Daniel Levy (@daniellevy_

Daniel Levy

763 posts

Daniel Levy

@daniellevy__

cofounder at @ssi

Stanford, CA

cs.stanford.edu/~danilevy

Joined October 2009

Daniel Levy
@daniellevy__
Jun 19, 2024
Beyond excited to be starting this company with Ilya and DG! I can't imagine working on anything else at this point in human history. If you feel the same and want to work in a small, cracked, high-trust team that will produce miracles, please reach out.
SSI Inc.
@ssi
Jun 19, 2024
Superintelligence is within reach. Building safe superintelligence (SSI) is the most important technical problem of our time. We've started the world’s first straight-shot SSI lab, with one goal and one product: a safe superintelligence. It’s called Safe Superintelligence
429K
Daniel Levy
@daniellevy__
Sep 4, 2024
Time to get to work ⛰️👷‍♂️
SSI Inc.
@ssi
Sep 4, 2024
SSI is building a straight shot to safe superintelligence. We’ve raised $1B from NFDG, a16z, Sequoia, DST Global, and SV Angel. We’re hiring: ssi.inc
211K
Daniel Levy
@daniellevy__
Jul 3, 2025
🫡
Ilya Sutskever
@ilyasut
Jul 3, 2025
I sent the following message to our team and investors: — As you know, Daniel Gross’s time with us has been winding down, and as of June 29 he is officially no longer a part of SSI. We are grateful for his early contributions to the company and wish him well in his next
114K
Daniel Levy
@daniellevy__
Jan 30, 2018
Our paper (with Matt Hoffman and @jaschasd) on learning MCMC kernels parameterized by neural networks was accepted to ICLR. Up to 106x ESS and improved posterior sampling for deep generative models. Paper: goo.gl/afzwVr Code: goo.gl/jCruwW cc: @GoogleBrain
Daniel Levy
@daniellevy__
Sep 24, 2019
First-order methods are great but we never know which one to use. In our paper (w/ John Duchi) arxiv.org/pdf/1909.10455, we tell you which one to use and when, depending on the quadratic convexity of the constraints. Also, adaptivity matters. To appear at #NeurIPS2019 as an oral.
Daniel Levy
@daniellevy__
Sep 29, 2021
Two papers on optimization with differential privacy accepted to #NeurIPS2021! In the 1st, with @SZiteng and collaborators at Google, we provide optimal algorithms to learn with *user-level* DP; a more stringent notion that protects a user's entire contribution (of many samples)
Daniel Levy
@daniellevy__
Oct 13, 2020
DRO: no-one knows what it does and it doesn't scale anyway... Or does it? In our #NeurIPS2020 paper, we propose optimization algorithms with running time independent of dimension and dataset size (think SGD for ERM) for CVaR and chi-square objectives. arxiv.org/abs/2010.05893 1/4
arxiv.org
Large-Scale Methods for Distributionally Robust Optimization
We propose and analyze algorithms for distributionally robust optimization of convex losses with conditional value at risk (CVaR) and $χ^2$ divergence uncertainty sets. We prove that our...
Daniel Levy
@daniellevy__
Apr 29, 2018
On my way to #ICLR2018! Will be presenting our work (with Matt Hoffman and @jaschasd) on generalizing HMC with neural networks. Come to the poster on Thursday! Code: github.com/brain-research… Paper: arxiv.org/abs/1711.09268 cc: @GoogleBrain
GitHub - brain-research/l2hmc: TensorFlow implementation for training MCMC samplers from the paper:...
From github.com
Daniel Levy
@daniellevy__
Dec 31, 2014
Just wrote a piece breaking down the extravagant toplaner @FnaticsOAZ and his raw talent! goldper10.com/article/534.ht…
Daniel Levy
@daniellevy__
Dec 10, 2019
Choosing the optimal gradient algorithm depending on the geometry is important. Interestingly, how to do it is in seminal results about the Gaussian Sequence model. Come to our oral at 4:50pm in West Exhibition Hall A! #NeurIPS2019
Daniel Levy
@daniellevy__
Sep 24, 2019
First-order methods are great but we never know which one to use. In our paper (w/ John Duchi) arxiv.org/pdf/1909.10455, we tell you which one to use and when, depending on the quadratic convexity of the constraints. Also, adaptivity matters. To appear at #NeurIPS2019 as an oral.
Daniel Levy
@daniellevy__
Feb 24, 2021
New paper out on learning under *user*-level differential privacy constraints! arxiv.org/abs/2102.11845 In the standard DP setting, we implicitly assume that each user contributes a single sample but it turns out we often contribute many many samples (like all of our texts). 1/
Daniel Levy
@daniellevy__
Dec 9, 2020
The poster everyone has been waiting for will happen tomorrow (Wednesday) between 9-11am PST. Come say hi and learn the secret to robustness at scale! Paper: arxiv.org/abs/2010.05893 Gathertown: neurips.gather.town/app/xrip42gQ8g…
Daniel Levy
@daniellevy__
Oct 13, 2020
DRO: no-one knows what it does and it doesn't scale anyway... Or does it? In our #NeurIPS2020 paper, we propose optimization algorithms with running time independent of dimension and dataset size (think SGD for ERM) for CVaR and chi-square objectives. arxiv.org/abs/2010.05893 1/4
arxiv.org
Large-Scale Methods for Distributionally Robust Optimization
We propose and analyze algorithms for distributionally robust optimization of convex losses with conditional value at risk (CVaR) and $χ^2$ divergence uncertainty sets. We prove that our...
Daniel Levy
@daniellevy__
Feb 4, 2018
Just arrived to #AAAI2018! Will be presenting our work (with @ermonste) on sample-efficient policy optimization for discrete action spaces. Spotlight tomorrow at 2:30. Paper: arxiv.org/abs/1711.08068
Daniel Levy
@daniellevy__
Dec 20, 2018
Replying to @LSXYZ9
GIF