Google TechTalks

Google TechTalks

Views:
55,670,645
Subscribers:
348,000
Videos:
2,383
Duration:
82:18:02:36
United States
United States

Google TechTalks is an American YouTube channel which has more than 348 thousand subscribers, with his content totaling around 55.67 million views views across approximately 2.38 thousand videos.

Created on ● Channel Link: https://www.youtube.com/channel/UCtXKDgv1AVoG88PLl8nGXmw



All Videos by Google TechTalks



PublishedVideo TitleDurationViewsCategoryGame
2022-01-07What Could Be the Data-Structures of the Mind?34:002,489
2021-12-21Differential Privacy and the 2020 Census in the United States42:29981
2021-12-21Covariance-Aware Private Mean Estimation Without Private Covariance Estimation52:33419
2021-12-21Private Histograms in the Shuffle Model49:47330
2021-12-14The Platform Design Problem44:001,670
2021-11-19Reducing Polarization and Increasing Diverse Navigability in Graphs40:581,275
2021-10-12Near-Optimal Experimental Design for Networks: Independent Block Randomization57:581,629
2021-10-06Greybeard Qualification (Linux Internals) part 4: Startup and Init50:003,857
2021-10-06Greybeard Qualification (Linux Internals) part 3: Memory Management49:476,884
2021-10-06Greybeard Qualification (Linux Internals) part 5: Block Devices & File Systems59:315,956
2021-10-06Greybeard Qualification (Linux Internals) part 6: Networking & Building a Kernel48:253,820
2021-10-06Greybeard Qualification (Linux Internals) part 2 Execution, Scheduling, Processes & Threads48:066,956
2021-10-06Greybeard Qualification (Linux Internals) part 1: Process Structure and IPC52:5810,879
2021-09-30A Regret Analysis of Bilateral Trade55:56851
2021-09-29CoinPress: Practical Private Mean and Covariance Estimation49:12885
2021-09-29"I need a better description": An Investigation Into User Expectations For Differential Privacy55:42717
2021-09-29On the Convergence of Deep Learning with Differential Privacy47:23991
2021-09-29A Geometric View on Private Gradient-Based Optimization54:14768
2021-09-29BB84: Quantum Protected Cryptography1:01:41863
2021-09-29Leveraging Public Data for Practical Synthetic Data Generation55:07981
2021-09-29Fast and Memory Efficient Differentially Private-SGD via JL Projections53:47579
2021-07-14Efficient Exploration in Bayesian Optimization – Optimism and Beyond by Andreas Krause1:15:194,593
2021-07-14Learning to Explore in Molecule Space by Yoshua Bengio1:05:163,817
2021-07-14Resource Allocation in Multi-armed Bandits by Kirthevasan Kandasamy59:481,402
2021-07-14Grey-box Bayesian Optimization by Peter Frazier1:17:301,962
2021-06-11Is There a Mathematical Model of the Mind? (Panel Discussion)55:352,318Discussion
2021-06-04Dataset Poisoning on the Industrial Scale13:443,391
2021-06-04Lions, Skunks, and Kangaroos: Geo-Distributed Learning on the Flickr-Mammal Dataset15:17329
2021-06-04FL Meets Wireless: More than the sum of its parts10:21369
2021-06-04Towards Training Provably Private Models via Federated Learning in Practice13:27207
2021-06-04Breaking the Communication-Privacy-Accuracy Trilemma20:46364
2021-06-04Federated Design of Compact and Private DNNs17:52203
2021-06-04Private Algorithms with Minimal Space18:52413
2021-06-04Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization16:021,376
2021-06-04Secure Federated Learning in Adversarial Environments15:061,115
2021-06-04Analyzing Model Poisoning Attacks on Federated Learning at Scale8:04755
2021-06-04Cronus: Robust Knowledge Transfer for Federated Learning20:56533
2021-06-04Flower: A Friendly Federated Learning Framework20:254,199
2021-06-04Orchard: Differentially Private Analytics at Scale13:37130
2021-06-04Workshop on Federated Learning & Analytics: Pre-recorded Talks Day 2 Track 1 Q&A Optimization/System58:10200
2021-06-04Workshop on Federated Learning & Analytics: Pre-recorded Talks Day 1 Track 1 Q&A Optimization/System1:00:30370
2021-06-04Attack of the Tails: Yes, you Really can Backdoor Federated Learning12:31695
2021-06-04FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning15:341,730
2021-06-04Privacy-Preserving Analytics on the Edge14:16163
2021-06-04Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogenous Data15:21134
2021-06-04Federated Tensor Factorization10:11568
