KDD 2026 - Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recomm. @TheOfficialACM
KDD 2026 - Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recomm.  @TheOfficialACM
Uploaded July 2026 | Updated September 2026, 2 weeks ago
Guoming Li, Shangyu Zhang, Junwei Pan, Wentao Ning, Jin Chen, Gengsheng Xue, Chao Zhou, Shudong Huang, Haijie Gu, Menglin Yang
KDD 2026 - Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recomm.KDD2026-TRACE:Discovering Task-SpecificParameter viaAdaptation-AwareProbing forContinual Fine-TuningKDD 2026 - Defending against Model Extraction for GNNs with Model ReprogrammingCAIS 2026 | Keynote: Percy Liang (Stanford University)Remembering Gordon Bell (Long Version)KDD 2026 - On Model Selection for Time to Event TasksKDD 2026-SOAR: Real-TimeJoint Opti.of Order Allocation andRobot Sched.in Robotic Mobile Fulfill. SysKDD 2026 - A Practical Upper Bound on Selection Bias Effects in Medical Prediction ModelsKDD2026: HGenPush: A HeterogeneousGenerative Recomm. Architecture for Industrial Push Notif. SystemsKDD 2026-Estimating Mutual Info between TimeSeries andTemporal Event Seq. AcrossDiverse Analys.TasksKDD 2026 -Eliciting Frequency-Conditioned Spatial Dynamics for Long-Term Spatio-Temporal ForecastingKDD2026-Can Fine-Tuning Erase Edits? On the Fragile Coexistence of Knowledge Editing and Fine-tuning
Association for Computing Machinery (ACM) |

KDD 2026 - Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recomm.

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