Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Guo, Wenshuo"'
Specifying reward functions for complex tasks like object manipulation or driving is challenging to do by hand. Reward learning seeks to address this by learning a reward model using human feedback on selected query policies. This shifts the burden o
Externí odkaz:
http://arxiv.org/abs/2302.12349
In online marketplaces, customers have access to hundreds of reviews for a single product. Buyers often use reviews from other customers that share their type -- such as height for clothing, skin type for skincare products, and location for outdoor f
Externí odkaz:
http://arxiv.org/abs/2302.09700
Federated learning is typically considered a beneficial technology which allows multiple agents to collaborate with each other, improve the accuracy of their models, and solve problems which are otherwise too data-intensive / expensive to be solved i
Externí odkaz:
http://arxiv.org/abs/2207.04557
The intersection of causal inference and machine learning for decision-making is rapidly expanding, but the default decision criterion remains an \textit{average} of individual causal outcomes across a population. In practice, various operational res
Externí odkaz:
http://arxiv.org/abs/2202.12958
Causal inference from observational datasets often relies on measuring and adjusting for covariates. In practice, measurements of the covariates can often be noisy and/or biased, or only measurements of their proxies may be available. Directly adjust
Externí odkaz:
http://arxiv.org/abs/2202.10665
Auctions with partially-revealed information about items are broadly employed in real-world applications, but the underlying mechanisms have limited theoretical support. In this work, we study a machine learning formulation of these types of mechanis
Externí odkaz:
http://arxiv.org/abs/2202.10606
Autor:
Guo, Wenshuo, Fu, Fang-Wei
This paper presents a key recovery attack on the cryptosystem proposed by Lau and Tan in a talk at ACISP 2018. The Lau-Tan cryptosystem uses Gabidulin codes as the underlying decodable code. To hide the algebraic structure of Gabidulin codes, the aut
Externí odkaz:
http://arxiv.org/abs/2112.15466
We study the problem of learning revenue-optimal multi-bidder auctions from samples when the samples of bidders' valuations can be adversarially corrupted or drawn from distributions that are adversarially perturbed. First, we prove tight upper bound
Externí odkaz:
http://arxiv.org/abs/2107.06259
Autor:
Guo, Wenshuo, Fu, Fang-Wei
This paper presents a new technique for disturbing the algebraic structure of linear codes in code-based cryptography. This is a new attempt to exploit Gabidulin codes in the McEliece setting and almost all the previous cryptosystems of this type hav
Externí odkaz:
http://arxiv.org/abs/2107.03157
Autor:
Guo, Wenshuo, Fu, Fang-Wei
This paper presents two public key cryptosystems based on the so-called expanded Gabidulin codes, which are constructed by expanding Gabidulin codes over the base field. Exploiting the fast decoder of Gabidulin codes, we propose an efficient algorith
Externí odkaz:
http://arxiv.org/abs/2107.01610