Event owner lens
Use this record to benchmark whether a comparable event makes the work after the room visible. The score is 42/100, so the next move is to benchmark the weakest pillars before repeating the format.
applied learning or working session agenda analysis
2026 Conference - Workshops at ICLR 2026
This applied learning or working session in Technology / AI / Startup shows 36 visible agenda rows from delta-workshop.github.io and scores 42/100: a moderate design signal with incomplete evidence. The clearest public signals sit in Future-of-Work Fit and Participation Architecture; the main limits are Follow Through and Network Design. Visible mechanisms include Participant work, Network design, and Learning transfer. The public record does not show follow-up or tracking, so the score should be read as design... A practical reading: For a reader, this is a comparison record more than a model to copy: it reads as an applied learning or working session, with the strongest visible signal in future-of-work fit and participation architecture and the biggest open question around follow through and network design. The practical test is whether the published agenda connects the room to post-event continuation and evidence. This page is an original public-evidence analysis, not a copy of the source agenda or an endorsement of the event. The score places the visible agenda in the moderate design signal band. The strongest visible pillars are Future-of-Work Fit, Participation Architecture, and Learning Transfer; the thinnest visible pillars are Follow Through, Network Design, and Personalization. Visible mechanisms include Participant work, Network design, and Learning transfer. The extracted agenda preview includes 60 visible rows. The most common formats are Unknown, Presentation, and Workshop; the most common inferred purposes are Unknown, Knowledge Transfer, and Co Creation.
Eight-pillar fingerprint
Hover any pillar to see what it measures and, where it scored low, what the agenda is missing.
Participation Architecture?58
Participation Architecture - 58/100. Participant work, contribution, interaction, and alternatives to passive broadcast.
Follow Through?5
Follow Through - 5/100. Owners, dates, commitments, progress checks, and accountability after the room.Missing: Add named owners, dates, implementation checkpoints, and a visible post-event continuation path.
Problem Specificity?56
Problem Specificity - 56/100. A clear costly problem, objective, decision, or performance target.
Personalization?37
Personalization - 37/100. Role, path, goal, preparation, or connection tailoring for participants.Missing: Create role-based paths, prepared questions, tailored breakouts, or participant-specific next steps.
Network Design?16
Network Design - 16/100. Structured weak ties, bridge-building, mixers, and relationship persistence.Missing: Replace generic networking blocks with designed introductions, ask-offer exchanges, peer groups, or bridge-building rituals.
Learning Transfer?58
Learning Transfer - 58/100. Applied practice, feedback, workplace use, refreshers, and 30-90 day transfer.
Evidence Maturity?45
Evidence Maturity - 45/100. Baseline, comparison, follow-up, isolation, and attribution confidence.
Future-of-Work Fit?65
Future-of-Work Fit - 65/100. Value against time, hybrid reality, accessibility, AI, and meeting load.
Fix the gaps
Field-tested exercises matched to this agenda's weakest pillars, from the exercise library.
Follow Through (5/100)
Exercises that strengthen it: Mental Toughness Workshop • WorkshopBank · 15% Solutions • WorkshopBank · Network Patches
Network Design (16/100)
Exercises that strengthen it: Fun Trust-Building Group Game for Team Connection - Izzat... · Exciting Group Trust Activity for Building Confidence... · PFP: September 2005
Personalization (37/100)
Exercises that strengthen it: Journey Mapping • WorkshopBank · Group Energiser & Perspective-Changing Exercise - Paradigm... · Photo Storytelling
Agenda Preview
The actual agenda we captured. Every block is classified by format and purpose. Open any block to see how we read it; the colored edge shows whether it is participant work, broadcast, logistics, or a showcase.
Room vs wrapper
35 percent of the 60 classified blocks put participants to work; the rest broadcast, show, or handle logistics. That mix is what drives the participation score.
