56 citations · 257 across the 54 of their papers we have counts for
6 papers · 1 filter
Scaling Up Temporal Domain Generalization via Temporal Experts Averaging
Aoming Liu, Kevin Miller, Venkatesh Saligrama +4
Temporal Domain Generalization (TDG) aims to generalize across temporal distribution shifts, e.g., lexical change over time. Prior work often addresses this by predicting future mo…
OP-LoRA: The Blessing of Dimensionality
Piotr Teterwak, Kate Saenko, Bryan A. Plummer +1
Low-rank adapters (LoRA) enable finetuning of large models with only a small number of parameters. However, they often suffer from an ill-conditioned loss landscape, leading to dif…
ERM++: An Improved Baseline for Domain Generalization
Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis +2
Domain Generalization (DG) aims to develop classifiers that can generalize to new, unseen data distributions, a critical capability when collecting new domain-specific data is impr…
Explaining Reinforcement Learning Policies through Counterfactual Trajectories
Julius Frost, Olivia Watkins, Eric Weiner +4
In order for humans to confidently decide where to employ RL agents for real-world tasks, a human developer must validate that the agent will perform well at test-time. Some policy…
Anchoring to Exemplars for Training Mixture-of-Expert Cell Embeddings
Siqi Wang, Manyuan Lu, Nikita Moshkov +2
Analyzing the morphology of cells in microscopy images can provide insights into the mechanism of compounds or the function of genes. Addressing this task requires methods that can…
Neural Parameter Allocation Search
Bryan A. Plummer, Nikoli Dryden, Julius Frost +2
Training neural networks requires increasing amounts of memory. Parameter sharing can reduce memory and communication costs, but existing methods assume networks have many identica…