Satinder Singh Baveja
Professor, EECS – Computer Science and Engineering
Studies reinforcement learning, including reward design, intrinsic motivation and computational game theory for adaptive agents.
Satinder Singh Baveja's lab at Michigan works on reinforcement learning and learning theory & optimization. 20 papers since 2023, 16 at top venues, mostly NeurIPS and ICLR. 7 PhD students. 18 alumni, most often to Google and Google DeepMind. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 80%
- PhD students
- 7
- MS students
- 0
- alumni listed
- 18
Papers by yearfrom DBLP, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 4 papers2025 · T1 3
- 2 papers2024 · T1 1
- 13 papers2023 · T1 11
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, Artificial Intelligence
Research focuspaper titles that use each topic's words
- Learning theory & optimization9 papers
- Reinforcement learning7 papers
- reward0 papers
- computational game0 papers
- studies reinforcement0 papers
Paper list
From DBLP.
20261 paper
20254 papers
- Nat.Discovering state-of-the-art reinforcement learning algorithms (opens in a new tab)
- NeurIPS (T1)Generating Creative Chess Puzzles (opens in a new tab)
- ICML (T1)Mastering Board Games by External and Internal Planning with Language Models (opens in a new tab)
- NeurIPS (T1)Plasticity as the Mirror of Empowerment (opens in a new tab)
20242 papers
202313 papers
- NeurIPS (T1)A Definition of Continual Reinforcement Learning (opens in a new tab)
- NeurIPS (T1)Combining Behaviors with the Successor Features Keyboard (opens in a new tab)
- ICLR (T1)Composing Task Knowledge With Modular Successor Feature Approximators (opens in a new tab)
- GECCO CompanionDiscovering Evolution Strategies via Meta-Black-Box Optimization (opens in a new tab)
- ICLR (T1)Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality (opens in a new tab)
- ICML (T1)Human-Timescale Adaptation in an Open-Ended Task Space (opens in a new tab)
- ICLR (T1)In-context Reinforcement Learning with Algorithm Distillation (opens in a new tab)
- NeurIPS (T1)Large Language Models can Implement Policy Iteration (opens in a new tab)
- NeurIPS (T1)Optimistic Meta-Gradients (opens in a new tab)
- Trans. Mach. Learn. Res.POMRL: No-Regret Learning-to-Plan with Increasing Horizons (opens in a new tab)
- ICML (T1)ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs (opens in a new tab)
- Artificial Intelligence (T1)Risk-aware analysis for interpretations of probabilistic achievement and maintenance commitments (opens in a new tab)
Where alumni went18 listed, 78% to industry
Industry 7Academia 2
Most often to
- Google DeepMind
- IBM
Also to
- MIT
- Princeton University
Apply to MichiganFall 2027
Michigan CSE Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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