13 citations · 15 across the 7 of their papers we have counts for
7 papers
Enhancing In-context Learning via Linear Probe Calibration
Momin Abbas, Yi Zhou, Parikshit Ram +4
In-context learning (ICL) is a new paradigm for natural language processing that utilizes Generative Pre-trained Transformer (GPT)-like models. This approach uses prompts that incl…
End-to-end Differentiable Clustering with Associative Memories
Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki +1
Clustering is a widely used unsupervised learning technique involving an intensive discrete optimization problem. Associative Memory models or AMs are differentiable neural network…
What Is Missing in IRM Training and Evaluation? Challenges and Solutions
Yihua Zhang, Pranay Sharma, Parikshit Ram +3
Invariant risk minimization (IRM) has received increasing attention as a way to acquire environment-agnostic data representations and predictions, and as a principled solution for…
Toward Theoretical Guidance for Two Common Questions in Practical Cross-Validation based Hyperparameter Selection
Parikshit Ram, Alexander G. Gray, Horst C. Samulowitz +1
We show, to our knowledge, the first theoretical treatments of two common questions in cross-validation based hyperparameter selection: (1) After selecting the best hyperparameter…
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning
Yi Zhou, Parikshit Ram, Theodoros Salonidis +3
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss suRface Aggregation (FLoRA), the fir…
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement
Parikshit Ram, Kaushik Sinha
The mathematical formalization of a neurological mechanism in the olfactory circuit of a fruit-fly as a locality sensitive hash (Flyhash) and bloom filter (FBF) has been recently p…