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Found 17 papers, 6 papers with code
Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset
However, these gains in click prediction do not translate to enhanced ranking performance on expert relevance annotations, implying that conclusions strongly depend on how success is measured in this benchmark.
SLIM: Skill Learning with Multiple Critics
As it requires impacting a possibly large set of degrees of freedom composing the environment, mutual information maximization fails alone in producing useful and safe manipulation behaviors.
SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments
Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods.
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression
Motivated by the efficiency of $L_2$-based learning, we propose to jointly learn expectiles and quantiles of the return distribution in a way that allows efficient learning while keeping an estimate of the full distribution of returns.
An Offline Metric for the Debiasedness of Click Models
We prove that debiasedness is a necessary condition for recovering unbiased and consistent relevance scores and for the invariance of click prediction under covariate shift.
Generative Slate Recommendation with Reinforcement Learning
Our findings suggest that representation learning using generative models is a promising direction towards generalizable RL-based slate recommendation.
Offline Evaluation for Reinforcement Learning-based Recommendation: A Critical Issue and Some Alternatives
In this paper, we argue that the paradigm commonly adopted for offline evaluation of sequential recommender systems is unsuitable for evaluating reinforcement learning-based recommenders.
Pareto-Optimal Fairness-Utility Amortizations in Rankings with a DBN Exposure Model
We lay out the structure of a new geometrical object (the DBN-expohedron), and propose for it a Carath\'eodory decomposition algorithm of complexity $O(n^3)$, where $n$ is the number of documents to rank.
Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankings
Such a decomposition makes it possible to express any feasible target exposure as a distribution over at most $n$ rankings.
SmoothI: Smooth Rank Indicators for Differentiable IR Metrics
To address this shortcoming, we propose SmoothI, a smooth approximation of rank indicators that serves as a basic building block to devise differentiable approximations of IR metrics.
Real-Time Optimization Of Web Publisher RTB Revenues
The real-world challenges we had to tackle consist mainly of tracking the dependencies on both the user and placement in an highly non-stationary environment and of dealing with censored bid observations.
Modeling ASR Ambiguity for Dialogue State Tracking Using Word Confusion Networks
Spoken dialogue systems typically use a list of top-N ASR hypotheses for inferring the semantic meaning and tracking the state of the dialogue.
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling
We consider the problem of Active Search, where a maximum of relevant objects - ideally all relevant objects - should be retrieved with the minimum effort or minimum time.
Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting
We consider the optimal value of information (VoI) problem, where the goal is to sequentially select a set of tests with a minimal cost, so that one can efficiently make the best decision based on the observed outcomes.
Joint Event Detection and Entity Resolution: a Virtuous Cycle
Clustering web documents has numerous applications, such as aggregating news articles into meaningful events, detecting trends and hot topics on the Web, preserving diversity in search results, etc.
LSTM-based Mixture-of-Experts for Knowledge-Aware Dialogues
We introduce an LSTM-based method for dynamically integrating several word-prediction experts to obtain a conditional language model which can be good simultaneously at several subtasks.
Task-Driven Linguistic Analysis based on an Underspecified Features Representation
In this paper we explore a task-driven approach to interfacing NLP components, where language processing is guided by the end-task that each application requires.
