2 citations · 3 across the 22 of their papers we have counts for
9 papers · 1 filter
Efficient Learning and Computation of Linear Correlated Equilibrium in General Convex Games
Constantinos Daskalakis, Gabriele Farina, Maxwell Fishelson +2
We propose efficient no-regret learning dynamics and ellipsoid-based methods for computing linear correlated equilibria$\unicode{x2014}$a relaxation of correlated equilibria and a…
How much is a noisy image worth? Data Scaling Laws for Ambient Diffusion
Giannis Daras, Yeshwanth Cherapanamjeri, Constantinos Daskalakis
The quality of generative models depends on the quality of the data they are trained on. Creating large-scale, high-quality datasets is often expensive and sometimes impossible, e.…
The Conflict Graph Design: Estimating Causal Effects under Arbitrary Neighborhood Interference
Vardis Kandiros, Charilaos Pipis, Constantinos Daskalakis +1
A fundamental problem in network experiments is selecting an appropriate experimental design in order to precisely estimate a given causal effect of interest. In this work, we prop…
Maximizing utility in multi-agent environments by anticipating the behavior of other learners
Angelos Assos, Yuval Dagan, Constantinos Daskalakis
Learning algorithms are often used to make decisions in sequential decision-making environments. In multi-agent settings, the decisions of each agent can affect the utilities/losse…
A Lower Bound on Swap Regret in Extensive-Form Games
Constantinos Daskalakis, Gabriele Farina, Noah Golowich +2
Recent simultaneous works by Peng and Rubinstein [2024] and Dagan et al. [2024] have demonstrated the existence of a no-swap-regret learning algorithm that can reach average sw…
Near-Optimal Learning and Planning in Separated Latent MDPs
Fan Chen, Constantinos Daskalakis, Noah Golowich +1
We study computational and statistical aspects of learning Latent Markov Decision Processes (LMDPs). In this model, the learner interacts with an MDP drawn at the beginning of each…
Breaking the Barrier for Sequential Calibration
Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson +3
A set of probabilistic forecasts is calibrated if each prediction of the forecaster closely approximates the empirical distribution of outcomes on the subset of timesteps where tha…
Is Efficient PAC Learning Possible with an Oracle That Responds 'Yes' or 'No'?
Constantinos Daskalakis, Noah Golowich
The empirical risk minimization (ERM) principle has been highly impactful in machine learning, leading both to near-optimal theoretical guarantees for ERM-based learning algorithms…
Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data
Giannis Daras, Alexandros G. Dimakis, Constantinos Daskalakis
Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data. Both Ambient Diffusion and alternative SURE-based approaches for learning dif…