Jeff Schneider
Research Professor
Studies machine learning for decision making, including Bayesian optimization, reinforcement learning and self-driving vehicles.
Jeff Schneider's lab at CMU works on autonomous vehicles & drones, reinforcement learning and learning theory & optimization. 32 papers since 2023, 26 at top venues, mostly ICRA and ICLR. 13 PhD students and 7 master's students. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 81%
- PhD students
- 13
- MS students
- 7
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 6 papers2026 · T1 6 · collecting
- 8 papers2025 · T1 6
- 9 papers2024 · T1 7
- 9 papers2023 · T1 7
T1 venuesOtherVenues: ICRA, ICLR, ICML, NeurIPS, CoRL
Research focuspaper titles that use each topic's words
- Reinforcement learning16 papers
- Learning theory & optimization16 papers
- Autonomous vehicles & drones1 paper
- bayesian optimization1 paper
- self-driving0 papers
Paper list
From DBLP.
20266 papers
- ICLR (T1)Accelerating Diffusion Planners in Offline RL via Reward-Aware Consistency Trajectory Distillation (opens in a new tab)
- ICLR (T1)Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning (opens in a new tab)
- ICRA (T1)Efficient Active Search via Amortized Path-Integral Policies (opens in a new tab)
- AAMAS (T1)ME-IGM: Individual-Global-Max in Maximum Entropy Multi-Agent Reinforcement Learning (opens in a new tab)
- ICLR (T1)Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning (opens in a new tab)
- ICRA (T1)TADPO: Reinforcement Learning Goes Off-road (opens in a new tab)
20258 papers
- Trans. Mach. Learn. Res.Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts (opens in a new tab)
- NeurIPS (T1)Efficient Bayesian Experiment Design with Equivariant Networks (opens in a new tab)
- NeurIPS (T1)Improving Model-Based Reinforcement Learning by Converging to Flatter Minima (opens in a new tab)
- ICML (T1)Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks (opens in a new tab)
- NeurIPS (T1)Retrospective In-Context Learning for Temporal Credit Assignment with Large Language Models (opens in a new tab)
- Trans. Mach. Learn. Res.State Combinatorial Generalization In Decision Making With Conditional Diffusion Models (opens in a new tab)
- ICML (T1)Training a Generally Curious Agent (opens in a new tab)
- NeurIPS (T1)What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions (opens in a new tab)
20249 papers
- RLJAssigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization (opens in a new tab)
- ICRA (T1)Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents (opens in a new tab)
- CVPR (T1)Diffusion-ES: Gradient-Free Planning with Diffusion for Autonomous and Instruction-Guided Driving (opens in a new tab)
- CoRL (T1)Genetic Algorithm for Curriculum Design in Multi-Agent Reinforcement Learning (opens in a new tab)
- ICML (T1)Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data (opens in a new tab)
- ICLR (T1)Reasoning with Latent Diffusion in Offline Reinforcement Learning (opens in a new tab)
- ICML (T1)Sampling-based Multi-dimensional Recalibration (opens in a new tab)
- ICRA (T1)Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving (opens in a new tab)
- RLJUnifying Model-Based and Model-Free Reinforcement Learning with Equivalent Policy Sets (opens in a new tab)
20239 papers
- ECAICost-Awareness in Multi-Agent Active Search (opens in a new tab)
- ICRA (T1)GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search (opens in a new tab)
- ICML (T1)Learning Temporally AbstractWorld Models without Online Experimentation (opens in a new tab)
- ICRA (T1)Multi-Agent Active Search using Detection and Location Uncertainty (opens in a new tab)
- ICRA (T1)Multi-Alpha Soft Actor-Critic: Overcoming Stochastic Biases in Maximum Entropy Reinforcement Learning (opens in a new tab)
- ICLR (T1)Near-optimal Policy Identification in Active Reinforcement Learning (opens in a new tab)
- L4DCOffline Model-Based Reinforcement Learning for Tokamak Control (opens in a new tab)
- NeurIPS (T1)PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks (opens in a new tab)
- CoRL (T1)Stealthy Terrain-Aware Multi-Agent Active Search (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to CMUFall 2027
CMU SCS Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL / IELTS / Duolingo (F-1/J-1 non-native speakers)
- Fee
- USD 100 (USD 80 by early deadline)
Confirmed on the program page.
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