Adam Klivans
Director of AI Initiatives for the School of Computing, Professor, Admiral B. R. Inman Centennial Chair in Computing Theory #2
Leads the NSF AI Institute for Foundations of Machine Learning, studying learning theory, provable algorithms for neural networks and protein engineering with ML.
Adam Klivans's lab at UT Austin works on learning theory & optimization. 19 papers since 2023, 15 at top venues, mostly NeurIPS and COLT. 5 alumni. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 79%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 5
Papers by yearfrom the lab homepage, 2023–2026
- 4 papers2026 · T1 0 · collecting
- 5 papers2025 · T1 5
- 5 papers2024 · T1 5
- 5 papers2023 · T1 5
T1 venuesOtherVenues: NeurIPS, COLT, ICLR, STOC
Research focuspaper titles that use each topic's words
- Learning theory & optimization16 papers
- protein engineering0 papers
- provable0 papers
- protein0 papers
- studying0 papers
Paper list
From the lab homepage.
20264 papers
- To appear, STOCA Fully Polynomial-Time Algorithm for Robustly Learning Halfspaces over the Hypercube (opens in a new tab)
- To appear, COLTEquivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift (opens in a new tab)
- To appear, COLTSandwiching Polynomials for Geometric Concepts with Low Intrinsic Dimension (opens in a new tab)
- To appear, COLTTesting Noise Assumptions of Learning Algorithms (opens in a new tab)
20255 papers
- COLT (T1)Learning Constant-Depth Circuits in Malicious Noise Models (opens in a new tab)
- NeurIPS (T1)Learning Juntas under Markov Random Fields (opens in a new tab)
- ICLR (T1)Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees (opens in a new tab)
- STOC (T1)Learning the Sherrington-Kirkpatrick Model Even at Low Temperature (opens in a new tab)
- NeurIPS (T1)The Power of Iterative Filtering for Supervised Learning with (Heavy) Contamination (opens in a new tab)
20245 papers
- ICLR (T1)An Efficient Tester-Learner for Halfspaces (opens in a new tab)
- NeurIPS (T1)Efficient Discrepancy Testing for Learning with Distribution Shift (opens in a new tab)
- COLT (T1)Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds (opens in a new tab)
- COLT (T1)Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension (opens in a new tab)
- COLT (T1)Testable Learning with Distribution Shift (opens in a new tab)
20235 papers
- STOC (T1)A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity (opens in a new tab)
- NeurIPS (T1)Agnostically Learning Single-Index Models using Omnipredictors (opens in a new tab)
- NeurIPS (T1)Learning Mixtures of Gaussians using the DDPM Objective (opens in a new tab)
- COLT (T1)Learning Narrow One-Hidden-Layer ReLU Networks (opens in a new tab)
- NeurIPS (T1)Tester-Learners for Halfspaces: Universal Algorithms (opens in a new tab)
Where alumni went5 listed
Apply to UT AustinFall 2027
Computer Science PhD
- Round
- Single round (Dec 15)
- Opens
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Data
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- Papers
- The lab homepage
- Members
- Lab homepage
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Tiers are venue classifications, not lab rankings.
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