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Found 15 papers, 3 papers with code
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval
Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems.
Overcoming Prior Misspecification in Online Learning to Rank
The recent literature on online learning to rank (LTR) has established the utility of prior knowledge to Bayesian ranking bandit algorithms.
Advantage Amplification in Slowly Evolving Latent-State Environments
Latent-state environments with long horizons, such as those faced by recommender systems, pose significant challenges for reinforcement learning (RL).
Empirical Bayes Regret Minimization
In this work, we pioneer the idea of algorithm design by minimizing the empirical Bayes regret, the average regret over problem instances sampled from a known distribution.
Deep Structured Prediction with Nonlinear Output Transformations
Deep structured models are widely used for tasks like semantic segmentation, where explicit correlations between variables provide important prior information which generally helps to reduce the data needs of deep nets.
Seq2Slate: Re-ranking and Slate Optimization with RNNs
Ranking is a central task in machine learning and information retrieval.
Planning and Learning with Stochastic Action Sets
From an RL perspective, we show that Q-learning with sampled action sets is sound.
Asynchronous Parallel Coordinate Minimization for MAP Inference
Finding the maximum a-posteriori (MAP) assignment is a central task in graphical models.
Linear-memory and Decomposition-invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes
Moreover, in case the optimal solution is sparse, the new convergence rate replaces a factor which is at least linear in the dimension in previous works, with a linear dependence on the number of non-zeros in the optimal solution.
Smooth and Strong: MAP Inference with Linear Convergence
Maximum a-posteriori (MAP) inference is an important task for many applications.
Train and Test Tightness of LP Relaxations in Structured Prediction
Structured prediction is used in areas such as computer vision and natural language processing to predict structured outputs such as segmentations or parse trees.
Fast and Scalable Structural SVM with Slack Rescaling
We present an efficient method for training slack-rescaled structural SVM.
Learning Max-Margin Tree Predictors
We demonstrate the effectiveness of our approach on several domains and show that, despite the relative simplicity of the structure, prediction accuracy is competitive with a fully connected model that is computationally costly at prediction time.
Convergence Rate Analysis of MAP Coordinate Minimization Algorithms
We also provide a simple dual to primal mapping that yields feasible primal solutions with a guaranteed rate of convergence.
More data means less inference: A pseudo-max approach to structured learning
The problem of learning to predict structured labels is of key importance in many applications.
