Plackett-Luce model
PulseAugur coverage of Plackett-Luce model — every cluster mentioning Plackett-Luce model across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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New Hierarchical Models Enhance Rank Aggregation for Grouped Data · 2 sources tracked
Researchers have introduced Hierarchical Partial-Order (HPO) models, an extension of existing rank aggregation techniques designed to handle grouped data with latent hierarchical structures. These models build upon part…
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New Soft-Rank Diffusion Model Enhances Permutation Learning
Researchers have developed a new diffusion model called Soft-Rank Diffusion for learning probability distributions on permutations. This method improves upon existing techniques by using a soft-rank forward process, whi…
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New Mult-DPO method aligns LLMs for recommender systems
Researchers have developed Mult-DPO, a new method for aligning large language models with recommender systems. Traditional DPO methods rely on pairwise preferences, which are not suitable for the set-wise feedback commo…
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New algorithm uses conversational queries for personalized multi-objective bandits
Researchers have developed a new algorithm, MO-PQUCB, designed to improve personalized decision-making in multi-objective bandit problems. This algorithm uniquely leverages proactive conversational queries from users, s…
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New credit-assigned policy gradient method improves retrieval system training
Researchers have developed a new reinforcement learning method called "credit-assigned" policy gradient (CA-PG) to address challenges in training early-stage rankers (ESRs) for large-scale retrieval systems. Traditional…
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New method MASS-DPO improves language model training with efficient sample selection
Researchers have developed MASS-DPO, a new method for Direct Preference Optimization (DPO) that efficiently selects informative negative samples for training language models. This approach uses a PL-specific Fisher-info…