Good Papers

Showing Event sequences Show all papers

45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026University College London, UniveUniversity College LondonEvent sequences

From Jumps to Signatures: a Generative Method for Temporal Point Processes

Niels Cariou-Kotlarek, Vasileios Lampos

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026ZhejiangTongjiEvent sequences

MOCHA: Discovering Multi-Order Dynamic Causal Structure in Temporal Point Processes

Yunyang Cao, Juekai Lin, Wenhao Li, Bo Jin

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026TsinghuaU CopenhagenEvent sequences

SurvCancel: A Longitudinal Dataset and Benchmark for Dynamic Order Cancellation Prediction in On-Demand Ride-Sharing Systems

Huayang Liu, Mingyu Zheng, Wei Zhang, Yijun Bian and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026TechnionNew YorkCourant Institute of MathematicaKAISTEvent sequences

Competing Event Models: Next Event Prediction Under Interventions

Yoav Wald, Xiang Gao, Sumit Chopra, Juho Lee and 1 more

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026DKFZSiemens AGGerman Cancer Research CenterEvent sequences

Flexible Intensities Matter: A comprehensive re-evaluation of Classical and Neural Temporal Point Processes

Hendrik Alexander Mehrtens, Florian Buettner, Oliver Stegle

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026U Science and Technology of ChinU Science and Technology of ChinEvent sequences

Stable Partial Order Constraints for Temporal Causal Structure Learning

Changxin Rong, Xiangyu Wang, Taiyu Ban, Yanze Gao and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026PekingTAOBAO & TMALL GROUPThe Chinese University of Hong KAlibaba GroupAlibaba Inc.Event sequences

Learning Deployable Causal Action Geometry under Temporal Non-Stationarity

Changjian Liu, Tianyu Wang, Yuwei Xu, Xiaoxuan Deng and 5 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026CapaloAIThe University of QueenslandEvent sequences

NOCE-Net: Representation Learning for Battery Operational Context via Nested Sequence Modelling

Arnab Bhattacharjee, Wayes Tushar, Tapan K Saha

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026SpotlightApplied AIAPPLIED-AIApplied AI InstituteHigher School of EconomicsSkoltechEvent sequences

Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective

Dmitry Osin, Egor Surkov, Petr Mokrov, Igor Udovichenko and 4 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
71%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026GE HealthCareAmazonGE HealthcareEvent sequences

Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes

Hyper Hawkes processes extend classical Hawkes models via latent spaces and hypernetworks for flexible, interpretable marked temporal point process predictions.

Alex Boyd, andrew warrington, Taha Kass-Hout, Parminder Bhatia and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
7/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 7 of 20 reviewers recommend it
lenient 3/5
medium 4/10
strict 0/5