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60 Results
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Workshop
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Describe, Explain, Plan and Select: Interactive Planning with LLMs Enables Open-World Multi-Task Agents Zihao Wang · Shaofei Cai · Guanzhou Chen · Anji Liu · Xiaojian Ma · Yitao Liang |
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Workshop
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EXPLAIN, AGREE and LEARN: A Recipe for Scalable Neural-Symbolic Learning Victor Verreet · Lennert De Smet · Emanuele Sansone |
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Poster
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Thu 13:30 |
Explainability as statistical inference Hugo Senetaire · Damien Garreau · Jes Frellsen · Pierre-Alexandre Mattei |
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Workshop
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Understanding the Size of the Feature Importance Disagreement Problem in Real-World Data Aniek Markus · Egill Fridgeirsson · Jan Kors · Katia Verhamme · Jenna Reps · Peter Rijnbeek |
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Workshop
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GraphChef: Learning the Recipe of Your Dataset Peter Müller · Lukas Faber · Karolis Martinkus · Roger Wattenhofer |
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Workshop
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Data Similarity is Not Enough to Explain Language Model Performance Gregory Yauney · Emily Reif · David Mimno |
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Workshop
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Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage Catherine Huang · Chelse Swoopes · Christina Xiao · Jiaqi Ma · Himabindu Lakkaraju |
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Workshop
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Generalizing Neural Additive Models via Statistical Multimodal Analysis Young Kyung Kim · Juan Di Martino · Guillermo Sapiro |
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Workshop
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Scoring Black-Box Models for Adversarial Robustness |
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Workshop
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Don't trust your eyes: on the (un)reliability of feature visualizations Robert Geirhos · Roland S. Zimmermann · Blair Bilodeau · Wieland Brendel · Been Kim |
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Workshop
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Self-verification improves few-shot clinical information extraction Zelalem Gero · Chandan Singh · Hao Cheng · Tristan Naumann · Michel Galley · Jianfeng Gao · Hoifung Poon |
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Poster
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Thu 16:30 |
Explaining the effects of non-convergent MCMC in the training of Energy-Based Models Elisabeth Agoritsas · Giovanni Catania · Aurélien Decelle · Beatriz Seoane |