Search Results for author:
Found 11 papers, 0 papers with code
Trusted Source Alignment in Large Language Models
Large language models (LLMs) are trained on web-scale corpora that inevitably include contradictory factual information from sources of varying reliability.
SMILE: Evaluation and Domain Adaptation for Social Media Language Understanding
We study the ability of transformer-based language models (LMs) to understand social media language.
Firebolt: Weak Supervision Under Weaker Assumptions
In particular, Firebolt learns the class balance and class-specific accuracy of LFs jointly from unlabeled data.
Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error
We study the $L_1$-regularized maximum likelihood estimator/estimation (MLE) problem for discrete Markov random fields (MRFs), where efficient and scalable learning requires both sparse regularization and approximate inference.
Temporal Poisson Square Root Graphical Models
We propose temporal Poisson square root graphical models (TPSQRs), a generalization of Poisson square root graphical models (PSQRs) specifically designed for modeling longitudinal event data.
Ivy: Instrumental Variable Synthesis for Causal Inference
To relax these assumptions, we propose Ivy, a new method to combine IV candidates that can handle correlated and invalid IV candidates in a robust manner.
High-Throughput Machine Learning from Electronic Health Records
For the first time, we can get a much more complete picture of how well risks for thousands of different diagnosis codes can be predicted.
A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications
We present a simple text mining method that is easy to implement, requires minimal data collection and preparation, and is easy to use for proposing ranked associations between a list of target terms and a key phrase.
A Screening Rule for l1-Regularized Ising Model Estimation
We discover a screening rule for l1-regularized Ising model estimation.
An Efficient Pseudo-likelihood Method for Sparse Binary Pairwise Markov Network Estimation
In this way, many insights and optimization procedures for sparse logistic regression can be applied to the learning of discrete Markov networks.
Computational Drug Repositioning Using Continuous Self-controlled Case Series
Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources.
