Event owner lens
Use this record to benchmark whether a comparable event makes the work after the room visible. The score is 37/100, so the next move is to benchmark the weakest pillars before repeating the format.
mixed-format event agenda agenda analysis
Language Models Can Teach Themselves to Program Better - Workshops
This mixed-format event agenda in Unknown shows 53 visible agenda rows from iclr.cc and scores 37/100: a thin but inspectable design signal. The clearest public signals sit in Learning Transfer and Participation Architecture; the main limits are Follow Through and Network Design. Visible mechanisms include Participant work, Feedback, Network design, and Learning transfer. The public record does not show follow-up or tracking, so the score should be read as design intent rather than durable impact. A practical reading: For a reader, this is a comparison record more than a model to copy: it reads as a mixed-format event agenda, with the strongest visible signal in learning transfer 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 thin outcome architecture band. The strongest visible pillars are Learning Transfer, Participation Architecture, and Personalization; the thinnest visible pillars are Follow Through, Network Design, and Problem Specificity. Visible mechanisms include Participant work, Feedback, Network design, Learning transfer, and Personalization. The extracted agenda preview includes 53 visible rows. The most common formats are Unknown, Workshop, and Presentation; the most common inferred purposes are Unknown, Co Creation, and Knowledge Transfer.
Eight-pillar fingerprint
Hover any pillar to see what it measures and, where it scored low, what the agenda is missing.
Participation Architecture?48
Participation Architecture - 48/100. 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?37
Problem Specificity - 37/100. A clear costly problem, objective, decision, or performance target.Missing: Name the costly problem, decision, or performance target the gathering is meant to move.
Personalization?46
Personalization - 46/100. Role, path, goal, preparation, or connection tailoring for participants.
Network Design?24
Network Design - 24/100. 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?55
Learning Transfer - 55/100. Applied practice, feedback, workplace use, refreshers, and 30-90 day transfer.
Evidence Maturity?41
Evidence Maturity - 41/100. Baseline, comparison, follow-up, isolation, and attribution confidence.
Future-of-Work Fit?43
Future-of-Work Fit - 43/100. 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 (24/100)
Exercises that strengthen it: Fun Trust-Building Group Game for Team Connection - Izzat... · Exciting Group Trust Activity for Building Confidence... · PFP: September 2005
Problem Specificity (37/100)
Exercises that strengthen it: 0 - “Two Person Scene” Theory · Challenge Axis - Knowmium: The Un-Training Consultancy · Fist to Five • WorkshopBank
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
19 percent of the 53 classified blocks put participants to work; the rest broadcast, show, or handle logistics. That mix is what drives the participation score.
Participant workBroadcastShowcaseLogistics
all eventMain ConferenceUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventWorkshopsWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventCommunityUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventSelect Year: (2023)UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventWorkshopWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventBlog Track Poster SessionPoster SessionShowcase+
Format · ShowcasePoster SessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisMediumRead from source
11:30 AM - 1:30 PMMay 2, MH1-2-3-4UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventView full detailsUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventTrustworthy and Reliable Large-Scale Machine Learning ModelsUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:45 AM - 5:30 PMMay 4, AD12UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventShow moreUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventTackling Climate Change with Machine Learning: Global Perspectives and Local ChallengesUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
9:00 AM - 6:45 PMMay 4, AuditoriumUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventTrustworthy Machine Learning for HealthcareUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
9:00 AM - 5:00 PMMay 4, VirtualUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventPhysics for Machine LearningUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
9:00 AM - 6:00 PMMay 4, MH1UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventAI for Agent-Based Modelling (AI4ABM)UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
9:00 AM - 6:00 PMMay 4, AD4UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventReincarnating Reinforcement LearningUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
9:00 AM - 5:00 PMMay 4, AD1UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventMathematical and Empirical Understanding of Foundation Models (ME-FoMo)UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
9:15 AM - 4:45 PMMay 4, AD10UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventNeural Fields across Fields: Methods and Applications of Implicit Neural RepresentationsPresentationKnowledge Transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisMediumRead from source
9:40 AM - 6:05 PMMay 4, MH4UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventNeurosymbolic Generative Models (NeSy-GeMs)UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
