Zobrazeno 1 - 10
of 204
pro vyhledávání: '"OLIVA, P. B."'
We introduce Dynamic Information Sub-Selection (DISS), a novel framework of AI assistance designed to enhance the performance of black-box decision-makers by tailoring their information processing on a per-instance basis. Blackbox decision-makers (e.
Externí odkaz:
http://arxiv.org/abs/2410.23423
Voice conversion (VC) aims at altering a person's voice to make it sound similar to the voice of another person while preserving linguistic content. Existing methods suffer from a dilemma between content intelligibility and speaker similarity; i.e.,
Externí odkaz:
http://arxiv.org/abs/2308.06382
Publikováno v:
Journal of Computational and Graphical Statistics, 2023
Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to supervised learning problems in the biomedical sciences. However, the greater prevalence and complexity of missing d
Externí odkaz:
http://arxiv.org/abs/2207.08911
Modern high-throughput single-cell immune profiling technologies, such as flow and mass cytometry and single-cell RNA sequencing can readily measure the expression of a large number of protein or gene features across the millions of cells in a multi-
Externí odkaz:
http://arxiv.org/abs/2207.00584
Autor:
Strauss, Ryan R., Oliva, Junier B.
Arbitrary conditioning is an important problem in unsupervised learning, where we seek to model the conditional densities $p(\mathbf{x}_u \mid \mathbf{x}_o)$ that underly some data, for all possible non-intersecting subsets $o, u \subset \{1, \dots ,
Externí odkaz:
http://arxiv.org/abs/2201.12414
Autor:
Chowdhury, Somnath Basu Roy, Ghosh, Sayan, Li, Yiyuan, Oliva, Junier B., Srivastava, Shashank, Chaturvedi, Snigdha
Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task. We aim to scrub such undesirable attributes and learn fair re
Externí odkaz:
http://arxiv.org/abs/2109.08613
Truly intelligent systems are expected to make critical decisions with incomplete and uncertain data. Active feature acquisition (AFA), where features are sequentially acquired to improve the prediction, is a step towards this goal. However, current
Externí odkaz:
http://arxiv.org/abs/2107.04163
Autor:
Li, Yang, Oliva, Junier B.
Modeling dependencies among features is fundamental for many machine learning tasks. Although there are often multiple related instances that may be leveraged to inform conditional dependencies, typical approaches only model conditional dependencies
Externí odkaz:
http://arxiv.org/abs/2102.06083
Autor:
Strauss, Ryan R., Oliva, Junier B.
Modeling distributions of covariates, or density estimation, is a core challenge in unsupervised learning. However, the majority of work only considers the joint distribution, which has limited utility in practical situations. A more general and usef
Externí odkaz:
http://arxiv.org/abs/2102.04426
Time series imputation is a fundamental task for understanding time series with missing data. Existing methods either do not directly handle irregularly-sampled data or degrade severely with sparsely observed data. In this work, we reformulate time s
Externí odkaz:
http://arxiv.org/abs/2102.03340