Participant workBroadcastShowcaseLogistics
all event2nd Workshop on Deep Generative Model in Machine LearningWorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisParticipant work is implied by the formatInferred from format
all event1. Training instability and convergence issues, especially in adversarial settings, can result in unreliable model outputs.TrainingSkill building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisExplicit action or follow-through languageRead from source
all eventThis workshop will center around these challenges, aiming to bring together experts from learning theory and applications.WorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisParticipant work is implied by the formatInferred from format
all eventWorkshop Date: April 27, 2026WorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisParticipant work is implied by the formatInferred from format
all eventSubmissions must follow the DeLTa Workshop style template.WorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisParticipant work is implied by the formatInferred from format
all eventSubmit your paper through the OpenReview platform.PresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
all eventFor inquiries, contact us at delta.workshop.ml@gmail.com.WorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisParticipant work is implied by the formatInferred from format
9:00 to 9:10Opening RemarksOpeningOrientation+
Format · BroadcastOpeningFormat not classified from the source; treated as a broadcast block by default.
Evidence basisNo participant output visible from this rowRead from source, no work signal
9:10 to 9:40René VidalPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
9:40 to 10:10Sitan ChenPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
10:10 to 11:00Poster / BreakBreakShowcase+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisOutcome inferred from formatInferred from format
11:00 to 11:30Nisha ChandramoorthyPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
11:30 to 11:40Oral: Learning Unmasking Policies for Diffusion Language ModelsPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
11:40 to 11:50Oral: Manifold Generalization Provably Proceeds Memorization in Diffusion ModelsPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
11:50 to 12:00Oral: Spectral Condition for µP under Width - Depth ScalingPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
12:00 to 13:3013:30 to 14:00 Atsushi NitandaPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
13:30 to 14:00Atsushi NitandaPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
14:00 to 14:30Zahra KadkhodaiePresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
15:20 to 15:50Arnaud DoucetPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
15:50 to 16:20Jannis ChemseddinePresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
16:20 to 16:30Oral: WiSP-OSch: Solver Within-Step Parallelism and Order Scheduling for Diffusion SamplingPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
16:30 to 16:40Oral: Latent Process Generator MatchingPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
16:40 to 16:50Oral: Query Lower Bounds for Diffusion SamplingPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
16:50 to 17:00Awards & Closing RemarksClosingOrientation+
Format · BroadcastClosingFormat not classified from the source; treated as a broadcast block by default.
Evidence basisNo participant output visible from this rowRead from source, no work signal
all eventHome Speakers Call for Papers Schedule Awards OrganizersUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all event2nd Workshop on Deep Generative Model in Machine Learning:WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventSubmission SiteUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventOn the algorithmic front, DGMs face critical issues related to computational efficiency and scalability. As models grow in complexity and size, they require increasingly large datasets and computational resources, which makes training and deployment a significant challenge.TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all event1. Training instability and convergence issues, especially in adversarial settings, can result in unreliable model outputs.TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all event2. Scaling DGMs for high-resolution or multi-modal data is computationally intensive and often leads to a trade-off between model accuracy and training time.TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventThis workshop will center around these challenges, aiming to bring together experts from learning theory and applications.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventWe are excited to invite submissions to the ICLR 2026 Workshop on Deep Generative Models: Theory, Principle, and Efficacy. This workshop aims to explore challenges and opportunities in advancing the theoretical foundations and practical applications of deep generative models (DGMs).WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventWorkshop Date: April 27, 2026WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventBuilding on the success of the inaugural DeLTa 2025 workshop, DeLTa 2026 expands its scope to address new theoretical and algorithmic frontiers emerging from the rapid evolution of modern deep generative models. Discussions will be organized along two major axes - Theoretical Foundations and Algorithms & Applications.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventOptimization and Convergence in Flow-Matching and Diffusion Models: Analyze training and sampling dynamics under different solvers, discretization schemes, and noise schedules; derive convergence guarantees and variance bounds.TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventPost-Training Theoretical Analysis: Study diffusion model post-training phases (e.g., reward-guided fine-tuning, preference alignment) through the lens of optimization, generalization, and stability.TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventDiffusion Model Post-Training and Adaptation: Design post-training methods for alignment, preference optimization, or reinforcement learning within diffusion frameworks.TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventSubmissions must follow the DeLTa Workshop style template.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventSubmit your paper through the OpenReview platform.UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventFor inquiries, contact us at delta.workshop.ml@gmail.com.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventThis year, ICLR is discontinuing the separate “Tiny Papers” track and instead requires each workshop to accept short (3 to 5 pages in ICLR format, exact page length to be determined by each workshop) paper submissions, with an eye towards inclusion. Authors of these papers will be earmarked for potential funding from ICLR. A separate application for Financial Assistance is required to evaluate eligibility. The application for Financial Assistance will open at the beginning of February and close on March 2, 2025. For more details, visit <https://iclr.cc/Conferences/2025/CallForTinyPapers>.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
9:00 to 9:10Opening RemarksOpening RemarksOrientation+
Format · BroadcastOpening RemarksFraming or welcome from the stage. Orients the room, not participatory.