10:00 AM - 7:00 PMMay 4, AD11UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventThe Neurosymbolic Generative Models (NeSy-GeMs) workshop at ICLR 2023 aims to bridge the Neurosymbolic AI and Generative Modeling communities, bringing together machine learning, neurosymbolic programming, knowledge representation and reasoning, tractable probabilistic modeling, probabilistic programming, and application researchers to discuss new research directions and define novel open challenges.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventWhat do we need for successful domain generalization?UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventFrom Molecules to Materials: ICLR 2023 Workshop on Machine learning for materials (ML4Materials)WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventScene Representations for Autonomous DrivingPresentationKnowledge Transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisMediumRead from source
8:50 AM - 5:30 PMMay 5, AD1UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventFirst workshop on "Machine Learning & Global Health".WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
9:00 AM - 5:00 PMMay 5, AD10UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventPitfalls of limited data and computation for Trustworthy MLUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
9:00 AM - 6:00 PMMay 5, MH2UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventMachine Learning (ML) algorithms are known to suffer from various issues when it comes to their trustworthiness. This can hinder their deployment in sensitive application domains in practice. But how much of this problem is due to limitations in available data and/or limitations in compute (or memory)? In this workshop, we will look at this question from both a theoretical perspective, to understand where fundamental limitations exist, and from an applied point of view, to investigate which issues we can mitigate by scaling up our datasets and computer architectures.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventICLR 2023 Workshop on Sparsity in Neural Networks: On practical limitations and tradeoffs between sustainability and efficiencyWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
9:00 AM - 7:00 PMMay 5, AD12UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventICLR 2023 Workshop on Machine Learning for Remote SensingWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
9:00 AM - 5:30 PMMay 5, MH3UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventMultimodal Representation Learning (MRL): Perks and PitfallsPresentationKnowledge Transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisMediumRead from source
9:00 AM - 6:00 PMMay 5, VirtualUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventThe 4th Workshop on practical ML for Developing Countries: learning under limited/low resource settingsWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
9:00 AM - 5:00 PMMay 5, MH1UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventPractical Machine Learning for Developing Countries (PML4DC) workshop is a full-day event that has been running regularly for the past 3 years at ICLR (past events include PML4DC 2020, PML4DC 2021 and PML4DC 2022). PML4DC aims to foster collaborations and build a cross-domain community by featuring invited talks, panel discussions, contributed presentations (oral and poster) and round-table mixers.PanelDeliberation+
Format · BroadcastPanelExperts discuss while the audience watches. Surfaces perspective but rarely creates participant work.
Evidence basisMediumRead from source
all event4th Workshop on African Natural Language Processing (AfricaNLP 2023)WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
10:00 AM - 7:00 PMMay 5, AuditoriumUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventTime Series Representation Learning for HealthPresentationKnowledge Transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisMediumRead from source
all eventMachine Learning for Drug Discovery (MLDD)UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventDeep Learning for Code (DL4C)UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventMachine Learning for IoT: Datasets, Perception, and UnderstandingUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventBackdoor Attacks and Defenses in Machine LearningUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
The Full Reading
Why It Ranks This Way +
Calibrated from GES design 37/100 and verified 37/100 with no fourth-loop cap.
Reader Takeaway. For a reader, this is a comparison record more than a model to copy: it reads as a mixed-format event agenda, with the strongest visible signal in learning transfer 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: Learning Transfer, Participation Architecture, and Personalization. Weakest signals: Follow Through, Network Design, and Problem Specificity.
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.
- Name the costly problem, decision, or performance target the gathering is meant to move.
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 iclr.cc. 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, Feedback, Network design, Learning transfer, Personalization.
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.
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_2023_iclr_cc_conferences_2023_language_models_can_teach_themselves_to_program_be_9ee322"><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 Unknown events scored on the same eight pillars.