activity
20232026
most citedkeqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM

4 citations · 5 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2026

ROAR: Retrieval Opportunity-Aware Refinement for Zero-Shot Time Series Forecasting

Jinjin Chi, Lei Feng, Lulu Zhang +7

Retrieval augmentation provides time series forecasters with historical continuations, yet even candidates that outperform the base forecast may fail to improve the final predictio…

cs.LG2026

When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer

Shuqi Liu, Yuzhou Cao, Lei Feng +2

Learning to Defer (L2D) enables a classifier to abstain from predictions and defer to an expert, and has recently been extended to multi-expert settings. In this work, we show that…

cs.LG2025

Establishing Linear Surrogate Regret Bounds for Convex Smooth Losses via Convolutional Fenchel-Young Losses

Yuzhou Cao, Han Bao, Lei Feng +1

Surrogate regret bounds, also known as excess risk bounds, bridge the gap between the convergence rates of surrogate and target losses. The regret transfer is lossless if the surro…

cs.LG2024

MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading

Chuqiao Zong, Chaojie Wang, Molei Qin +3

High-frequency trading (HFT) that executes algorithmic trading in short time scales, has recently occupied the majority of cryptocurrency market. Besides traditional quantitative t…

cs.CL2023★ 4 cited

keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM

Chaojie Wang, Yishi Xu, Zhong Peng +5

Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, especially for question answering. However, in the face of pr…

cs.LG2023

Regression with Cost-based Rejection

Xin Cheng, Yuzhou Cao, Haobo Wang +3

Learning with rejection is an important framework that can refrain from making predictions to avoid critical mispredictions by balancing between prediction and rejection. Previous…

cs.LG2023★ 1 cited

In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer

Yuzhou Cao, Hussein Mozannar, Lei Feng +2

Enabling machine learning classifiers to defer their decision to a downstream expert when the expert is more accurate will ensure improved safety and performance. This objective ca…