Evidence basisMediumRead from source
9:10 to 9:40René VidalUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
9:40 to 10:10Sitan ChenUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
10:10 to 11:00Poster / BreakPoster SessionShowcase+
Format · ShowcasePoster SessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisMediumRead from source
11:00 to 11:30Nisha ChandramoorthyUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
11:30 to 11:40Oral: Learning Unmasking Policies for Diffusion Language ModelsUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
11:40 to 11:50Oral: Manifold Generalization Provably Proceeds Memorization in Diffusion ModelsUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
11:50 to 12:00Oral: Spectral Condition for µP under Width - Depth ScalingUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
12:00 to 13:3013:30 to 14:00 Atsushi NitandaUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
13:30 to 14:00Atsushi NitandaUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
14:00 to 14:30Zahra KadkhodaieUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
15:20 to 15:50Arnaud DoucetUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventUnsupervised Source Separation via Generative Modeling Show Abstract The goal of single-channel source separation is to reconstruct \(K\) sources given their mixture. In supervised settings where vast amounts of clean source data are available, this challenging, ill-posed problem has been addressed successfully by generative diffusion and flow-based prior models. However, access to such clean source samples is often limited. To bridge this gap, we present an unsupervised flow matching approach for source separation that learns directly from observed mixtures. This method relies on a novel combination of state-of-the-art supervised flow matching and regression-based self-supervised techniques. We provide insights into the objectives optimized by this approach and demonstrate it on image and audio benchmarks.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
15:50 to 16:20Jannis ChemseddineUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventSpherical Flows for Sampling Discrete Distributions Show Abstract We study generative modeling of discrete sequences in a continuous embedding space. We work on the sphere $\mathbb S^{d-1}$, where the von Mises-Fisher (vMF) distribution can be used to define a natural noise process. Leveraging vMFs radial symmetry, we reduce the continuity equation to a scalar ODE in cosine similarity and derive the conditional velocity. On $(\mathbb S^{d-1})^L$, both the marginal velocity and Riemannian score decompose into posterior-weighted tangent sums differing only by per-token scalars. The posterior is learned via cross-entropy. We sample using the probability flow ODE and a predictor-corrector scheme and compare the vMF path against alternative choices on Sudoku and language data.Poster SessionShowcase+
Format · ShowcasePoster SessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisMediumRead from source
16:20 to 16:30Oral: WiSP-OSch: Solver Within-Step Parallelism and Order Scheduling for Diffusion SamplingUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
16:30 to 16:40Oral: Latent Process Generator MatchingUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
16:40 to 16:50Oral: Query Lower Bounds for Diffusion SamplingUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
16:50 to 17:00Awards & Closing RemarksClosing RemarksOrientation+
Format · BroadcastClosing RemarksClosing framing from the stage. Wraps the event, not participatory.
