Zobrazeno 1 - 10
of 4 148
pro vyhledávání: '"Been Namkoong"'
Fairness research in machine learning often centers on ensuring equitable performance of individual models. However, real-world recommendation systems are built on multiple models and even multiple stages, from candidate retrieval to scoring and serv
Externí odkaz:
http://arxiv.org/abs/2412.04655
For tabular datasets, the change in the relationship between the label and covariates ($Y|X$-shifts) is common due to missing variables (a.k.a. confounders). Since it is impossible to generalize to a completely new and unknown domain, we study models
Externí odkaz:
http://arxiv.org/abs/2410.07395
Queuing network control determines the allocation of scarce resources to manage congestion, a fundamental problem in manufacturing, communications, and healthcare. Compared to standard RL problems, queueing problems are distinguished by unique challe
Externí odkaz:
http://arxiv.org/abs/2410.06170
As LLMs become capable of complex tasks, there is growing potential for personalized interactions tailored to the subtle and idiosyncratic preferences of the user. We present a public benchmark, PersonalLLM, focusing on adapting LLMs to provide maxim
Externí odkaz:
http://arxiv.org/abs/2409.20296
Queuing network control is essential for managing congestion in job-processing systems such as service systems, communication networks, and manufacturing processes. Despite growing interest in applying reinforcement learning (RL) techniques, queueing
Externí odkaz:
http://arxiv.org/abs/2409.03740
Real-world experiments involve batched & delayed feedback, non-stationarity, multiple objectives & constraints, and (often some) personalization. Tailoring adaptive methods to address these challenges on a per-problem basis is infeasible, and static
Externí odkaz:
http://arxiv.org/abs/2408.04570
Innovations across science and industry are evaluated using randomized trials (a.k.a. A/B tests). While simple and robust, such static designs are inefficient or infeasible for testing many hypotheses. Adaptive designs can greatly improve statistical
Externí odkaz:
http://arxiv.org/abs/2408.04531
Autor:
Ye, Naimeng, Namkoong, Hongseok
Intelligent agents must be able to articulate its own uncertainty. In this work, we show that pre-trained sequence models are naturally capable of probabilistic reasoning over exchangeable data points -- forming informed beliefs and sharpening them a
Externí odkaz:
http://arxiv.org/abs/2408.03307
The performance of ML models degrades when the training population is different from that seen under operation. Towards assessing distributional robustness, we study the worst-case performance of a model over all subpopulations of a given size, defin
Externí odkaz:
http://arxiv.org/abs/2407.01316
To leverage prediction models to make optimal scheduling decisions in service systems, we must understand how predictive errors impact congestion due to externalities on the delay of other jobs. Motivated by applications where prediction models inter
Externí odkaz:
http://arxiv.org/abs/2406.06855