Europe
Data Acquisition via Experimental Design for Data Markets Charles Lu
The acquisition of training data is crucial for machine learning applications.
Regression under demographic parity constraints via unlabeled post-processing
We address the problem of performing regression while ensuring demographic parity, even without access to sensitive attributes during inference. We present a general-purpose post-processing algorithm that, using accurate estimates of the regression function and a sensitive attribute predictor, generates predictions that meet the demographic parity constraint. Our method involves discretization and stochastic minimization of a smooth convex function.
PaDeLLM-NER: Parallel Decoding in Large Language Models for Named Entity Recognition Jinghui Lu1, Ziwei Y ang 1, Y anjie Wang
The main cause of high latency in LLMs is the sequential decoding process, which autoregressively generates all labels and mentions for NER, significantly increase the sequence length.
Verified Safe Reinforcement Learning for Neural Network Dynamic Models
Learning reliably safe autonomous control is one of the core problems in trustworthy autonomy.
State-free Reinforcement Learning
Reinforcement learning (RL) studies the problem where an agent interacts with an unknown environment to optimize cumulative rewards/losses [Sutton and Barto, 2018].
Jun Wang
With the success of deep learning, there are growing concerns over interpretability (Lipton, 2018). Ideally, the explanation should be both faithful (reflecting the model's actual behavior) and plausible