20:15 Spotlght Talk: Performance Guarantees for Transferring Representations Daniel McNamara, Australian National UniversityFoundations of Machine Learning
https://simons.berkeley.edu/talks/daniel-mcnamara-2017-03-31
47:18 Word Representation Learning without unk Assumptions Chris Dyer, Carnegie Mellon UniversityFoundations of Machine Learning
https://simons.berkeley.edu/talks/chris-dyer-2017-3-31
42:18 Resilient Representation and Provable Generalization Dawn Song, UC BerkeleyFoundations of Machine Learning
https://simons.berkeley.edu/talks/dawn-song-2017-03-31
1:06:38 Learning Representations for Active Vision Bruno Olshausen, UC BerkeleyFoundations of Machine Learning
https://simons.berkeley.edu/talks/bruno-olshausen-2017-3-31
43:17 Formation and Association of Symbolic Memories in the Brain Christos Papadimitriou, UC BerkeleyFoundations of Machine Learning
https://simons.berkeley.edu/talks/christos-papadimitriou-...
39:44 Learning Paraphrastic Representations of Natural Language Sentences Kevin Gimpel, TTI ChicagoFoundations of Machine Learning
https://simons.berkeley.edu/talks/kevin-gimpel-2017-3-31
41:32 Provably Learning of Noisy-or Networks Rong Ge, Duke UniversityFoundations of Machine Learning
https://simons.berkeley.edu/talks/rong-ge-2017-3-30
1:09:16 Unsupervised Representation Learning Yann LeCun, New York UniversityFoundations of Machine Learning
https://simons.berkeley.edu/talks/yann-lecun-2017-3-30
43:28 Generalization and Equilibrium in Generative Adversarial Nets (GANs) Sanjeev Arora, Princeton UniversityFoundations of Machine Learning
https://simons.berkeley.edu/talks/sanjeev-arora-2017-3-30
42:44 Representation Learning of Grounded Language and Knowledge: with and without End-to-End Learning Yejin Choi, University of WashingtonFoundations of Machine Learning
https://simons.berkeley.edu/talks/yejin-choi-2017-3-30
41:54 Re-Thinking Representational Learning in Robotics and Music Sham Kakade, University of WashingtonFoundations of Machine Learning
https://simons.berkeley.edu/talks/sham-kakade-2017-3-30
16:19 Spotlight Talk: How to Escape Saddle Points Efficiently Praneeth Netrapalli, Microsoft Research IndiaFoundations of Machine Learning
https://simons.berkeley.edu/talks/praneeth-netrapalli-201...
40:09 Tractable Learning in Structured Probability Spaces Adnan Darwiche, UCLAFoundations of Machine Learning
https://simons.berkeley.edu/talks/adnan-darwiche-2017-3-29
22:47 Spotlight Talk: Convolutional Dictionary Learning through Tensor Factorization Furong Huang, UC IrvineFoundations of Machine Learning
https://simons.berkeley.edu/talks-furong-huang-2017-03-29
45:01 Representation Learning for Reading Comprehension Russ Salakhutdinov, Carnegie Mellon UniversityFoundations of Machine Learning
https://simons.berkeley.edu/talks/russ-salakhutdinov-2017...
1:04:15 Evaluating Neural Network Representations Against Human Cognition Tom Griffiths, UC BerkeleyFoundations of Machine Learning
https://simons.berkeley.edu/talks/tom-griffiths-2017-3-29
46:57 Adversarial Perceptual Representation Learning Across Diverse Modalities and Domains Trevor Darrell, UC BerkeleyFoundations of Machine Learning
https://simons.berkeley.edu/talks/trevor-darrell-2017-3-29
38:00 Continuous State Machines and Grammars for Linguistic Structure Prediction Noah Smith, University of WashingtonFoundations of Machine Learning
https://simons.berkeley.edu/talks/noah-smith-201
45:44 Failures of Deep Learning Shai Shalev-Shwartz, Hebrew University of JerusalemFoundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2
40:42 Supersizing Self-Supervision: Learning Perception and Action without Human Supervision Abhinav Gupta, Carnegie Mellon UniversityFoundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2
1:11:34 Deep Reinforcement Learning Pieter Abbeel, UC BerkeleyFoundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2
44:54 Learning from Unlabeled Video Kristen Grauman, University of Texas, AustinFoundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2
41:44 Unsupervised Discovery Through Adversarial Self-Play Rob Fergus, New York UniversityFoundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2
18:30 Spotlight Talk: Semi-Random Units for Learning Neural Networks with Guarantees Bo Xie, Georgia Institute of TechnologyFoundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2
17:44 Spotlight Talk: A Formalization of Representation Learning Andrej Risteski, Princeton UniversityFoundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2
42:54 Representations of Relationships in Images and Text Hal Daume, University of Maryland at College ParkFoundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2
59:31 The Missing Signal Leon Bottou, Facebook AI ResearchFoundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2
1:14:06 Representations for Language: From Word Embeddings to Sentence Meanings Christopher Manning, Stanford UniversityFoundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2
36:01 A Non-generative Framework and Convex Relaxations for Unsupervised Learning Elad Hazan, Princeton UniversityFoundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2
49:19 Geometry, Optimization and Generalization in Multilayer Networks Nathan Srebro, TTI ChicagoFoundations of Machine Learning
https://simons.berkeley.edu/workshops/machinelearning2017-2
1:06:03 Understanding Silicon Valley John Markoff, Simons Institute Journalist in Residencehttps://simons.berkeley.edu/events/understanding-silicon-...
