Daniel M. Kane
Professor
Works on theoretical computer science and learning theory, including algorithmic robust statistics, derandomization and Boolean functions.
Daniel M. Kane's lab at UCSD works on learning theory & optimization and trustworthy & safe ML. 59 papers since 2023. Applications close .
At a glance
- papers, 2023–2026
- 15/ year
- at T1 venues
- 0%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 12 papers2026 · T1 0 · collecting
- 18 papers2025 · T1 0
- 13 papers2024 · T1 0
- 16 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- Learning theory & optimization23 papers
- Trustworthy & safe ML0 papers
- theoretical computer0 papers
- theoretical0 papers
- algorithmic0 papers
Paper list
From the lab homepage.
202612 papers
- Conference on Learning Theory (COLT)A Quasi-Polynomial Time Mean Estimator Under Mean-Shift Contamination with Unknown Covariance (opens in a new tab)
- APPROX/RANDOM , journal version in LIPIcsHard-to-Sample Distributions from Robust Extractors (opens in a new tab)
- Symposium on Theory of Computation (STOC)High-Accuracy List-Decodable Mean Estimation (opens in a new tab)
- Conference on Learning Theory (COLT)High-Dimensional Gaussian Mean Estimation under Realizable Contamination (opens in a new tab)
- Ilias Diakonikolas, Daniel M. Kane, Mingchen Ma Robust Learning of Multi-Index Models under Logconcave Distributions via Weak Partial Learners, Foundations Of Computer Science (FOCS)Ilias Diakonikolas, Daniel M. Kane, Mingchen Ma Robust Learning of Multi-Index Models under Logconcave Distributions via Weak Partial Learners, Foundations Of Computer Science (FOCS), 2026.
- Ilias Diakonikolas, Daniel M. Kane, Sihan Liu, Trung Tran PTF Lower Bounds from Low-Degree Testing Lower Bounds in Additive Noise Models, Foundations Of Computer Science (FOCS)Ilias Diakonikolas, Daniel M. Kane, Sihan Liu, Trung Tran PTF Lower Bounds from Low-Degree Testing Lower Bounds in Additive Noise Models, Foundations Of Computer Science (FOCS), 2026.
- Conference on Learning Theory (COLT)Linear Regression under Missing or Corrupted Coordinates (opens in a new tab)
- APPROX/RANDOM , journal version in LIPIcsOn the Advantage of Adaptivity for Sampling with Cell Probes (opens in a new tab)
- Quantum Information Processing (QIP)Quantum Advantage from Sampling Shallow Circuits: Beyond Hardness of Marginals (opens in a new tab)
- Symposium on Theory of Computation (STOC)Rigorous Implications of the Low-Degree Heuristic (opens in a new tab)
- International Conference on Machine Learning (ICML)Sample Complexity Bounds for Robust Mean Estimation with Mean-Shift Contamination (opens in a new tab)
- APPROX/RANDOM , journal version in LIPIcsSymmetric Distributions from Shallow Circuits (opens in a new tab)
202518 papers
- Advances in Neural Information Processing Systems (NeurIPS)Algorithms and SQ Lower Bounds for Robustly Learning Real-valued Multi-index Models (opens in a new tab)
- International Conference on Machine Learning (ICML)Batch List-Decodable Linear Regression via Higher Moments (opens in a new tab)
- Symposium on Discrete Algorithms (SODA)Clustering Mixtures of Bounded Covariance Distributions Under Optimal Separation (opens in a new tab)
- Algorithmic Learning Theory (ALT)Do PAC-Learners Learn the Marginal Distribution? (opens in a new tab)
- International Conference on Machine Learning (ICML)Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination (opens in a new tab)
- Symposium on Theory of Computation (STOC)Entangled Mean Estimation in High-Dimensions (opens in a new tab)
- Conference on Learning Theory (COLT)Faster Algorithms for Agnostically Learning Disjunctions and their Implications (opens in a new tab)
- Foundations Of Computer Science (FOCS)Implicit High-Order Moment Tensor Estimation and Learning Latent Variable Models (opens in a new tab)
- Advances in Neural Information Processing Systems (NeurIPS)Information-Computation Tradeoffs for Noiseless Linear Regression with Oblivious Contamination (opens in a new tab)
- (updated since publication) Symposium on Theory of Computation (STOC)Locally Sampleable Uniform Symmetric Distributions (opens in a new tab)
- International Conference on Machine Learning (ICML)On Fine-Grained Distinct Element Estimation (opens in a new tab)
- International Conference on Machine Learning (ICML)On Learning Parallel Pancakes with Mostly Uniform Weights (opens in a new tab)
202413 papers
- Advances in Neural Information Processing Systems (NeurIPS)Active Learning of General Halfspaces: Label Queries vs Membership Queries (opens in a new tab)
- Foundations Of Computer Science (FOCS) , accepted to the FOCS special issueAgnostically Learning Multi-index Models with Queries (opens in a new tab)
- Conference on Learning Theory (COLT)Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials (opens in a new tab)
- Symposium on Theory of Computation (STOC)Locality Bounds for Sampling Hamming Slices (opens in a new tab)
- Conference on Learning Theory (COLT)New Lower Bounds for Testing Monotonicity and Log Concavity of Distributions (opens in a new tab)
- Symposium on Discrete Algorithms (SODA)Online Robust Mean Estimation (opens in a new tab)
- Conference on Learning Theory (COLT) , Journal version TheoretiCSRealizable Learning is All You Need (opens in a new tab)
- Foundations Of Computer Science (FOCS)Replicability in High Dimensional Statistics (opens in a new tab)
- International Conference on Machine Learning (ICML)Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination (opens in a new tab)
- Conference on Learning Theory (COLT)Statistical Query Lower Bounds for Learning Truncated Gaussians (opens in a new tab)
- Symposium on Theory of Computation (STOC)Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs (opens in a new tab)
- Conference on Learning Theory (COLT)Testable Learning of General Halfspaces with Adversarial Label Noise (opens in a new tab)
202316 papers
- Conference on Learning Theory (COLT)A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points (opens in a new tab)
- Advances in Neural Information Processing Systems (NeurIPS)A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm (opens in a new tab)
- Symposium on Theory of Computation (STOC)A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning (opens in a new tab)
- Advances in Neural Information Processing Systems (NeurIPS)Efficient Testable Learning of Halfspaces with Adversarial Label Noise (opens in a new tab)
- Conference on Learning Theory (COLT)Exponential Hardness of Reinforcement Learning with Linear Function Approximation (opens in a new tab)
- SOSAGaussian Mean Testing Made Simple (opens in a new tab)
- Conference on Learning Theory (COLT)Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise (opens in a new tab)
- Advances in Neural Information Processing Systems (NeurIPS)Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression (opens in a new tab)
- Advances in Neural Information Processing Systems (NeurIPS)Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise (opens in a new tab)
- International Conference on Machine Learning (ICML)Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals (opens in a new tab)
- International Conference on Machine Learning (ICML)Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA (opens in a new tab)
- Symposium on Discrete Algorithms (SODA)Sampling Equilibria: Fast No-Regret Learning in Structured Games (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UCSDFall 2027
UCSD CSE Computer Science PhD
- Round
- Single round
- Opens
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- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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