Hongning Wang

@feifeiM

Computer Science, Photography, Soccer, IT

cs.virginia.edu/~hw5x/

Joined September 2009

Replies Reposts Media

Hongning Wang
@feifeiM
Apr 1
Lifelong learning is hard, but exciting!
Chengxing Xie
@Chengxing_Xie
Mar 31
Claude Code may be hiding far more than most people realize We analyzed a source snapshot and found evidence of 24+ gated feature systems, including: coordinator mode, forked subagents, agent swarms, Kairos, Auto Dream, and deep MCP/auth infrastructure... The real story
Hongning Wang
@feifeiM
Feb 28, 2024
(1/2) Will humans be replaced by generative AI #Sora #chatgpt4? Our recent work provides some cues from a game theoretical perspective: arxiv.org/abs/2402.15467
1
Hongning Wang
@feifeiM
Feb 28, 2024
Really excited about our recent work on RLHF: from a probability matching perspective, we showed that DPO does mean-seeking, while our EXO does mode-seeking!
Haozhe Ji
@HaozJi
Feb 9, 2024
Introducing EXO, a method towards efficient exact optimization of LM alignment. tldr: EXO is more efficient than RLHF by perfoming probability matching, and exact for arbitary policy parametrization compared to DPO. arxiv: arxiv.org/pdf/2402.00856… repo: github.com/cdjhz/exact-op…
Hongning Wang
@feifeiM
Jul 25, 2023
Welcome to our #ICML2023 oral presentation about a game theoretical analysis of the "invisible hands" in recommender systems! Very insightful and interesting results!
Fan Yao
@FanYao55671545
Jul 25, 2023
#ICML2023 Excited to share our ICML oral paper arxiv.org/pdf/2302.01971… . Our result reveals the economic insight of content creator competition in recommendation platforms, which echos the insight of "invisible hand" from Adam Smith. Welcome to join our oral and poster sessions!
Hongning Wang
@feifeiM
Nov 29, 2022
Want to use generalized linear bandits in a federated learning setting? Come to our #NeurIPS2022 poster #939 at 11am CST and check out our paper: arxiv.org/abs/2202.01087… to find out more details!
arxiv.org
Communication Efficient Federated Learning for Generalized Linear Bandits
Contextual bandit algorithms have been recently studied under the federated learning setting to satisfy the demand of keeping data decentralized and pushing the learning of bandit models to the...