• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 323 matches

• 2024
• 
  Peter L. Bartlett, Philip M. Long:
  Corrigendum to "Prediction, learning, uniform convergence, and scale-sensitive dimensions" [J. Comput. Syst. Sci. 56 (2) (1998) 174-190]. J. Comput. Syst. Sci. 140: 103465 (2024)
• 
  Philip M. Long, Peter L. Bartlett:
  Sharpness-Aware Minimization and the Edge of Stability. J. Mach. Learn. Res. 25: 179:1-179:20 (2024)
• 
  Ruiqi Zhang, Spencer Frei, Peter L. Bartlett:
  Trained Transformers Learn Linear Models In-Context. J. Mach. Learn. Res. 25: 49:1-49:55 (2024)
• 
  Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
  A Diffusion Process Perspective on Posterior Contraction Rates for Parameters. SIAM J. Math. Data Sci. 6(2): 553-577 (2024)
• 
  Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu:
  Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. COLT 2024: 5019-5073
• 
  Saptarshi Chakraborty, Peter L. Bartlett:
  A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data. ICLR 2024
• 
  Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett:
  How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR 2024
• 
  Gautam Goel, Peter L. Bartlett:
  Can a transformer represent a Kalman filter? L4DC 2024: 1502-1512
• 
  Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett:
  Contextual Bandits with Stage-wise Constraints. CoRR abs/2401.08016 (2024)
• 
  Saptarshi Chakraborty, Peter L. Bartlett:
  On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension. CoRR abs/2401.15801 (2024)
• 
  Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet:
  Implicit Diffusion: Efficient Optimization through Stochastic Sampling. CoRR abs/2402.05468 (2024)
• 
  Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett:
  In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization. CoRR abs/2402.14951 (2024)
• 
  Saptarshi Chakraborty, Peter L. Bartlett:
  A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data. CoRR abs/2402.15710 (2024)
• 
  Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu:
  Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. CoRR abs/2402.15926 (2024)
• 
  Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee:
  Scaling Laws in Linear Regression: Compute, Parameters, and Data. CoRR abs/2406.08466 (2024)
• 
  Yuhang Cai, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett:
  Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization. CoRR abs/2406.08654 (2024)
• 2023
• 
  Peter L. Bartlett, Philip M. Long, Olivier Bousquet:
  The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima. J. Mach. Learn. Res. 24: 316:1-316:36 (2023)
• 
  Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett:
  Random Feature Amplification: Feature Learning and Generalization in Neural Networks. J. Mach. Learn. Res. 24: 303:1-303:49 (2023)
• 
  Juan C. Perdomo, Akshay Krishnamurthy, Peter L. Bartlett, Sham M. Kakade:
  A Complete Characterization of Linear Estimators for Offline Policy Evaluation. J. Mach. Learn. Res. 24: 284:1-284:50 (2023)
• 
  Alexander Tsigler, Peter L. Bartlett:
  Benign overfitting in ridge regression. J. Mach. Learn. Res. 24: 123:1-123:76 (2023)
• 
  Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
  An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. ALT 2023: 1166-1215
• 
  Laura Bartlett, Angelo Pirrone, Noman Javed, Peter C. R. Lane, Fernand Gobet:
  Genetic Programming for Developing Simple Cognitive Models. CogSci 2023
• 
  Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro:
  Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. COLT 2023: 3173-3228
• 
  Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu:
  Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. ICLR 2023
• 
  Spencer Frei, Gal Vardi, Peter L. Bartlett, Nati Srebro:
  The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. NeurIPS 2023
• 
  Angelo Pirrone, Peter C. R. Lane, Laura Bartlett, Noman Javed, Fernand Gobet:
  Heuristic Search of Heuristics. SGAI Conf. 2023: 407-420
• 
  Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro:
  The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. CoRR abs/2303.01456 (2023)
• 
  Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro:
  Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. CoRR abs/2303.01462 (2023)
• 
  Peter L. Bartlett, Philip M. Long:
  Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions. CoRR abs/2304.11059 (2023)
• 
  Ruiqi Zhang, Spencer Frei, Peter L. Bartlett:
  Trained Transformers Learn Linear Models In-Context. CoRR abs/2306.09927 (2023)

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