| 101. | Stochastic Gradient MCMC for Independent and Dependent Data Sources | 113 | |
|
| 102. | Real-Time Convex Optimization | 111 | |
|
| 103. | Adversarial Examples in Deep Learning | 110 | |
|
| 104. | Geometric Aspects of Sampling and Optimization | 110 | |
|
| 105. | Markov Chain Mixing Times and Applications I | 110 | |
|
| 106. | Build an Ecosystem, Not a Monolith | 108 | |
|
| 107. | SAT-Solving | 106 | |
|
| 108. | Distributional Robustness, Learning, and Empirical Likelihood | 106 | |
|
| 109. | On the Foundations of Deep Learning: SGD, Overparametrization, and Generalization | 106 | |
|
| 110. | Optimal Power Flow: Relaxation, Online Algorithm, Fast Dynamics | 105 | |
|
| 111. | Sparks of Artificial General Intelligence | 105 | |
|
| 112. | Watermarking of Large Language Models | 104 | |
|
| 113. | The Mathematics of Lattices II | 104 | |
|
| 114. | Optimization Crash Course | 104 | |
|
| 115. | PBFT and Blockchains | 104 | |
|
| 116. | Statistical Inference I | 102 | |
|
| 117. | An Integrated Cognitive Architecture | 102 | |
|
| 118. | Why are Many-Body Problems in Physics so Difficult? | 102 | |
|
| 119. | Mad Max: Affine Spline Insights into Deep Learning | 100 | |
|
| 120. | Flexible Neural Networks and the Frontiers of Meta-Learning | 100 | |
|
| 121. | Thalamocortical System I | 100 | |
|
| 122. | Theory of Computation I | 99 | |
|
| 123. | A Brief History of Practical Garbled Circuit Optimizations | 99 | |
|
| 124. | Classical Shadows of Quantum States | Quantum Colloquium | 99 | |
|
| 125. | The Learning With Errors Problem and Cryptographic Applications | 98 | |
|
| 126. | Online Learning and Online Convex Optimization I | 97 | |
|
| 127. | Berkeley in the 80s, Episode 1: Shafi Goldwasser | 97 | Show |
|
| 128. | Recent Progress in High-Dimensional Learning | 96 | |
|
| 129. | Introduction to Quantum Chemistry | 95 | |
|
| 130. | User-Friendly Tools for Random Matrices I | 95 | |
|
| 131. | A Brief Introduction to Theoretical Foundations of Machine Learning and Machine Teaching | 95 | |
|
| 132. | The Asteroid Terrestrial-impact Last Alert System (ATLAS) | 94 | |
|
| 133. | Zero Knowledge from the Discrete Logarithm Problem | 93 | Vlog |
|
| 134. | Are Polar Codes Practical? | 93 | |
|
| 135. | Introduction to Practical FHE and the TFHE Scheme | 93 | |
|
| 136. | Sketching Big Data | 92 | |
|
| 137. | Introduction to Quantum Hamiltonian Complexity | 92 | |
|
| 138. | The Geometry of Matroids | 91 | |
|
| 139. | Min-Max Optimization (Part I) | 91 | |
|
| 140. | Lessons Learned from Evaluating the Robustness of Defenses to Adversarial Examples | 91 | |
|
| 141. | Polar Codes II | 90 | |
|
| 142. | Training on the Test Set and Other Heresies | 90 | |
|
| 143. | Tutorial: Statistical Learning Theory and Neural Networks I | 90 | Tutorial |
|
| 144. | Crash Course on Optimal Transport | 89 | |
|
| 145. | Independent Component Analysis: From Theory to Practice and Back | 89 | |
|
| 146. | Linear Logic, Session Types and Deadlock-Freedom | 88 | |
|
| 147. | The Green-Tao Theorem and a Relative Szemeredi Theorem | 88 | |
|
| 148. | The Power of Graph Learning | Richard M. Karp Distinguished Lecture | 88 | |
|
| 149. | Backpropagation and Deep Learning in the Brain | 88 | |
|
| 150. | Towards Reliable Use of Large Language Models: Better Detection, Consistency, and Instruction-Tuning | 87 | Tutorial |
|
| 151. | Interior Point Methods 1 | 87 | |
|
| 152. | Introduction to Data Structures and Optimization for Fast Algorithms | 86 | |
