Adam R. Klivans
where else to find them
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what they talk about
where they keep going back
no series more than once — all 5 in the filters
on a stage with
- Daniel Reichmanonce
- Fanny Yangonce
- Florent Krzakalaonce
all 9 in the filters
who talks about the same things
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| title | conference | date |
|---|---|---|
| Efficient Algorithms for Reliable Machine Learning | The Role of TCS in Modern Machine Learning | 2026-05-26 |
Introduces a concrete proof system called testable learning where an algorithm either returns a classifier with error at most opt + ε or abstains, and must… | ||
| Panel on the future of scientific research and education | Unknown Futures of Generalization 2024 | 2024-12-02 |
A live panel debates which research skills survive widespread LLM use and gives concrete examples: a student-run “Resurrected Scientists” ChatGPT experiment… | ||
| A New Paradigm for Learning with Distribution Shift | Unknown Futures of Generalization 2024 | 2024-12-02 |
A new model called TDS learning requires an efficient test on training data that may reject or else outputs a classifier with provable low test error even… | ||
| Tight Hardness Results for Training Depth-2 ReLU Networks | ITCS 2021 | 2021-01-06 |
Proves concrete limits and matching algorithms for depth-2 ReLU training: shows single-ReLU NP-hard (agnostic) and NP-hardness for any K≥2 in the realizable… | ||
| Efficient Algorithms for Outlier-Robust Regression | Robust and High-Dimensional Statistics (Simons Institute workshop, 2018) | 2018-10-29 |
Provides a polynomial-time algorithm that tolerates an adversary corrupting an η fraction of training points and outputs a linear predictor whose square loss… | ||
| Analytic Methods for Supervised Learning I | Real Analysis Boot Camp 2013 | 2013-09-09 |
A concrete claim: decision trees become learnable by finding the largest Fourier coefficients and outputting the sign of a sparse polynomial. The algorithm… | ||
| Analytic Methods for Supervised Learning II | Real Analysis Boot Camp 2013 | 2013-09-09 |
Shows that a decision tree with t leaves has L1(f) ≤ t for t‑leaf tree and uses that to get sparse approximations via zeroing small coefficients; states… | ||
| Analytic Methods for Supervised Learning III | Real Analysis Boot Camp 2013 | 2013-09-09 |
Shows an algorithmic pipeline: a projected gradient descent routine approximately solves the polynomial L1 minimization in time poly(n,1/ε,t), which gives an… | ||
| Analytic Methods for Supervised Learning IV | Real Analysis Boot Camp 2013 | 2013-09-09 |
Shows that agnostic learning of a single halfspace under the Gaussian reduces to finding a low‑degree polynomial: Hermite/Fourier coefficients give… | ||
| nothing matches | ||