyonka Liang; Lily Xu; Aparna Taneja; Milind Tambe; Lucas Janson (CoRR, 2024)

Evaluating the Effectiveness of Index-Based Treatment Allocation. - Niclas Boehmer; Yash Nair; Sanket Shah; Lucas Janson; Aparna Taneja; Milind Tambe (CoRR, 2024)

SOAP: Improving and Stabilizing Shampoo using Adam. - Nikhil Vyas 001; Depen Morwani; Rosie Zhao; Itai Shapira; David Brandfonbrener; Lucas Janson; Sham M. Kakade (CoRR, 2024)

Stronger Regret Bounds for Safe Online Reinforcement Learning in the Linear Quadratic Regulator. - Benjamin Schiffer; Lucas Janson (CoRR, 2024)

Evaluating Index-based Treatment Allocation in Underresourced Communities. - Niclas Boehmer; Yash Nair; Sanket Shah; Lucas Janson; Aparna Taneja; Milind Tambe (AAAI, 2025)

Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits. - Biyonka Liang; Lily Xu; Aparna Taneja; Milind Tambe; Lucas Janson (AAAI, 2025)

SOAP: Improving and Stabilizing Shampoo using Adam for Language Modeling. - Nikhil Vyas 001; Depen Morwani; Rosie Zhao; Itai Shapira; David Brandfonbrener; Lucas Janson; Sham M. Kakade (ICLR, 2025)

A New Perspective on Shampoo's Preconditioner. - Depen Morwani; Itai Shapira; Nikhil Vyas 001; Eran Malach; Sham M. Kakade; Lucas Janson (ICLR, 2025)

Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate. - Massimo Aufiero; Lucas Janson (SIAM/ASA J. Uncertain. Quantification, 2025)

Foundations of Safe Online Reinforcement Learning in the Linear Quadratic Regulator: √T-Regret. - Benjamin Schiffer; Lucas Janson (CoRR, 2025)

Estimating the Convex Hull of the Image of a Set with Smooth Boundary: Error Bounds and Applications. - Thomas Lew; Riccardo Bonalli; Lucas Janson; Marco Pavone 001 (Discret. Comput. Geom., 2025)