Bin Hu
Associate Professor
Studies the intersection of machine learning and control, including robust control for reinforcement learning and optimization algorithms.
Bin Hu's lab at UIUC works on reinforcement learning and learning theory & optimization. 18 papers since 2023, 13 at top venues, mostly NeurIPS and ICLR. 3 PhD students and 2 master's students. 3 alumni, most often to Meta. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 72%
- PhD students
- 3
- MS students
- 2
- alumni listed
- 3
Papers by yearfrom the lab homepage, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 5 papers2025 · T1 4
- 6 papers2024 · T1 4
- 6 papers2023 · T1 4
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, ACC, CDC
Research focuspaper titles that use each topic's words
- Learning theory & optimization4 papers
- Reinforcement learning3 papers
- control3 papers
- robust control0 papers
- optimization algorithms0 papers
Paper list
From the lab homepage.
20261 paper
20255 papers
- ICML (T1)Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching (opens in a new tab)
- ICLR (T1)DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models (opens in a new tab)
- L4DCNeural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems (opens in a new tab)
- NeurIPS (T1)Toward Engineering AGI: Benchmarking the Engineering Design Capabilities of LLMs (opens in a new tab)
- NeurIPS (T1)Two-Stage Learning of Stabilizing Neural Controllers via Zubov Sampling and Iterative Domain Expansion (opens in a new tab)
20246 papers
- ICML (T1)COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability (opens in a new tab)
- ICML (T1)Fine-grained Local Sensitivity Analysis of Standard Dot-Product Self-Attention (opens in a new tab)
- ICLR (T1)Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations (opens in a new tab)
- ICLR (T1)On the Scalability and Memory Efficiency of Semidefinite Programs for Lipschitz Constant Estimation of Neural Networks (opens in a new tab)
- IEEE Control Systems LettersOnline Nonstochastic Control Versus Retrospective Cost Adaptive Control (opens in a new tab)
- CDCStability and Performance Analysis of Discrete-Time ReLU Recurrent Neural Networks (opens in a new tab)
20236 papers
- ICLR (T1)A Unified Algebraic Perspective on Lipschitz Neural Networks (opens in a new tab)
- NeurIPS (T1)Complexity of Derivative-Free Policy Optimization for Structured H-Infinity Control (opens in a new tab)
- NeurIPS (T1)Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability (opens in a new tab)
- NeurIPS (T1)Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models (opens in a new tab)
- ACCLearning the Kalman Filter with Fine-Grained Sample Complexity (opens in a new tab)
- Annual Review of Control, Robotics, and Autonomous SystemsToward a Theoretical Foundation of Policy Optimization for Learning Control Policies (opens in a new tab)
Where alumni went3 listed, 100% to industry
Industry 1Academia 0
Most often to
- Meta
Apply to UIUCFall 2027
UIUC Siebel Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL iBT / IELTS
- Fee
- USD 90
Not yet confirmed for this intake.
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- The lab homepage
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- Lab homepage
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