2 citations · 3 across the 22 of their papers we have counts for
25 papers
Diffusion-Based Auctions for Multi-Winner Banner Ads
Lilian Sun, Henry Huang, Fucheng Warren Zhu +2
We propose a new framework for banner ad auctions, leveraging diffusion models to allow for multiple bidders to co-win a single banner ad opportunity, and in particular influence t…
Normal-Form Correlation in Markov Games
Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina +3
There has been a surge of recent work on correlated equilibrium concepts in Markov games. However, existing results focus on concepts weaker than normal-form correlated equilibria…
Polynomial-time computation of -contraction fixed points for even
Constantinos Daskalakis, Gabriele Farina, Brian Hu Zhang
We give a -time algorithm that computes an -approximate fixed point of any -nonexpansive map , where $\mat…
Causal Inference for Sequential Settings under Interference and Latent Confounding
Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah
We study causal inference under outcome interference for sequential, observational settings. Specifically, we consider settings where the binary outcomes over N units are Markovian…
Ambient Diffusion Policy: Imitation Learning from Suboptimal Data in Robotics
Adam Wei, Nicholas Pfaff, Thomas Cohn +4
We propose Ambient Diffusion Policy, a simple and principled method for imitation learning from suboptimal data in robotics. High-quality, task-specific robot data is expensive and…
High-accuracy log-concave sampling with stochastic queries
Fan Chen, Sinho Chewi, Constantinos Daskalakis +1
We show that high-accuracy guarantees for log-concave sampling -- that is, iteration and query complexities which scale as , where is the desired target…
High-accuracy sampling for diffusion models and log-concave distributions
Fan Chen, Sinho Chewi, Constantinos Daskalakis +1
We present algorithms for diffusion model sampling which obtain -error in steps, given access to -accurate score estimates in . Thi…
Ambient Dataloops: Generative Models for Dataset Refinement
Adrián Rodríguez-Muñoz, William Daspit, Adam Klivans +3
We propose Ambient Dataloops, an iterative framework for refining datasets that makes it easier for diffusion models to learn the underlying data distribution. Modern datasets cont…
Estimating Ising Models in Total Variation Distance
Constantinos Daskalakis, Vardis Kandiros, Rui Yao
We consider the problem of estimating Ising models over variables in Total Variation (TV) distance, given independent samples from the model. While the statistical complexi…
Learning Gaussian DAG Models without Condition Number Bounds
Constantinos Daskalakis, Vardis Kandiros, Rui Yao
We study the problem of learning the topology of a directed Gaussian Graphical Model under the equal-variance assumption, where the graph has nodes and maximum in-degree . P…
Learning Correlated Reward Models: Statistical Barriers and Opportunities
Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Gabriele Farina +1
Random Utility Models (RUMs) are a classical framework for modeling user preferences and play a key role in reward modeling for Reinforcement Learning from Human Feedback (RLHF). H…
DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization
Danial Hosseintabar, Fan Chen, Giannis Daras +2
Diffusion models have emerged as powerful generative priors for high-dimensional inverse problems, yet learning them when only corrupted or noisy observations are available remains…
Ambient Diffusion Omni: Training Good Models with Bad Data
Giannis Daras, Adrian Rodriguez-Munoz, Adam Klivans +2
We show how to use low-quality, synthetic, and out-of-distribution images to improve the quality of a diffusion model. Typically, diffusion models are trained on curated datasets t…
Learning High-dimensional Gaussians from Censored Data
Arnab Bhattacharyya, Constantinos Daskalakis, Themis Gouleakis +1
We provide efficient algorithms for the problem of distribution learning from high-dimensional Gaussian data where in each sample, some of the variable values are missing. We suppo…
Fixed Point Computation: Beating Brute Force with Smoothed Analysis
Idan Attias, Yuval Dagan, Constantinos Daskalakis +2
We propose a new algorithm that finds an -approximate fixed point of a smooth function from the -dimensional unit ball to itself. We use the general framew…
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…