Yao-Yuan Yang

Congrats to @AndrewDai and team on the launch. This is really a talented team. I worked with Andrew at Google DeepMind and have a lot of respect for his vision and leadership. Excited to see what’s ahead.

After almost 12 years in Brain/DeepMind, I’ve finally decided to take the leap. My cofounders: @yinfeiy, Seth and I have kicked-off @ElorianAI. The first multimodal reasoning lab founded and led by former LLM pretraining, data and multimodal leads. youtu.be/YlvfNpOMeOY?si… (1/n)

@yangarbiter and I verified this concern by tracking the performance of 9,149 freelancers across two platforms (Upwork and Bēhance): Creators who declare the use of AI receive significantly lower pay, but non-creatives jobs earn more by labeling themselves as "AI Pros."

Presenting this paper in person at #NeurIPS2022 tomorrow (Tue) at 4pm. We apply the connections between privacy and generalization to (1) reduction of disparate impact of DP-SGD, (2) group DRO, and (3) adversarial training. Let's chat if you're around! neurips.cc/virtual/2022/p…

A new preprint is out! “What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning” w/ @yangarbiter, @yaodong_yu, Jarek Błasiok, & @PreetumNakkiran, in which we show that differential privacy can ensure predictable model behavior.

Understanding Rare Spurious Correlations in Neural Networks deepai.org/publication/un… by @yangarbiter et al. #NeuralNetwork #Classifier

Interested in models which are both interpretable and robust to adversarial perturbation? Joint work with @yangarbiter and @kamalikac Accepted to #ICML2021 1/5

Our paper "Robustness for Non-Parametric Classification: A Generic Attack and Defense" is on #AISTATS2020 live Q&A time at Aug 26, 27 10:00AM PDT poster: aistats2020.net/poster_334.html blog post: ucsdml.github.io/jekyll/update/… Work w/ @CyrusRashtchian, Yizhen Wang and @kamalikac

Real image datasets are well-separated, and in retrospect, we expect them to be because they are clean and curated. So what's up with adversarial examples?! A thread #MachineLearning #DataScience [1/10]

Finally, big thanks to @kamalikac, @yangarbiter, and @CyrusRashtchian for blog upkeep and help with the post!

New blog post: bit.ly/39hcZXc Paper: arxiv.org/abs/1906.03310 TLDR; we give a general defense against adversarial examples for non-parametric methods and attacks to evaluate. Work w/ @CyrusRashtchian @yangarbiter Y. Wang

New blog post: Adversarial Robustness Through Local Lipschitzness: ucsdml.github.io/jekyll/update/… We posit that achieving both accuracy and robustness is possible by learning locally smooth classifiers. Paper: arxiv.org/abs/2003.02460 w/t Y Yang, @CyrusRashtchian, H Zhang, @kamalikac

Adversarial Robustness Through Local Lipschitzness. (arXiv:2003.02460v1 [cs.LG]) ift.tt/2TpQQ2I

#arXiv #machinelearning [cs.LG] Adversarial Robustness Through Local Lipschitzness. (arXiv:2003.02460v1 [cs.LG]) bit.ly/32TgWhC A standard method for improving the robustness of neural networks is adversarial training, where the network is trained on adversarial examp…

Tough questions from @kamalikac at the #ITA2020 ML panel: Is deep learning going to solve climate change? @rsalakhu defers to Turing award winners but also "obviously not"

"Open problem: explain this plot" 😂Another good theory question from @roydanroy during the #ITA2020 plenary.

Adversarial Examples for Non-Parametric Methods: Attacks, Defenses and Large Sample Limits. arxiv.org/abs/1906.03310

When your poster isn't ready in time, you have to improvise. #NeurIPS2019