KDD 2026 - Structured Inductive Bias for Multi-timescale Knowledge Tracing @TheOfficialACM
KDD 2026 - Structured Inductive Bias for Multi-timescale Knowledge Tracing @TheOfficialACM
Uploaded July 2026 | Updated September 2026, 2 weeks ago
Xinjia Ou, Tao Huang, Shengze Hu, Huali Yang, Zhuoran Xu, Jing Geng, Yuxia Chen, Junjie Hu
KDD 2026 - Structured Inductive Bias for Multi-timescale Knowledge TracingKDD 2026-BiasMap:Lever. Cross-Attentions toDiscoverandMitigateHiddenSocialBiases inText-to-ImageGen.ByteCast Ep80: Andrew Barto & Richard SuttonPeople of ACM: Julia Gersey (12/18/2025)KDD 2026-FLEDA: Forec.-Based Data PartitionPlacement forEfficient Deep Learning Recom.Model TrainingKDD 2026 - HazardFlow: Enhancing Health Status Representations via Score-based Energy ModelingWhy the Facebook Like Button is a Smart Design - Cliff KuangKDD 2026 - Continuous Causal Component and Structure Discovery from Time SeriesKDD 2026 - BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory ForecastingKDD 2026-AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion NetworkKDD 2026 - Bradley-Terry Rankings for Recommender Systems Across Dataset TaxonomiesKDD 2026 - Causal Representation Learning from Network Data
Association for Computing Machinery (ACM) |

KDD 2026 - Structured Inductive Bias for Multi-timescale Knowledge Tracing

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