Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Gittens, Alex"'
Knowledge graph (KG) completion aims to identify additional facts that can be inferred from the existing facts in the KG. Recent developments in this field have explored this task in the inductive setting, where at test time one sees entities that we
Externí odkaz:
http://arxiv.org/abs/2410.00876
Missing data is commonly encountered in practice, and when the missingness is non-ignorable, effective remediation depends on knowledge of the missingness mechanism. Learning the underlying missingness mechanism from the data is not possible in gener
Externí odkaz:
http://arxiv.org/abs/2409.04407
Autor:
Rathnashyam, Arvind, Gittens, Alex
We derive approximation bounds for learning single neuron models using thresholded gradient descent when both the labels and the covariates are possibly corrupted adversarially. We assume the data follows the model $y = \sigma(\mathbf{w}^{*} \cdot \m
Externí odkaz:
http://arxiv.org/abs/2409.03703
Autor:
Ngweta, Lilian, Agarwal, Mayank, Maity, Subha, Gittens, Alex, Sun, Yuekai, Yurochkin, Mikhail
Large Language Models (LLMs) need to be aligned with human expectations to ensure their safety and utility in most applications. Alignment is challenging, costly, and needs to be repeated for every LLM and alignment criterion. We propose to decouple
Externí odkaz:
http://arxiv.org/abs/2403.04224
Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions. Nevertheless, they are computationally expensive to creat
Externí odkaz:
http://arxiv.org/abs/2308.15027
Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation. Although this task is important for language understanding and knowledge discovery, recent works in this domain have largely
Externí odkaz:
http://arxiv.org/abs/2308.03891
Inference of causal structures from observational data is a key component of causal machine learning; in practice, this data may be incompletely observed. Prior work has demonstrated that adversarial perturbations of completely observed training data
Externí odkaz:
http://arxiv.org/abs/2305.20043
Autor:
Gittens, Alex, Magdon-Ismail, Malik
Given data ${\rm X}\in\mathbb{R}^{n\times d}$ and labels $\mathbf{y}\in\mathbb{R}^{n}$ the goal is find $\mathbf{w}\in\mathbb{R}^d$ to minimize $\Vert{\rm X}\mathbf{w}-\mathbf{y}\Vert^2$. We give a polynomial algorithm that, \emph{oblivious to $\math
Externí odkaz:
http://arxiv.org/abs/2305.07486
Pre-trained contextual language models are ubiquitously employed for language understanding tasks, but are unsuitable for resource-constrained systems. Noncontextual word embeddings are an efficient alternative in these settings. Such methods typical
Externí odkaz:
http://arxiv.org/abs/2304.10642
Learning visual representations with interpretable features, i.e., disentangled representations, remains a challenging problem. Existing methods demonstrate some success but are hard to apply to large-scale vision datasets like ImageNet. In this work
Externí odkaz:
http://arxiv.org/abs/2302.09795