Zobrazeno 1 - 10
of 54 885
pro vyhledávání: '"A. 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.g. in certain scientific applications where there is no access to clea
Externí odkaz:
http://arxiv.org/abs/2411.02780
Autor:
Qureshi, Hassan A., Papachatzakis, Michael A., Abdelmagid, Ahmed Gaber, Salomäki, Mikko, Mäkilä, Ermei, Siltanen, Olli, Daskalakis, Konstantinos S.
Planar microcavity polaritons have recently emerged as a promising technology for improving several performance characteristics of organic light-emitting diodes, photodiodes and photovoltaics. To form polaritons and achieve enhanced performance, trad
Externí odkaz:
http://arxiv.org/abs/2410.19392
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/losses of the other agents. Therefore, if an agent is good at anticipating
Externí odkaz:
http://arxiv.org/abs/2407.04889
Autor:
Dagan, Yuval, Daskalakis, Constantinos, Fishelson, Maxwell, Golowich, Noah, Kleinberg, Robert, Okoroafor, Princewill
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 that prediction was made. We study the fundamental problem of online cali
Externí odkaz:
http://arxiv.org/abs/2406.13668
Autor:
Daskalakis, Constantinos, Farina, Gabriele, Golowich, Noah, Sandholm, Tuomas, Zhang, Brian Hu
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 $\epsilon$ average swap regret against an adversary in any extensive-form game withi
Externí odkaz:
http://arxiv.org/abs/2406.13116
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 as well as driving many of the recent empirical successes in deep lea
Externí odkaz:
http://arxiv.org/abs/2406.11667
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 epoch from an unknown mixture of MDPs. To sidestep known impossibilit
Externí odkaz:
http://arxiv.org/abs/2406.07920
Organic light-emitting diodes (OLEDs) have redefined lighting with their environment-friendliness and flexibility. However, only 25 % of the electronic states of organic molecules can emit light upon electrical excitation, limiting the overall effici
Externí odkaz:
http://arxiv.org/abs/2404.04257
Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data. Both Ambient Diffusion and alternative SURE-based approaches for learning diffusion models from corrupted data resort to approximations which deter
Externí odkaz:
http://arxiv.org/abs/2404.10177
While Online Gradient Descent and other no-regret learning procedures are known to efficiently converge to a coarse correlated equilibrium in games where each agent's utility is concave in their own strategy, this is not the case when utilities are n
Externí odkaz:
http://arxiv.org/abs/2403.08171