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The Thesis Review Podcast

Each episode of The Thesis Review is a conversation centered around a researcher's PhD thesis, giving insight into their history, revisiting older ideas, and providing a valuable perspective on how their research has evolved (or stayed the same) since.

Episodes

• [16] Aaron Courville | A Latent Cause Theory of Classical Conditioning
  
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• [15] Christian Szegedy | Some Applications of the Weighted Combinatorial Laplacian
  
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• [14] Been Kim | Interactive and Interpretable Machine Learning Models for Human Machine Collaboration
  
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• [13] Adji Bousso Dieng | Deep Probabilistic Graphical Modeling
  
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• [12] Martha White | Regularized Factor Models
  
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• [11] Jacob Andreas | Learning from Language
  
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• [10] Chelsea Finn | Learning to Learn with Gradients
  
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• [09] Kenneth Stanley | Efficient Evolution of Neural Networks through Complexification
  
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• [08] He He | Sequential Decisions and Predictions in Natural Language Processing
  
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• [07] John Schulman | Optimizing Expectations: From Deep RL to Stochastic Computation Graphs
  
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• [06] Yoon Kim | Deep Latent Variable Models of Natural Language
  
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• [05] Julian Togelius | Optimization, Imitation, and Innovation: Computational Intelligence and Games
  
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• [04] Sebastian Nowozin | Learning with Structured Data: Applications to Computer Vision
  
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• [03] Sebastian Ruder | Neural Transfer Learning for Natural Language Processing
  
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• [02] Colin Raffel | Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching
  
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• [01] Gus Xia | Expressive Collaborative Music Performance via Machine Learning
  
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• [00] Introduction | The Thesis Review Podcast
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