9 papers
Cold-Start Forecasting of New Product Life-Cycles via Conditional Diffusion Models
Ruihan Zhou, Zishi Zhang, Jinhui Han +2
Forecasting the life-cycle trajectory of a newly launched product is important for launch planning, resource allocation, and early risk assessment. This task is especially difficul…
SACRED: A Faithful Annotated Multimedia Multimodal Multilingual Dataset for Classifying Connectedness Types in Online Spirituality
Qinghao Guan, Yuchen Pan, Donghao Li +6
In religion and theology studies, spirituality has garnered significant research attention for the reason that it not only transcends culture but offers unique experience to each i…
Optimal low-rank stochastic gradient estimation for LLM training
Zehao Li, Tao Ren, Zishi Zhang +2
Large language model (LLM) training is often bottlenecked by memory constraints and stochastic gradient noise in extremely high-dimensional parameter spaces. Motivated by empirical…
Nonparametric Bayesian Optimization for General Rewards
Zishi Zhang, Tao Ren, Yijie Peng
This work focuses on Bayesian optimization (BO) under reward model uncertainty. We propose the first BO algorithm that achieves no-regret guarantee in a general reward setting, req…
LLM-Inspired Pretrain-Then-Finetune for Small-Data, Large-Scale Optimization
Zishi Zhang, Jinhui Han, Ming Hu +1
We consider small-data, large-scale decision problems in which a firm must make many operational decisions simultaneously (e.g., across a large product portfolio) while observing o…
RiskPO: Risk-based Policy Optimization via Verifiable Reward for LLM Post-Training
Tao Ren, Jinyang Jiang, Hui Yang +10
Reinforcement learning with verifiable reward has recently emerged as a central paradigm for post-training large language models (LLMs); however, prevailing mean-based methods, suc…
Half-order Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer
Tao Ren, Zishi Zhang, Jingyang Jiang +9
The probabilistic diffusion model (DM), generating content by inferencing through a recursive chain structure, has emerged as a powerful framework for visual generation. After pre-…
FLOPS: Forward Learning with OPtimal Sampling
Tao Ren, Zishi Zhang, Jinyang Jiang +4
Given the limitations of backpropagation, perturbation-based gradient computation methods have recently gained focus for learning with only forward passes, also referred to as quer…
Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection
Zishi Zhang, Yijie Peng
This work aims to improve the sample efficiency of parallel large-scale ranking and selection (R&S) problems by leveraging correlation information. We modify the commonly used "div…