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Ai alignment: A comprehensive survey
AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, so do risks from misalignment. To provide a comprehensive …
AI systems grow more capable, so do risks from misalignment. To provide a comprehensive …
Reimagining multi-criterion decision making by data-driven methods based on machine learning: A literature review
Multi-criterion decision making (MCDM) methods can derive alternative rankings as
solutions to decision-making problems based on survey or historical data about the …
solutions to decision-making problems based on survey or historical data about the …
Preference learning algorithms do not learn preference rankings
Preference learning algorithms (eg, RLHF and DPO) are frequently used to steer LLMs to
produce generations that are more preferred by humans, but our understanding of their …
produce generations that are more preferred by humans, but our understanding of their …
Learning with differentiable pertubed optimizers
Abstract Machine learning pipelines often rely on optimizers procedures to make discrete
decisions (eg, sorting, picking closest neighbors, or shortest paths). Although these discrete …
decisions (eg, sorting, picking closest neighbors, or shortest paths). Although these discrete …
Fast differentiable sorting and ranking
The sorting operation is one of the most commonly used building blocks in computer
programming. In machine learning, it is often used for robust statistics. However, seen as a …
programming. In machine learning, it is often used for robust statistics. However, seen as a …
A survey on multi-label feature selection from perspectives of label fusion
W Qian, J Huang, F Xu, W Shu, W Ding - Information Fusion, 2023 - Elsevier
With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …
multi-label data have become prevalent in various fields. However, these datasets often …
[ספר][B] Active preference-based learning of reward functions
Our goal is to efficiently learn reward functions encoding a human's preferences for how a
dynamical system should act. There are two challenges with this. First, in many problems it is …
dynamical system should act. There are two challenges with this. First, in many problems it is …
Multilevel language and vision integration for text-to-clip retrieval
H Xu, K He, BA Plummer, L Sigal, S Sclaroff… - Proceedings of the …, 2019 - ojs.aaai.org
We address the problem of text-based activity retrieval in video. Given a sentence describing
an activity, our task is to retrieve matching clips from an untrimmed video. To capture the …
an activity, our task is to retrieve matching clips from an untrimmed video. To capture the …
[PDF][PDF] Do we need hundreds of classifiers to solve real world classification problems?
M Fernández-Delgado, E Cernadas, S Barro… - The journal of machine …, 2014 - jmlr.org
We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural
networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging …
networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging …
A survey of preference-based reinforcement learning methods
C Wirth, R Akrour, G Neumann, J Fürnkranz - Journal of Machine Learning …, 2017 - jmlr.org
Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a
suitably chosen reward function. However, designing such a reward function often requires …
suitably chosen reward function. However, designing such a reward function often requires …