Jacob Steinhardt (jsteinhardt@cs) I am a fifth-year graduate student in artificial intelligence at Stanford University working with Percy Liang. My main research interest is in designing machine learning algorithms that are reliable and easy for humans to reason about. Thus far this has led to two major directions: Machine learning with contracts: how can we design formal contracts for machine learning models, which robustly hold across many possible input distributions, so that users can reason abstractly about the behavior of a model without worrying about low-level details such as what dataset it was trained on? Uncertainty-aware approximate inference: given insufficient resources for exploration, many approximate inference algorithms fail by outputting high confidence in a local optimum that is likely to be wrong. Can we instead build algorithms that know when they have insufficient resources and output predictions with appropriately low confidence? I am interested in computationally-bounded reasoning more generally, and have done work on the foundations of resource-constrained learnability as well as online learning. I am also interested in the long-term impacts of AI; I have written some preliminary thoughts about this here. Outside of research, I am a coach for the USA Computing Olympiad and an instructor at the Summer Program in Applied Rationality and Cognition. I also consult part-time for the Open Philanthropy Project (formerly GiveWell Labs). I like indoor bouldering and ultimate frisbee.

Blogs

Essays

Talks

Past/Present Collaborators

Publications

Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané
Concrete Problems in AI Safety
arXiv
[Paper]

Jacob Steinhardt, Gregory Valiant, and Moses Charikar
Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction
NIPS 2016
[Paper]

Jacob Steinhardt and Percy Liang
Unsupervised Risk Estimation Using Only Conditional Independence Structure
NIPS 2016
[Paper] [Older preprint]

Jacob Steinhardt*, Gregory Valiant*, and Stefan Wager*
Memory, Communication, and Statistical Queries
COLT 2016
[Paper] [ECCC preprint]

Jacob Steinhardt and Percy Liang
Learning with Relaxed Supervision
NIPS 2015
[Paper] [Code] [Poster]

Jacob Steinhardt and Percy Liang
Reified Context Models
ICML 2015
[Paper] [Code] [Slides] [Poster]

Jacob Steinhardt and Percy Liang
Learning Fast-Mixing Models for Structured Prediction
ICML 2015
[Paper] [Code] [Slides] [Talk] [Poster]

Jacob Steinhardt and John Duchi
Minimax Rates for Memory-Constrained Sparse Linear Regression
COLT 2015
[Paper] [Slides] [Talk] [Poster]

Tianlin Shi, Jacob Steinhardt, and Percy Liang
Learning Where to Sample in Structured Prediction
AISTATS 2015
[Paper] [Code: GitHub/CodaLab] [Slides]

Jacob Steinhardt*, Stefan Wager*, and Percy Liang
The Statistics of Streaming Sparse Regression
arXiv preprint
[Paper]

Jacob Steinhardt and Percy Liang
Adaptivity and Optimism: An Improved Exponentiated Gradient Algorithm
ICML 2014
[Paper] [Slides] [Poster]

Jacob Steinhardt and Percy Liang
Filtering with Abstract Particles
ICML 2014
[Paper] [Slides] [Poster]

Jacob Steinhardt and Zoubin Ghahramani
Flexible Martingale Priors for Deep Hierarchies
AISTATS 2012
[Paper] [Slides] [Poster]

Jacob Steinhardt and Zoubin Ghahramani
Pathological Properties of Deep Bayesian Hierarchies
2011 NIPS Workshop on Bayesian Nonparametrics
[Poster Abstract] [Poster]

Jacob Steinhardt and Russ Tedrake
Finite-Time Regional Verification of Stochastic Nonlinear Systems
Robotics: Science and Systems, 2011
Best Student Paper Finalist
[Conference Paper and Errata] [Journal Paper] [Slides] [Poster]

Jacob Steinhardt
Permutations with Ascending and Descending Blocks
Electronic Journal of Combinatorics, 17:R14
[Paper] [Slides]

Jacob Steinhardt
On Coloring the Odd-Distance Graph
Electronic Journal of Combinatorics, 16:N12
[Paper]

Jacob Steinhardt
Cayley Graphs Formed by Conjugate Generating Sets of S_n
3rd Place in 2007 Siemens Competition
[Paper]