2021-06-04Learning on Large-Scale Data with Security & Privacy15:24237
2021-06-04Efficient Differentially Private Averaging w Trusted Curator Utility Robustness to Malicious Parties16:3694
2021-06-04Google Workshop on Federated Learning and Analytics: Breakout Session Closing Summaries30:42232
2021-06-04Profile-based Privacy for Locally Private Computations18:46240
2021-06-04Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning11:11486
2021-06-04Google Workshop on Federated Learning and Analytics: Talks from Google Day 248:57107
2021-06-04Federated learning with only positive labels and federated deep retrieval15:14389
2021-06-04Learning discrete distributions: User vs item-level privacy16:32137
2021-06-04Adaptive Federated Optimization12:53555
2021-06-04Domain Compression: A primitive for distributed inference under communication & privacy constraints18:0897
2021-06-04Fault-tolerant federated and distributed learning11:35362
2021-06-04Generative Models for Effective ML on Private, Decentralized Datasets5:28236
2021-06-04PIR-with-Default and Applications16:43128
2021-06-04Federated Analytics based Contact Tracing for COVID-1916:15168
2021-06-04Workshop on Federated Learning and Analytics: Pre-recorded Talks Day 2 Track 2 Q&A Privacy/Security1:05:5274
2021-06-04TensorFlow Federated Tutorial Session3:13:4917,167
2021-06-04Boosted and Differentially Private Ensembles of Decision Trees15:47220
2021-06-04Salvaging Federated Learning: Google Workshop on Federated Learning and Analytics Day 1 Keynote, Q&A44:30280
2021-06-04Google Workshop on Federated Learning and Analytics: Welcome7:38926
2021-06-04Google Workshop on Federated Learning and Analytics: Talks from Google55:15488
2021-06-04On Heterogeneity in Federated Settings: Workshop on Federated Learning and Analytics Day 2 Keynote48:14423
2021-06-04Towards Responsible AI23:41145
2021-06-04Context-Aware Local Differential Privacy13:45629
2021-06-04Semantic Adversarial Attacks for Privacy Protection14:55225
2021-06-04Workshop on Federated Learning & Analytics: Pre-recorded Talks Day 1 Track 2 Q&A Privacy/Security54:18174
2021-06-04How Could the Mind be Storing Information?8:561,655Guide
2021-05-17Towards Reliable Machine Learning via Distributional Robustness56:082,256
2021-05-11Advent of Code: Behind the Scenes35:5920,530
2021-04-222021 Blockly Developer Summit: Test Helpers9:481,068Let's Play
2021-04-222021 Blockly Developer Summit: Metrics Deep Dive7:05574
2021-04-222021 Blockly Developer Summit: Blockly on npm10:263,570
2021-04-222021 Blockly Developer Summit: Operator Precedence13:10781
2021-04-222021 Blockly Developer Summit: Developer Tools6:335,878
2021-04-222021 Blockly Developer Summit: Blockly Roadmap7:551,266
2021-04-222021 Blockly Developer Summit: How to Build a Plugin7:161,262Guide
2021-04-222021 Blockly Developer Summit: Blockly Codelabs4:34686
2021-04-222021 Blockly Developer Summit: Plugins Overview6:571,343
2021-04-222021 Blockly Developer Summit: Toolbox APIs11:082,950
2021-04-222021 Blockly Developer Summit: Blockly Year in Review7:1513,799Review
2021-03-16Managing Digital Accessibility at Universities During the COVID 1947:36946
2021-03-16Auto-Differentiation: At the Intersection of Nifty and Obvious47:03985
2021-03-16Beyond Submodular Maximization via One-Sided Smoothness and Meta-Submodularity53:57561
2021-02-16Statistically Valid Inferences from Privacy Protected Data57:331,323
2021-02-16Two Case Studies in Private Data Analysis55:18617
2021-02-16Uncertainty Estimation via (Multi) Calibration59:35851
2021-02-16(Nearly) Optimal Algorithm for Private Online Learning56:391,056
2021-02-16DP-SGD Privacy Analysis is Tight!48:201,679
2021-02-16Efficient Alternatives to Min-Max Models53:26844
2021-02-16When is Memorization of Entire Examples Necessary for High-Accuracy Learning?59:151,776
2020-09-28Pathways to Innovation: A Fireside Chat with Google Research1:35:436,573
2020-09-13Dominant Truthful Multi-task Peer Prediction Mechanism with a Constant Number of Tasks43:252,209
2020-04-11VR/AR: A Renaissance Art Form1:02:033,591
2020-04-11Complexities of Capacity Management for Distributed Services49:2610,881
2020-03-30Up in the Cloud: Innovation and Solutions for All1:30:111,842