| 201. | Analyzing Optimization and Generalization in Deep Learning via Trajectories of Gradient Descent | 69 | |
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| 202. | High Dimensional Geometry and Concentration I | 68 | |
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| 203. | Knowledge is embedded in language neural networks but can they reason? | 68 | |
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| 204. | How Large is the Norm of a Random Matrix? | 68 | |
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| 205. | Project CETI Next Steps: Industrial-Scale Whale Bioacoustic Data Collection and Analysis | 68 | |
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| 206. | Will Vanishing Gradients Ever Vanish from Deep Learning? | 68 | |
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| 207. | On Algorithmic Game Theory I | 68 | |
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| 208. | Reductionism in Reinforcement Learning | 67 | |
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| 209. | Quantum Machine Learning: Prospects and Challenges | 67 | |
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| 210. | In-Context Learning: A Case Study of Simple Function Classes | 66 | |
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| 211. | Complexity and Algorithmic Game Theory I | 66 | |
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| 212. | Data Driven Optimization Models and Algorithms | 66 | |
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| 213. | The Sparse Manifold Transform | 66 | |
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| 214. | Using Program Synthesis to Build Compilers | 65 | |
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| 215. | Crash Course on Probabilistically Checkable Proofs (PCP): Introduction | 64 | |
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| 216. | Sparse Fourier Transform Algorithm for Real-Time Applications | 64 | |
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| 217. | Algorithms for Lattice Problems | 63 | |
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| 218. | Kidney Exchange: Algorithms and Incentives | 63 | |
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| 219. | A Theory of Polarization | 63 | |
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| 220. | Large Language Models Meet Copyright Law | 63 | |
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| 221. | Panel on Quantum Machine Learning and Barren Plateaus | Quantum Colloquium | 62 | |
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| 222. | Learning Exploration Strategies with Meta-Reinforcement Learning | 62 | |
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| 223. | Simulating Chemistry on Realistic Quantum Computers | 62 | |
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| 224. | Recent Progress in High-Dimensional Learning | 62 | |
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| 225. | Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms | 61 | |
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| 226. | Signatures, Commitments, Zero-Knowledge, and Applications | 61 | |
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| 227. | Optimization II | 61 | |
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| 228. | Quantum Computing and Simulation with Trapped Ions | 61 | |
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| 229. | Erdős and Shannon: A Story of Probability, Communication, and Combinatorics | 61 | |
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| 230. | Deep Learning Frameworks for Regulatory Genomics and Epigenomics | 60 | |
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| 231. | Quantum Algorithms for Classification | 60 | |
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| 232. | An Automated Approach to the Collatz Conjecture | 60 | |
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| 233. | Quantum Error Correction | 60 | |
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| 234. | Decoding Nonhuman Communication | Polylogues | 60 | Vlog |
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| 235. | Attribute-based Encryption for Circuits | 60 | |
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| 236. | The Importance of Better Models in Stochastic Optimization... | 59 | |
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| 237. | Cryptocurrencies and Smart Contracts | 59 | |
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| 238. | Representation Learning of Grounded Language and Knowledge: with and without End-to-End Learning | 58 | |
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| 239. | Berkeley in the 80s, Episode 2: Manuel Blum | 58 | Show |
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| 240. | Quantum Simulators and Processors Based on Rydberg Atom Arrays | 58 | |
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| 241. | Secure Multiparty Computation I | 58 | |
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| 242. | In Search of New Algorithms Part I: Neural Networks | 58 | |
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| 243. | Algebraic Lattices and Ring Learning with Errors | 58 | |
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| 244. | Computational Optimal Transport | 58 | |
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| 245. | The Simplest Oblivious Transfer Protocol | 58 | |
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| 246. | Programming Z3 | 58 | |
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| 247. | Sampling Using Diffusion Processes, from Langevin to Schrödinger | 58 | |
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| 248. | Efficient Distributed Deep Learning Using MXNet | 58 | |
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| 249. | Offline Deep Reinforcement Learning Algorithms | 57 | |
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| 250. | Language Models as Statisticians, and as Adapted Organisms | 57 | |
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| 251. | Tensor Networks and Quantum Algorithms | 57 | |