John Markoff reported on the emergence of Silicon Valley and has covered tech...
54:29 The Contextual Bandits Problem Robert Schapire, Microsoft ResearchSimons Institute Open Lecture Series
https://simons.berkeley.edu/events/openlectures2017-spring-2
50:20 A Reduction from Efficient Non-Malleable Extractors to Low-Error Two-Source Extractors Dean Doron, Tel Aviv Universityhttps://simons.berkeley.edu/talks/dean-doron-2017-03-10
Proving and Using Pseudorandomness
1:03:53 Randomness Intuition Jacob Fox, Stanford Universityhttps://simons.berkeley.edu/talks/jacob-fox-2017-03-10
Proving and Using Pseudorandomness
49:14 Fast Permutation Property Testing and Metrics of Permutations Fan Wei, Stanford Universityhttps://simons.berkeley.edu/talks/fan-wei-2017-03-10
Proving and Using Pseudorandomness
28:40 Query-to-Communication Lifting for BPP Mika Göös, University of Torontohttps://simons.berkeley.edu/talks/mika-goos-2017-03-09
Proving and Using Pseudorandomness
34:35 Pseudorandom Generators from Pseudorandom Multi-Switching Lemmas Rocco Servedio, Columbia Universityhttps://simons.berkeley.edu/talks/rocco-servedio-2017-03-09
Proving and Using Pseudorandomness
33:34 PRGs for Small Space via Fourier Analysis Thomas Steinke, IBM Almadenhttps://simons.berkeley.edu/talks/thomas-steinke-2017-03-09
Proving and Using Pseudorandomness
32:57 Deterministic Isolation for Bipartite Matching and Matroid Intersection Rohit Gurjar, Tel Aviv Universityhttps://simons.berkeley.edu/talks/rohit-gurjar-2017-03-09
Proving and Using Pseudorandomness
59:44 Derandomizing "Algebraic RL" Michael Forbes, Princeton Universityhttps://simons.berkeley.edu/talks/michael-forbes-2017-03-09
Proving and Using Pseudorandomness
59:55 Linear-Algebraic Pseudorandomness: Subspace Designs and Dimension Expanders Venkatesan Guruswami, Carnegie Mellon Universityhttps://simons.berkeley.edu/talks/venkatesan-guruswami-20...
Proving and Using Pseudorandomness
30:54 Algorithmic Regularity Lemmas and Applications László Miklós Lovász, MIThttps://simons.berkeley.edu/talks/laszlo-miklos-lovasz-20...
Proving and Using Pseudorandomness
49:46 Mixing Implies Lower Bounds for Space Bounded Learning Dana Moshkovitz, University of Texas, Austinhttps://simons.berkeley.edu/talks/dana-moshkovitz-2017-03-08
Proving and Using Pseudorandomness
1:00:26 Preserving Randomness for Adaptive Adversaries Adam Klivans, University of Texas, Austinhttps://simons.berkeley.edu/talks/adam-klivans-2017-03-08
Proving and Using Pseudorandomness
1:04:49 Explicit, Almost Optimal, Epsilon-Balanced Codes Amnon Ta-Shma, Tel Aviv Universityhttps://simons.berkeley.edu/talks/amnon-ta-shma-2017-03-07
Proving and Using Pseudorandomness
1:01:25 Forcing Quasirandomness with Triangles Mathias Schacht, University of Hamburghttps://simons.berkeley.edu/talks/Mathias-Schacht-2017-03-07
Proving and Using Pseudorandomness
57:05 Removal Lemmas with Polynomial Bounds Asaf Shapira, Tel-Aviv Universityhttps://simons.berkeley.edu/talks/asaf-shapira-2017-03-07
Proving and Using Pseudorandomness
36:27 The Number of B_h-sets of a Given Cardinality Yoshiharu Kohayakawa, University of São Paulohttps://simons.berkeley.edu/talks/yoshiharu-kohayakawa-20...
Proving and Using Pseudorandomness
31:43 Regularity Inheritance in Pseudorandom Graphs Maya Stein, University of Chilehttps://simons.berkeley.edu/talks/maya-stein-2017-03-06
Proving and Using Pseudorandomness
1:00:35 Packing Degenerate Graphs Using Pseudorandomness Julia Böttcher, London School of Economicshttps://simons.berkeley.edu/talks/julia-bottcher-2017-03-06
Proving and Using Pseudorandomness
1:00:42 Pseudorandomness in Data Structures Omer Reingold, Stanford Universityhttps://simons.berkeley.edu/talks/omer-reingold-2017-03-06
Proving and Using Pseudorandomness
30:39 Approximately Counting Solutions to Systems of Quadratic Equations Ryan Williams, MIThttps://simons.berkeley.edu/talks/ryan-williams-2017-03-06
Proving and Using Pseudorandomness
34:00 The Uncanny Usefulness of Constructive Proofs of Pseudorandomness Valentine Kabanets, Simon Fraser Universityhttps://simons.berkeley.edu/talks/valentine-kabanets-2017...
Proving and Using Pseudorandomness
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