Introduces Maximal Coding Rate Reduction (MCR²) principle to maximize the difference between total and class-specific coding rates, enhancing discriminability and diversity.

Yaodong Yu, Kwan Ho Ryan Chan, Chong You et al.

Proposes assumption-free inference for GLM parameters using influence functions, enabling robust estimation under model misspecification.

Stijn Vansteelandt, Oliver Dukes

Pretrained FinBERT on 4.9B tokens, outperforming generic BERT in financial sentiment tasks with up to 15.6% accuracy gain.

Yi Yang, Mark Christopher Siy UY, Allen Huang

Introduces adaptive GPR-GNN with learnable weights, improving node classification on both homophilic and heterophilic graphs.

Eli Chien, Jianhao Peng, Pan Li et al.

BYOL employs dual networks with a momentum target to learn image representations without negative samples, achieving 74.3% top-1 accuracy on ImageNet.

Jean-Bastien Grill, Florian Strub, Florent Altché et al.

Proposes PELEG, an explicit algorithm achieving instance-optimal sample complexity in linear bandits via no-regret strategies.

Mohammadi Zaki, Avi Mohan, Aditya Gopalan

FrugalML framework optimizes API usage to achieve higher accuracy and significant cost savings within budget constraints.

Lingjiao Chen, Matei Zaharia, James Zou

Ansor uses hierarchical search combined with evolutionary algorithms to generate high-performance tensor programs, achieving up to 3.8× speedup.

Lianmin Zheng, Chengfan Jia, Minmin Sun et al.

Proposed adaptive discriminator augmentation stabilizes GAN training with limited data, reducing FID from 5.59 to 2.42.

Tero Karras, Miika Aittala, Janne Hellsten et al.

ETHOS dataset offers hate speech detection based on YouTube and Reddit comments, using binary and multi-label classification.

Ioannis Mollas, Zoe Chrysopoulou, Stamatis Karlos et al.

Weighted split-CQR delivers finite-sample or doubly robust coverage for counterfactual and ITE intervals.

Lihua Lei, Emmanuel J. Candès

Proposes Search-based Interest Model (SIM) with two-stage search for long user behavior sequences, boosting CTR prediction accuracy.

Pi Qi, Xiaoqiang Zhu, Guorui Zhou et al.

MeshWalker learns 3D shapes directly from triangular meshes via random walks, enhancing classification and segmentation performance.

Alon Lahav, Ayellet Tal

Linformer reduces self-attention complexity from O(n²) to O(n) using low-rank approximation, enabling efficient long-sequence modeling.

Sinong Wang, Belinda Z. Li, Madian Khabsa et al.

Introduces recursive expected free energy framework for hierarchical belief-based active inference, enabling deep future action planning via belief about beliefs modeling.

Karl Friston, Lancelot Da Costa, Danijar Hafner et al.

Proposed Visual Transformer (VT) with 16 semantic tokens improves ImageNet top-1 accuracy by 4.6-7 points, reduces FLOPs by 6.5x.

Bichen Wu, Chenfeng Xu, Xiaoliang Dai et al.

Performer with FAVOR achieves linear complexity in long protein sequence modeling, enabling efficient large-scale biological analysis.

Krzysztof Choromanski, Valerii Likhosherstov, David Dohan et al.

Shared grasping optimizes grasp via environmental contacts, using hybrid force-velocity control for stability.

Yifan Hou, Zhenzhong Jia, Matthew T. Mason

Leveraging DNN inference determinism, Clockwork achieves end-to-end predictable low-latency model serving.

Arpan Gujarati, Reza Karimi, Safya Alzayat et al.

Double GAN-based conditional independence test controls Type I error and detects dependencies effectively in high-dimensional data.

Chengchun Shi, Tianlin Xu, Wicher Bergsma et al.