Search Results for author:
Found 5 papers, 3 papers with code
JumpCoder: Go Beyond Autoregressive Coder via Online Modification
While existing code large language models (code LLMs) exhibit impressive capabilities in code generation, their autoregressive sequential generation inherently lacks reversibility.
ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt
Based on this framework, we propose a prompt-based transferability test to find the most relevant pretext task in order to reduce the semantic gap.
Calibration of Time-Series Forecasting Transformers: Detecting and Adapting Context-Driven Distribution Shift
In this paper, we introduce a universal calibration methodology for the detection and adaptation of CDS with a trained Transformer model.
Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank
Unbiased Learning to Rank (ULTR) aims to train unbiased ranking models from biased click logs, by explicitly modeling a generation process for user behavior and fitting click data based on examination hypothesis.
Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank
Most of the current ULTR methods are based on the examination hypothesis (EH), which assumes that the click probability can be factorized into two scalar functions, one related to ranking features and the other related to bias factors.
