Statistical Learning
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression
Large language models (LLMs) are known for their exceptional performance in natural language processing, making them highly effective in many human life-related tasks.
Boosting Vision-Language Models with Transduction
Transduction is a powerful paradigm that leverages the structure of unlabeled data to boost predictive accuracy. We present TransCLIP, a novel and computationally efficient transductive approach designed for Vision-Language Models (VLMs).
S-SOS: Stochastic Sum-Of-Squares for Parametric Polynomial Optimization
But it is often the case that we want to find global optima.
On Sampling Strategies for Spectral Model Sharding
The general recipe for each communication round is composed of three steps.
Fairness Without Harm: An Influence-Guided Active Sampling Approach Jinlong Pang
In this work, we aim to train models that mitigate group fairness disparity without causing harm to model accuracy.
Neural Conditional Probability for Uncertainty Quantification Vladimir R. Kostic
We introduce Neural Conditional Probability (NCP), an operator-theoretic approach to learning conditional distributions with a focus on statistical inference tasks.