Evidence basisMediumRead from source
The Full Reading
Why It Ranks This Way +
Calibrated from GES design 42/100 and verified 42/100 with no fourth-loop cap.
Reader Takeaway. For a reader, this is a comparison record more than a model to copy: it reads as an applied learning or working session, with the strongest visible signal in future-of-work fit and participation architecture and the biggest open question around follow through and network design. The practical test is whether the published agenda connects the room to post-event continuation and evidence.
Strongest signals: Future-of-Work Fit, Participation Architecture, and Learning Transfer. Weakest signals: Follow Through, Network Design, and Personalization.
How This Agenda Could Improve +
- Add named owners, dates, implementation checkpoints, and a visible post-event continuation path.
- Replace generic networking blocks with designed introductions, ask-offer exchanges, peer groups, or bridge-building rituals.
- Create role-based paths, prepared questions, tailored breakouts, or participant-specific next steps.
Fastest next move: Add named owners, dated next steps, and a visible continuation path before treating the event as outcome-ready.
Role-Specific Reading +
Sponsor lens
Look beyond exposure. Strong sponsor value would show qualified interaction, problem work, buyer learning, customer evidence, or follow-up. The practical sponsor move is to look for structured introductions, buyer-seller fit, and relationship persistence.
Designer lens
The agenda is useful as a pattern sample from delta-workshop.github.io. Redesign attention should go first to the lowest-scoring pillars; in practice, turn the thinnest agenda blocks into participant work.
Executive lens
Treat the visible agenda as an operating plan. The executive move is to require owners, dates, and evidence before treating the event as strategic. If owners, proof, and follow-through are not visible, the public record does not yet prove strategic movement.
Aggregator lens
Treat the source URL as evidence, not decoration. The data-product move is to label the source boundary clearly before ranking the record before ranking or syndicating the record.
What GES Means Here +
The Gathering Effectiveness Score is a strict 0-100 public-evidence reading of the agenda across eight pillars. It rewards visible participant work, follow-through, transfer, network design, and proof mechanisms more than polish, speaker fame, attendance, or satisfaction.
Visible mechanisms: Participant work, Network design, Learning transfer.
Evidence boundary: Scores reflect visible agenda/source evidence and should not be read as proof of causal event impact.
Limitations, Score Caps, and Review Flags +
Limitations
- No visible follow-up, progress monitoring, or longitudinal tracking.
- No baseline measurement is visible.
Score caps
- No fourth-loop score cap applied.
Review flags
- No source-backed follow-up, validation, baseline, tracking, or impact evidence.
- No tracking, validation, feedback, or impact measurement found in the visible source text.
Is this proof the event worked? +
No. This is a strict public-evidence reading of the agenda. Proof would require baseline, comparison, follow-up, attribution, and impact evidence beyond the listing.
What should a reader inspect first? +
Start with the source URL, then compare the eight pillar scores against the agenda rows. The biggest opportunities usually sit in follow-through, evidence maturity, and participant work.
Why publish weak records? +
Weak records are part of the map. They show where public agendas still describe sessions and speakers more often than outcomes, commitments, transfer, or proof.
How should I use the rows? +
Read the agenda rows as the visible design trace: formats, purposes, and evidence labels show what the public source made inspectable, not everything that happened in the room. This is a source-grounded interpretation of the public agenda record, not a copy of the source, and not an endorsement of the event.
Embed the verified badge +
This record is in the hand-verified gold set. Copy the snippet below to embed the verified badge on your own site.
<a href="https://unitedwetransform.com/events/evt_2026_2026_conference_workshops_at_iclr_2026_jan_13_2026_deep_generative_model_in"><img src="https://unitedwetransform.com/badge/ges-verified.svg" alt="GES verified by United We Transform" height="40"></a>Where To Go Next
Compare this agenda against other Technology / AI / Startup events scored on the same eight pillars.