5 citations · 15 across the 24 of their papers we have counts for
6 papers · 1 filter
ExpertMuon-Compass: Alignment-Guided Step Sizes for Mixture-of-Experts Training
Omatharv Bharat Vaidya, Ashwin Vinod, Pedram Akbarian +3
Mixture-of-experts (MoE) language models send each token to a few experts, so each expert is trained on a different part of the data, and this part changes during training. With a…
Queryable LoRA: Instruction-Regularized Routing Over Shared Low-Rank Update Atoms
Omatharv Bharat Vaidya, Connor T. Jerzak, Nhat Ho +1
We present a data-adaptive method for parameter-efficient fine-tuning of large neural networks. Standard low-rank adaptation methods improve efficiency by restricting each layer up…
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty
Kazuki Sakamoto, Connor T. Jerzak, Adel Daoud
Earth observation (EO) data such as satellite imagery can have far-reaching impacts on our understanding of the geography of poverty, especially when coupled with machine learning…
CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images
Connor T. Jerzak, Adel Daoud
The causalimages R package enables causal inference with image and image sequence data, providing new tools for integrating novel data sources like satellite and bio-medical imager…
Image-based Treatment Effect Heterogeneity
Connor T. Jerzak, Fredrik Johansson, Adel Daoud
Randomized controlled trials (RCTs) are considered the gold standard for estimating the average treatment effect (ATE) of interventions. One use of RCTs is to study the causes of g…
Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa
Connor T. Jerzak, Fredrik Johansson, Adel Daoud
Observational studies of causal effects require adjustment for confounding factors. In the tabular setting, where these factors are well-defined, separate random variables, the eff…