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Found 15 papers, 0 papers with code
Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights and Algorithms
Numerical results confirm the effectiveness of our model and the computational efficacy of algorithms.
Towards Generalized Inverse Reinforcement Learning
This paper studies generalized inverse reinforcement learning (GIRL) in Markov decision processes (MDPs), that is, the problem of learning the basic components of an MDP given observed behavior (policy) that might not be optimal.
Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments
Online learning to rank (ONL2R) is a foundational problem for recommender systems and has received increasing attention in recent years.
Federated Multi-Objective Learning
In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications.
G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer
The detection of the underlying shopping intentions of users based on their historical interactions is a crucial aspect for e-commerce platforms, such as Amazon, to enhance the convenience and efficiency of their customers' shopping experiences.
AdaSelection: Accelerating Deep Learning Training through Data Subsampling
In this paper, we introduce AdaSelection, an adaptive sub-sampling method to identify the most informative sub-samples within each minibatch to speed up the training of large-scale deep learning models without sacrificing model performance.
Multi-Label Learning to Rank through Multi-Objective Optimization
Moreover, it formulates multiple goals that may be conflicting yet important to optimize for simultaneously, e. g., in product search, a ranking model can be trained based on product quality and purchase likelihood to increase revenue.
Bandit Learning with Joint Effect of Incentivized Sampling, Delayed Sampling Feedback, and Self-Reinforcing User Preferences
We show that the delay impacts in both cases can still be upper bounded by an additive penalty on both the regret and total incentive costs.
Incentivized Bandit Learning with Self-Reinforcing User Preferences
In this paper, we investigate a new multi-armed bandit (MAB) online learning model that considers real-world phenomena in many recommender systems: (i) the learning agent cannot pull the arms by itself and thus has to offer rewards to users to incentivize arm-pulling indirectly; and (ii) if users with specific arm preferences are well rewarded, they induce a "self-reinforcing" effect in the sense that they will attract more users of similar arm preferences.
One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning
Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments.
Inverse Multiobjective Optimization Through Online Learning
We study the problem of learning the objective functions or constraints of a multiobjective decision making model, based on a set of sequentially arrived decisions.
Learning Risk Preferences from Investment Portfolios Using Inverse Optimization
Our approach allows the learner to continuously estimate real-time risk preferences using concurrent observed portfolios and market price data.
Wasserstein Distributionally Robust Inverse Multiobjective Optimization
To hedge against the uncertainties in the hypothetical DMP, the data, and the parameter space, we investigate in this paper the distributionally robust approach for inverse multiobjective optimization.
Generalized Inverse Optimization through Online Learning
Inverse optimization is a powerful paradigm for learning preferences and restrictions that explain the behavior of a decision maker, based on a set of external signal and the corresponding decision pairs.
Inferring Parameters Through Inverse Multiobjective Optimization
Given a set of human's decisions that are observed, inverse optimization has been developed and utilized to infer the underlying decision making problem.
