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Poster
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Tue 17:00
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Function-Space Regularization in Neural Networks: A Probabilistic Perspective
Tim G. J. Rudner · Sanyam Kapoor · Shikai Qiu · Andrew Wilson
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Poster
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Tue 14:00
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Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
Kyurae Kim · Kaiwen Wu · Jisu Oh · Jacob Gardner
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Oral
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Thu 18:56
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Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
Kyurae Kim · Kaiwen Wu · Jisu Oh · Jacob Gardner
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Workshop
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Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation
Qiwen Cui · Kaiqing Zhang · Simon Du
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Workshop
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BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery
Yashas Annadani · Nick Pawlowski · Joel Jennings · Stefan Bauer · Cheng Zhang · Wenbo Gong
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Poster
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Thu 16:30
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Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks
Louis Bethune · Paul Novello · Guillaume Coiffier · Thibaut Boissin · Mathieu Serrurier · Quentin VINCENOT · Andres Troya-Galvis
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Poster
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Thu 13:30
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Variational Mixture of HyperGenerators for Learning Distributions over Functions
Batuhan Koyuncu · Pablo Sanchez Martin · Ignacio Peis · Pablo Olmos · Isabel Valera
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Workshop
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PITS: Variational Pitch Inference Without Fundamental Frequency for End-to-End Pitch-Controllable TTS
Junhyeok Lee · Wonbin Jung · Hyunjae Cho · Jaeyeon Kim · Jaehwan Kim
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Workshop
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Dimensionality Reduction as Probabilistic Inference
Aditya Ravuri · Francisco Vargas · Vidhi Ramesh · Neil Lawrence
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