Zobrazeno 1 - 10
of 305
pro vyhledávání: '"Caramanis, Constantine"'
Autor:
Rout, Litu, Chen, Yujia, Ruiz, Nataniel, Caramanis, Constantine, Shakkottai, Sanjay, Chu, Wen-Sheng
Generative models transform random noise into images; their inversion aims to transform images back to structured noise for recovery and editing. This paper addresses two key tasks: (i) inversion and (ii) editing of a real image using stochastic equi
Externí odkaz:
http://arxiv.org/abs/2410.10792
In many real-world decision problems there is partially observed, hidden or latent information that remains fixed throughout an interaction. Such decision problems can be modeled as Latent Markov Decision Processes (LMDPs), where a latent variable is
Externí odkaz:
http://arxiv.org/abs/2406.01389
Autor:
Rout, Litu, Chen, Yujia, Ruiz, Nataniel, Kumar, Abhishek, Caramanis, Constantine, Shakkottai, Sanjay, Chu, Wen-Sheng
We propose Reference-Based Modulation (RB-Modulation), a new plug-and-play solution for training-free personalization of diffusion models. Existing training-free approaches exhibit difficulties in (a) style extraction from reference images in the abs
Externí odkaz:
http://arxiv.org/abs/2405.17401
Autor:
Rout, Litu, Chen, Yujia, Kumar, Abhishek, Caramanis, Constantine, Shakkottai, Sanjay, Chu, Wen-Sheng
Sampling from the posterior distribution poses a major computational challenge in solving inverse problems using latent diffusion models. Common methods rely on Tweedie's first-order moments, which are known to induce a quality-limiting bias. Existin
Externí odkaz:
http://arxiv.org/abs/2312.00852
In many interactive decision-making settings, there is latent and unobserved information that remains fixed. Consider, for example, a dialogue system, where complete information about a user, such as the user's preferences, is not given. In such an e
Externí odkaz:
http://arxiv.org/abs/2310.07596
Autor:
Caramanis, Constantine, Fotakis, Dimitris, Kalavasis, Alkis, Kontonis, Vasilis, Tzamos, Christos
Deep Neural Networks and Reinforcement Learning methods have empirically shown great promise in tackling challenging combinatorial problems. In those methods a deep neural network is used as a solution generator which is then trained by gradient-base
Externí odkaz:
http://arxiv.org/abs/2310.05309
Autor:
Rout, Litu, Raoof, Negin, Daras, Giannis, Caramanis, Constantine, Dimakis, Alexandros G., Shakkottai, Sanjay
We present the first framework to solve linear inverse problems leveraging pre-trained latent diffusion models. Previously proposed algorithms (such as DPS and DDRM) only apply to pixel-space diffusion models. We theoretically analyze our algorithm s
Externí odkaz:
http://arxiv.org/abs/2307.00619
Autor:
Atsidakou, Alexia, Kveton, Branislav, Katariya, Sumeet, Caramanis, Constantine, Sanghavi, Sujay
We derive the first finite-time logarithmic Bayes regret upper bounds for Bayesian bandits. In a multi-armed bandit, we obtain $O(c_\Delta \log n)$ and $O(c_h \log^2 n)$ upper bounds for an upper confidence bound algorithm, where $c_h$ and $c_\Delta$
Externí odkaz:
http://arxiv.org/abs/2306.09136
Individuals involved in gang-related activity use mainstream social media including Facebook and Twitter to express taunts and threats as well as grief and memorializing. However, identifying the impact of gang-related activity in order to serve comm
Externí odkaz:
http://arxiv.org/abs/2304.11485
This work considers the problem of finding a first-order stationary point of a non-convex function with potentially unbounded smoothness constant using a stochastic gradient oracle. We focus on the class of $(L_0,L_1)$-smooth functions proposed by Zh
Externí odkaz:
http://arxiv.org/abs/2302.06570