Filter by pillar, year or venue, or download the table as CSV. Each row links to the paper; the extraction note behind it is in the repository.
| Paper | Year | Venue | Contribution | Methods | Relevance | Tags |
|---|---|---|---|---|---|---|
| Graph-level representation learning with joint-embedding predictive architectures Skenderi G.; Li H.; Tang J.; Cristani M. |
2025 | TMLR | Graph-JEPA: masked sub-graph latent prediction with a hyperbolic coordinate target. | world-model self-supervised hyperbolic-geometry graph-learning | baseline RQ1 RQ4 seed | |
| Ironies of artificial intelligence Endsley M.R. |
2023 | Ergonomics | Updates the ironies of automation for AI: capability growth still erodes human readiness to intervene. | human-factors | RQ3 seed | |
| Two-stage learning to defer with multiple experts Mao A.; Mohri C.; Mohri M.; Zhong Y. |
2023 | NeurIPS | Predictor–rejector template with multiple experts; the formal structure PreCog's rejector follows. | surrogate-loss consistency-proof deferral | baseline RQ1 RQ2 seed | |
| Learning-to-defer for sequential medical decision-making under uncertainty Joshi S.; Parbhoo S.; Doshi-Velez F. |
2021 | arXiv | Extends L2D to sequential settings where deferral should account for future value not per-step loss. | sequential-decision deferral POMDP | baseline RQ2 seed | |
| Consistent estimators for learning to defer to an expert Mozannar H.; Sontag D. |
2020 | ICML | First consistent surrogate loss for learning to defer; the reference L2D formulation. | surrogate-loss consistency-proof deferral | baseline RQ2 seed | |
| Moral crumple zones: cautionary tales in human-robot interaction Elish M.C. |
2019 | Engaging Science Technology and Society | The nearest human absorbs blame for failures of a system they could not meaningfully control. | human-factors ethics | RQ3 RQ5 seed | |
| Measurement of situation awareness in dynamic systems Endsley M.R. |
1995 | Human Factors | Three-level SA model (perception comprehension projection) and the SAGAT freeze-probe method. | SA-measurement human-factors | RQ3 seed | |
| Ironies of automation Bainbridge L. |
1983 | Automatica | The more capable the automation the less practised the human who must take over when it fails. | human-factors | RQ3 seed | |
| Self-supervised learning from images with a joint-embedding predictive architecture Assran M.; Duval Q.; Misra I.; Bojanowski P.; Vincent P.; Rabbat M.; LeCun Y.; Ballas N. |
2023 | CVPR | I-JEPA: the first practical JEPA with an EMA target encoder and masked latent prediction. | world-model self-supervised | RQ1 seed | |
| A path towards autonomous machine intelligence LeCun Y. |
2022 | OpenReview | Proposes JEPA world models that predict in representation space rather than reconstructing inputs. | world-model energy-based | RQ1 seed | |
| Hyperbolic graph convolutional neural networks Chami I.; Ying R.; Ré C.; Leskovec J. |
2019 | NeurIPS | Message passing in hyperbolic space for hierarchical graphs; the encoder family behind the context encoder. | hyperbolic-geometry graph-learning | baseline RQ1 RQ4 seed | |
| Poincaré embeddings for learning hierarchical representations Nickel M.; Kiela D. |
2017 | NeurIPS | Embeds hierarchies in the Poincaré ball; the distance PreCog uses for its safe-manifold energy. | hyperbolic-geometry representation-learning | RQ1 RQ4 seed | |
| Trust in automation: designing for appropriate reliance Lee J.D.; See K.A. |
2004 | Human Factors | Appropriate reliance depends on automation being understandable; over- and under-trust both fail. | human-factors | RQ3 seed | |
| A model for types and levels of human interaction with automation Parasuraman R.; Sheridan T.B.; Wickens C.D. |
2000 | IEEE Trans. SMC-A | Levels-of-automation framework describing how function allocation shifts between human and machine. | human-factors | RQ3 seed | |
| Calibrated learning to defer with one-vs-all classifiers Verma R.; Nalisnick E. |
2022 | ICML | One-vs-all surrogate that yields calibrated deferral probabilities. | surrogate-loss calibration deferral | baseline RQ2 seed | |
| VICReg: variance-invariance-covariance regularization for self-supervised learning Bardes A.; Ponce J.; LeCun Y. |
2022 | ICLR | Variance and covariance regularisers that prevent representational collapse in joint-embedding training. | self-supervised regularisation | RQ1 seed | |
| Predict responsibly: improving fairness and accuracy by learning to defer Madras D.; Pitassi T.; Zemel R. |
2018 | NeurIPS | Frames deferral as a joint objective over the model and a downstream decision-maker. | surrogate-loss deferral | baseline RQ2 seed | |
| Safe reinforcement learning via shielding Alshiekh M.; Bloem R.; Ehlers R.; Könighofer B.; Niekum S.; Topcu U. |
2018 | AAAI | A formal shield blocks unsafe actions at runtime given a temporal-logic specification. | formal-methods temporal-logic shielding | baseline RQ5 seed | |
| Selective classification for deep neural networks Geifman Y.; El-Yaniv R. |
2017 | NeurIPS | Risk-coverage trade-off for abstaining deep classifiers with a guaranteed risk bound. | reject-option risk-bounds | RQ2 seed | |
| A concise introduction to decentralized POMDPs Oliehoek F.A.; Amato C. |
2016 | Springer | Standard treatment of Dec-POMDPs and factored approximations that motivate local sub-graph evaluation. | POMDP factored-models | RQ1 RQ4 seed | |
| The complexity of decentralized control of Markov decision processes Bernstein D.S.; Givan R.; Immerman N.; Zilberstein S. |
2002 | Mathematics of Operations Research | Finite-horizon Dec-POMDPs are NEXP-complete; motivates local factored approximations. | POMDP complexity-theory | RQ1 RQ4 seed | |
| On optimum recognition error and reject tradeoff Chow C.K. |
1970 | IEEE Trans. Information Theory | Origin of the reject option: abstain when the cost of error exceeds the cost of rejection. | reject-option decision-theory | RQ2 seed | |
| Control barrier function based quadratic programs for safety critical systems Ames A.D.; Xu X.; Grizzle J.W.; Tabuada P. |
2017 | IEEE Trans. Automatic Control | Forward invariance of a safe set via barrier functions; the control-theoretic alternative to learned interception. | control-theory barrier-functions | baseline seed |