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| 252. | Finite and Algorithmic Model Theory I | 57 | |
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| 253. | On Quantum Linear Algebra for Machine Learning | Quantum Colloquium | 57 | |
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| 254. | An Introduction to Hamiltonian Monte Carlo Method for Sampling | 56 | |
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| 255. | Mathematical Models in Population Genetics I | 56 | |
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| 256. | Classical Simulation of Quantum Many-body Systems with Tensor Networks | 56 | |
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| 257. | Quantum Algorithms -- An Overview of Techniques | 56 | |
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| 258. | Better Learning from the Past: Counterfactual / Batch RL | 56 | |
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| 259. | Tutorial: Statistical Learning Theory and Neural Networks II | 56 | Tutorial |
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| 260. | The Space of Lorentzian Polynomials | 56 | |
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| 261. | Mathematical Theories of Communication: Old and New | 56 | |
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| 262. | SNARKs and their Practical Applications | 56 | |
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| 263. | Provable Robustness Beyond Bound Propagation | 56 | |
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| 264. | Power and Limitations of the QAOA | 55 | |
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| 265. | The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies | 55 | |
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| 266. | Deep Robust Reinforcement Learning and Regularization | 55 | |
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| 267. | An Algorithmic Theory of Brain Networks | 55 | |
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| 268. | Simulating Quantum Field Theory with a Quantum Computer | 55 | |
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| 269. | Duality 1 | 55 | |
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| 270. | Learning to Predict Arbitrary Quantum Processes | 55 | |
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| 271. | Formalizing Explanations of Neural Network Behaviors | 54 | |
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| 272. | Barren Plateaus and Quantum Generative Training Using Rényi Divergences | Quantum Colloquium | 54 | |
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| 273. | Approximation Algorithms for Optimization under Uncertainty | 54 | |
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| 274. | Mathematical Imaging: From Geometric PDEs and Variational Modeling to Deep Learning for Images | 54 | |
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| 275. | Introduction to Regulatory Genomics and Epigenomics I: Intro to the Biology of Gene Regulation | 54 | |
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| 276. | Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks | 54 | |
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| 277. | Intro and Foundations of Data Science I | 54 | |
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| 278. | Constrained Optimization On Riemannian Manifolds | 53 | |
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| 279. | The Quantum LDPC Manifesto | Quantum Colloquium | 53 | |
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| 280. | The Complexity of Computing Averages | 53 | |
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| 281. | Fast Reinforcement Learning With Generalized Policy Updates | 53 | |
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| 282. | Berkeley in the 80s, Episode 4: Andrew Yao | 52 | Show |
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| 283. | Quantum Money based on Lattices | 52 | |
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| 284. | Graph Sparsification I: Sparsification via Effective Resistances | 52 | |
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| 285. | Tutorial on Deep Learning II | 52 | |
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| 286. | Embedding as a Tool for Algorithm Design | 52 | |
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| 287. | LP/SDP Hierarchies and Sum of Squares Proofs 1 | 51 | |
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| 288. | Sensitivity Conjecture and Its Applications | 51 | |
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| 289. | Equivariant RL | 51 | |
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| 290. | Matrix Factorizations | 51 | |
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| 291. | The Missing Signal | 51 | |
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| 292. | Symmetry and Topological Order in Quantum States | 51 | |
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| 293. | Machine Learning Combinatorial Optimization Algorithms | 51 | |
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| 294. | Practical Applications of Homomorphic Encryption | 51 | |
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| 295. | Equivariant Machine Learning Structured Like Classical Physics | 51 | |
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| 296. | Practical Model-based Algorithms for Reinforcement Learning and Imitation Learning, with... | 50 | |
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| 297. | Recent Developments in Over-parametrized Neural Networks, Part I | 50 | |
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| 298. | Meaning in the age of large language models | 50 | |
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| 299. | Foundations for a Democratic Metaverse | 50 | |
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| 300. | A data-centric view on reliable generalization: From ImageNet to LAION-5B | 50 | |
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