|
| 153. | Optimization for Machine Learning II | 86 | |
|
| 154. | Recent Progress in Quantum Advantage | 86 | |
|
| 155. | A Variational Inequality Framework for Network Games: Existence, Uniqueness, ... | 86 | |
|
| 156. | High-Dimensional Statistics II | 85 | |
|
| 157. | A Personal Viewpoint on Probabilistic Programming | 85 | |
|
| 158. | Panel Discussion on Potential for Quantum Advantage in Machine Learning | Quantum Colloquium | 84 | Discussion |
|
| 159. | Trends in Large-scale Nonconvex Optimization | 84 | |
|
| 160. | A Tutorial on Reinforcement Learning II | 84 | |
|
| 161. | Quantum Supremacy: Checking a Quantum Computer with a Classical Supercomputer | 83 | |
|
| 162. | Is There Evidence of Exponential Quantum Advantage in Quantum Chemistry? | Quantum Colloquium | 83 | |
|
| 163. | The Imitation Learning View of Structured Prediction | 83 | |
|
| 164. | The Digital Fence: Taiwan’s Response to COVID-19 | 82 | |
|
| 165. | Accurate, Fast, and Model-Aware Transcript Expression Quantification with Salmon | 82 | |
|
| 166. | Panel Discussion | 82 | Discussion |
|
| 167. | Generalization I | 81 | |
|
| 168. | Submodularity: Theory and Applications II | 80 | |
|
| 169. | Offline Reinforcement Learning and Model-Based Optimization | 79 | |
|
| 170. | Interactive Proofs (Part I) | 79 | |
|
| 171. | Lattices: Algorithms, Complexity, and Cryptography | 79 | |
|
| 172. | Optimal Transport and PDE: Gradient Flows in the Wasserstein Metric | 79 | |
|
| 173. | Polar Codes III | 79 | |
|
| 174. | Big Data: The Computation/Statistics Interface | 78 | |
|
| 175. | Stochastic Programming Approach to Optimization Under Uncertainty (Part 1) | 77 | |
|
| 176. | Selective Inference and False Discovery Rate I | 77 | |
|
| 177. | Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints | 77 | |
|
| 178. | Equilibrium Computation and Machine Learning | 77 | |
|
| 179. | Using Lattices for Cryptanalysis | 76 | |
|
| 180. | Pattern Separation and Completion in Subregions of the Hippocampus | 76 | |
|
| 181. | Feedback Control Theory: Architectures and Tools for Real-Time Decision Making I | 76 | |
|
| 182. | Safety-Critical Autonomous Systems: What is Possible? What is Required? | 75 | |
|
| 183. | First-Order Stochastic Optimization | 75 | |
|
| 184. | Tensor Decomposition I | 75 | |
|
| 185. | Thinking Algorithmically About Impossibility | 75 | |
|
| 186. | Online Learning and Bandits (Part 1) | 74 | |
|
| 187. | Deep Robotic Learning | 74 | |
|
| 188. | Optimization Crash Course (continued) | 73 | |
|
| 189. | "The Problem with Qubits" | 73 | |
|
| 190. | The Alignment Problem: Machine Learning and Human Values | 73 | |
|
| 191. | Quantum Supremacy via Boson Sampling: Theory and Practice | Quantum Colloquium | 73 | |
|
| 192. | Machine-Checked Proofs and the Rise of Formal Methods in Mathematics | Theoretically Speaking | 73 | |
|
| 193. | Oblivious RAM I | 72 | |
|
| 194. | Quantum-Inspired Classical Linear Algebra | 72 | |
|
| 195. | Introduction to Causal Graphical Models: Graphs, d-separation, do-calculus | 72 | |
|
| 196. | Cortical Travelling Waves: Mechanisms and Computational Principles | 71 | |
|
| 197. | Beyond Computation: The P versus NP question | 71 | |
|
| 198. | Are Aligned Language Models “Adversarially Aligned”? | 70 | |
|
| 199. | Studying Generalization in Deep Learning via PAC-Bayes | 70 | |
|
| 200. | Analyzing Optimization and Generalization in Deep Learning via Trajectories of Gradient Descent | 69 | |
|
