Constantinos Daskalakis
Armen Avanessians (1982) Professor
Studies computation theory and its interface with game theory, economics, and machine learning, including multi-agent learning and high-dimensional statistics.
Constantinos Daskalakis's lab at MIT works on learning theory & optimization, reasoning & neuro-symbolic AI and planning, control & multi-robot systems. 32 papers since 2023, 18 at top venues, mostly COLT and STOC. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 56%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 10 papers2026 · T1 4 · collecting
- 7 papers2025 · T1 5
- 8 papers2024 · T1 4
- 7 papers2023 · T1 5
T1 venuesOtherVenues: COLT, STOC, ICML, NeurIPS, AISTATS
Research focuspaper titles that use each topic's words
- Learning theory & optimization12 papers
- Planning, control & multi-robot systems3 papers
- Reasoning & neuro-symbolic AI1 paper
- interface0 papers
- multi-agent learning0 papers
Paper list
From the lab homepage.
202610 papers
- NeurIPS (T1)Ambient Dataloops: Generative Models for Dataset Refinement (opens in a new tab)
- CoRL (T1)Ambient Diffusion Policy: Imitation Learning from Suboptimal Data in Robotics (opens in a new tab)
- Journal of the ACM (JACM), 73(2): 9:1-9:96An Algorithmic Framework for Black-Box Reductions from Bayesian Mechanism Design to Algorithm Design (opens in a new tab)
- NeurIPS (T1)Causal Inference for Sequential Settings under Interference and Latent Confounding (opens in a new tab)
- EC (T1)Diffusion Based Auctions for Multi-Winner Banner Ads (opens in a new tab)
- In the Annual Conference on Learning Theory COLTEstimating Ising Models in Total Variation Distance (opens in a new tab)
- In the Annual Conference on Learning Theory COLTHigh-Accuracy Log-Concave Sampling with Stochastic Queries (opens in a new tab)
- In the International Conference on Machine Learning ICMLHigh-accuracy sampling for diffusion models and log-concave distributions (opens in a new tab)
- In the International Conference on Learning Representations ICLRLearning Correlated Reward Models: Statistical Barriers and Opportunities (opens in a new tab)
- In the ACM Symposium on Theory of Computing STOCProximal Regret and Proximal Correlated Equilibria: A New Tractable Solution Concept for Online Learning and Games (opens in a new tab)
20257 papers
- In the Annual Conference on Neural Information Processing Systems (NeurIPS), NeurIPS . SpotlightAmbient Diffusion Omni: Training Good Models with Bad Data (opens in a new tab)
- In the Annual Conference on Neural Information Processing Systems (NeurIPS), NeurIPS . SpotlightAmbient Proteins: Training Diffusion Models on Low Quality Structures (opens in a new tab)
- STOC (T1)Breaking the T^2/3 Barrier for Sequential Calibration (opens in a new tab)
- STOC (T1)Efficient Learning and Computation of Linear Correlated Equilibrium in General Convex Games (opens in a new tab)
- ICLR (T1)How much is a noisy image worth? Data Scaling Laws for Ambient Diffusion (opens in a new tab)
- ICML (T1)Learning Gaussian DAG Models without Condition Number Bounds (opens in a new tab)
- AISTATS (T1)Learning High-dimensional Gaussians from Censored Data (opens in a new tab)
20248 papers
- In the Annual Conference on Neural Information Processing Systems (NeurIPS), NeurIPS , Creative TrackCharting the Shapes of Stories with Game Theory (opens in a new tab)
- ICML (T1)Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data (opens in a new tab)
- STOC (T1)From External to Swap Regret 2.0: An Efficient Reduction and Oblivious Adversary for Large Action Spaces (opens in a new tab)
- COLT (T1)Is Efficient PAC Learning Possible with an Oracle That Responds "Yes" or "No"? (opens in a new tab)
- In the Annual Conference on Neural Information Processing Systems (NeurIPS), NeurIPSMaximizing utility in multi-agent environments by anticipating the behavior of other learners (opens in a new tab)
- COLT (T1)Near-Optimal Learning and Planning in Separated Latent MDPs (opens in a new tab)
- In the Annual Conference on Neural Information Processing Systems (NeurIPS), NeurIPSOn Tractable Φ-Equilibria in Non-Concave Games (opens in a new tab)
- In the Innovations in Theoretical Computer Science (ITCS), ITCSSmooth Nash Equilibria: Algorithms and Complexity (opens in a new tab)
20237 papers
- In the Annual Conference on Neural Information Processing Systems (NeurIPS), NeurIPSConsistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent (opens in a new tab)
- COLT (T1)Learning and Testing Latent-Tree Ising Models Efficiently (opens in a new tab)
- COLT (T1)Online Learning and Solving Infinite Games with an ERM Oracle (opens in a new tab)
- Operations Research (OR), 71(1): 245-258Sequential Mechanisms with Ex Post Individual Rationality (opens in a new tab)
- COLT (T1)STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games (opens in a new tab)
- COLT (T1)The Complexity of Markov Equilibrium in Stochastic Games (opens in a new tab)
- STOC (T1)What Makes A Good Fisherman? Linear Regression under Self-Selection Bias (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to MITFall 2027
MIT EECS Computer Science PhD
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- Opens
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