_id,doi,title
14320,10.1103/physrevb.108.125411,Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene
14410,10.1007/978-3-031-40773-4_6,"On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift"
14446,10.2478/msr-2023-0023,Against the flow of time with multi-output models
14771,10.1109/cvpr52729.2023.02334,Bias in pruned vision models: In-depth analysis and countermeasures
14921,,Deep neural collapse is provably optimal for the deep unconstrained features model
15039,10.48550/ARXIV.2311.06103,1-Lipschitz neural networks are more expressive with N-activations
13053,,CrAM: A Compression-Aware Minimizer
13074,10.15479/at:ista:13074,Efficiency and generalization of sparse neural networks
10752,10.1109/bigdata52589.2021.9672003,Overcoming rare-language discrimination in multi-lingual sentiment analysis
10799,10.15479/at:ista:10799,Robustness and fairness in machine learning
10802,,Fairness-aware PAC learning from corrupted data
13241,,On the impossibility of fairness-aware learning from corrupted data
11839,10.1007/978-3-031-19803-8_21,Almost-orthogonal layers for efficient general-purpose Lipschitz networks
12161,10.1109/icpr56361.2022.9956195,Lightweight conditional model extrapolation for streaming data under class-prior shift
12299,10.1109/cvpr52688.2022.01195,How well do sparse ImageNet models transfer?
12495,,FLEA: Provably robust fair multisource learning from unreliable training data
12660,10.48550/arXiv.2210.06434,Cross-client Label Propagation for transductive federated learning
12662,10.48550/arXiv.2208.13499,Generalization in Multi-objective machine learning
10803,10.48550/arXiv.2102.05996,Fairness through regularization for learning to rank
14987,10.1007/978-3-030-63416-2_874,Zero-Shot Learning