Tutorial Selections for NeurIPS 2020!

  • Abstraction & Reasoning in AI systems: Modern Perspectives
    (Francois Chollet (Google), Melanie Mitchell (Santa Fe Institute), Christian Szegedy (Google))
  • Advances in Approximate Inference
    (Yingzhen Li, Cheng Zhang (Microsoft Research))
  • Beyond Accuracy: Grounding Evaluation Metrics for Human-Machine Learning Systems
    (Praveen Chandar (Spotify), Fernando Diaz (Microsoft Research), Brian St. Thomas (Spotify))
  • Deep Conversational AI
    (Pascale Fung, Zhaojiang Lin andAndrea Madotto (Hong Kong University of Science and Technology))
  • Deep Implicit Layers: Neural ODEs, Equilibrium Models, and Differentiable Optimization
    (David Duvenaud (University of Toronto), Matt Johnson (Google), Zico Kolter (Carnegie Melon University))
  • Designing Learning Dynamics
    (Wojtek Czarnecki, Marta Garnelo and David Balduzzi (DeepMind))
  • Equivariance and Covariance in Deep Neural Networks
    (Taco Cohen (University of Amsterdam) and Risi Kondor (University of Chicago))
  • Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities
    (Hima Lakkaraju (Harvard), Julius Adebayo (MIT), Sameer Singh (UC Irvine))
  • Federated Learning and Analytics: Industry Meets Academia(Peter Kairouz (Google), Brendan McMahan (Google), and Virginia Smith (CMU))
  • Machine Learning for Astrophysics and Astrophysics Problems for Machine Learning
    (David W Hogg, Kate Storey-Fisher (New York University))
  • Offline Reinforcement Learning From Algorithm Design to Practical Applications
    (Sergey Levine and Aviral Kumar (University of Berkley))
  • Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning
    (Dustin Tran, Jasper Snoek, Balaji Lakshminarayanan (Google Brain))
  • RL and Optimization
    (Sham Kakade (University of Washington), Martha White (University of Alberta), Nicolas Le Roux (Google))
  • Sketching and Streaming Algorithms(Jelani Nelson (University of California, Berkeley))
  • The Beautiful Intertwining of Causal Inference, Experimental Design and Reinforcement Learning
    (Susan Murphy (Harvard University))
  • There and Back Again: A Tale of Slopes and Expectations
    (Marc Deisenroth (University College London), Cheng Soon Ong (Data 61, Australian National University))
  • Where Neuroscience meets AI (And What’s in Store for the Future)
    (Jane Wang, Adam Marblestone, Kevin Miller (DeepMind))

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