Semidefinite Problem
Last edited: January 1, 2026\begin{align} &\min c^{T}x \\ &s.t.\ x_1 F_1 + x_2 F_2 + \dots + x_{n} F_{n} + G \preceq 0 \\ & Ax = b \end{align}
Stanford UG Courses Index
Last edited: January 1, 2026Stanford UG Y1, Aut
Stanford UG Y1, Win
Stanford UG Y1, Spr
Stanford UG Y2, Aut
Stanford UG Y2, Win
Stanford UG Y2, Spr
Stanford UG Y3, Aut
Stanford UG Y3, Win
Stanford UG Talks
| Date | Topic | Presenter | Link |
|---|---|---|---|
| UG Research Program | Brian Thomas | Stanford UG Research Program | |
| Bld an Ecosystem, Not Monolith | Colin Raffel | Build a System | |
| Training Helpful CHatbots | Nazeen Rajani | Training Helpful Chatbots | |
| AI Intepretability for Bio | Gasper Begus | AI Intepretability | |
| PT Transformers on Long Seqs | Mike Lewis | Pretraining Long Transformers | |
| Transformers! | A. Vaswani | Transformers | |
| Towards Interactive Agents | Jessy Lin | Interactive Agent | |
| Dissociating Language and Thought | Anna Ivanova | Dissociating Language and Thought | |
| Language Agents | Karthik Narasimhan | Language Agents with Karthik | |
| Pretraining Data | |||
| value alignment | Been Kim | LM Alignment | |
| model editing | Peter Hase | Knowledge Editing | |
| Knowledge Localization | |||
| Presentations | Sydney Katz | Presentations | |
| Video Generation with Learned Prior | Meenakshi Sarkar | Priors | |
| Theoretical Drone Control | Sliding Mode UAV Control | ||
| VLM to Agents | Tao Yu | VLM to Agents | |
| Social RL | Natasha Jaques | Social Reinforcement Learning | |
| Model Predictive Control + Prompting | Gabriel Maher | LLM MPC | |
| Planning for Learning | |||
| Theorem Proving | Self-Play Conjection Generalization | ||
| Safety for Trucks | Safety for Autonomous Trucking | ||
| Collaborate Multiagent DM | Collaborative Multiagent DM | ||
| AI Safety Talks | AI Safety Annual Meeting | ||
| Pretraining under infinite compute | Limited Samples and Infinite Compute | ||
| Mel Krusniak | Decisions.jl | ||
| SISL Flash Talks | SISL Talks | ||
| Predicting Scaling Performance | |||
| mixed-autonomy traffic with LLMS | mixed-autonomy traffic with LLMs | ||
| AI Incidents Policy | AI Incidents Policy | ||
| Reliable RL | Reliable RL | ||
| Words to Concepts | Words to Concepts |
Contacts
SU-EE364A JAN152026
Last edited: January 1, 2026Key Sequence
Notation
New Concepts
- Disciplined Convex Programming
- more ways of checking convexity
- quasiconvex function
- optimization (math)
- special convex problems
Important Results / Claims
Questions
Interesting Factoids
Fun example
SU-EE364A JAN202026
Last edited: January 1, 2026Key Sequence
Notation
New Concepts
- Types of convex problems
- problem transformation
Important Results / Claims
Questions
Interesting Factoids
SU-SOC175 JAN142025
Last edited: January 1, 2026Key Insights
- Does China have a modern form of government?
- China’s National Government as a Hierarchy: is it centralized? Meritocratic?
Forms of Government
- Modern Government Model (“European Model”—Enlightenment English/Scottish/French): state should adjudicate interests in a fair and legitimate fashion
- US Government Model: hybrid, constitutional, late 18th entry design (i.e. “government is designed to stop things”)
- Alternative Model (“Developer Model”): State’s purpose is to push national progress through economic and military strength (i.e. “the government pushes things forward)
- China
- Axis Powers
Modern Government
We typical define a modern government as a liberal democratic government. But China’s system is in some sense much newer than liberal democracy—invented by the Soviets.
