14 citations · 22 across the 9 of their papers we have counts for
11 papers
Nonmonotone Coderivative-Based Newton Methods for Nonsmooth Optimization with Machine Learning Applications
Sina Kazemdehbashi, Yanchao Liu, Boris S. Mordukhovich
Newton-type algorithms are among the most effective methods for solving optimization problems because of their rapid local convergence. However, extending these methods to nonsmoot…
Can Language Models Learn to Forecast Stock Prices
Jiacheng Guo, Suozhi Huang, Shuzhen Li +11
Post-training has been shown to significantly improve language models' performance on tasks with verifiable outcomes, including mathematical reasoning, software engineering, and co…
AQuA: Recursively Self-Improving Quantitative Trading Research Agents
Jiacheng Guo, Suozhi Huang, Yunlong Gao +5
We study recursive self-improvement at the level of quantitative-investment research: whether an autonomous system can use evidence from earlier experiments to improve the hypothes…
What is the Long-Term Value of Reliability?
Chenyu Qiu, Xu Kuang, Inessa Liskovich +2
We describe Chronos LTV, a system to measure the long-term impact of delays and other service defects on key business metrics. We use Markov decision processes to model customer in…
Prioritizing Recurrent Services
Lin Franklin Feng, Yue Hu, Xu Kuang
We study optimal scheduling in multi-class queueing systems with reentrance, where jobs may return for additional service after completion. Such reentrance creates feedback loops t…
Behavioral Generative Agents for Energy Operations
Cong Chen, Omer Karaduman, Xu Kuang
Problem definition: Accurately modeling consumer behavior in energy operations is challenging due to uncertainty, behavioral heterogeneity, and limited empirical data-particularly…
Learning Service Slowdown using Observational Data
Xu Kuang, Gal Mendelson
Being able to identify service slowdowns is crucial to many operational problems. We study how to use observational congestion data to learn service slowdown in a multi-server syst…
Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling
Zheqing Zhu, Yueyang Liu, Xu Kuang +1
Real-world applications of contextual bandits often exhibit non-stationarity due to seasonality, serendipity, and evolving social trends. While a number of non-stationary contextua…
Load Balancing Using Sparse Communication
Gal Mendelson, Xu Kuang
Load balancing across parallel servers is an important class of congestion control problems that arises in service systems. An effective load balancer relies heavily on accurate, r…
Treatment Effects in Market Equilibrium
Evan Munro, Xu Kuang, Stefan Wager
Policy-relevant treatment effect estimation in a marketplace setting requires taking into account both the direct benefit of the treatment and any spillovers induced by changes to…
Weak Signal Asymptotics for Sequentially Randomized Experiments
Xu Kuang, Stefan Wager
We use the lens of weak signal asymptotics to study a class of sequentially randomized experiments, including those that arise in solving multi-armed bandit problems. In an experim…