Gregory Valiant

ORCID

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 99

Top Venues

Publications

Spencer Compton, Gregory Valiant
Near-Optimal Mean Estimation with Unknown, Heteroskedastic Variances. STOC (2024)
Gregory Valiant
Matrix Multiplication in Quadratic Time and Energy? Towards a Fine-Grained Energy-Centric Church-Turing Thesis. ITCS (2024)
Qian Huang, Eric Zelikman, Sarah Chen, Yuhuai Wu, Gregory Valiant, Percy Liang
Lexinvariant Language Models. NeurIPS (2023)
Mingda Qiao, Gregory Valiant
Online Pen Testing. ITCS (2023)
Shivam Garg, Chirag Pabbaraju, Kirankumar Shiragur, Gregory Valiant
Testing with Non-identically Distributed Samples. CoRR (2023)
Qian Huang, Eric Zelikman, Sarah Li Chen, Yuhuai Wu, Gregory Valiant, Percy Liang
Lexinvariant Language Models. CoRR (2023)
Steven Cao, Percy Liang, Gregory Valiant
One-sided Matrix Completion from Two Observations Per Row. ICML (2023)
Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant
Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract). IJCAI (2023)
Gregory Valiant
Matrix Multiplication in Quadratic Time and Energy? Towards a Fine-Grained Energy-Centric Church-Turing Thesis. CoRR (2023)
Steven Cao, Percy Liang, Gregory Valiant
One-sided Matrix Completion from Two Observations Per Row. CoRR (2023)
Spencer Compton, Gregory Valiant
Near-Optimal Mean Estimation with Unknown, Heteroskedastic Variances. CoRR (2023)
Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant
Efficient Convex Optimization Requires Superlinear Memory. CoRR (2022)
Theo Jepsen, Stephen Ibanez, Gregory Valiant, Nick McKeown
From Sand to Flour: The Next Leap in Granular Computing with NanoSort. CoRR (2022)
Mingda Qiao, Gregory Valiant
Online Pen Testing. CoRR (2022)
Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant
Efficient Convex Optimization Requires Superlinear Memory. COLT (2022)
Shivam Garg, Dimitris Tsipras, Percy Liang, Gregory Valiant
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes. CoRR (2022)
Shivam Garg, Dimitris Tsipras, Percy Liang, Gregory Valiant
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes. NeurIPS (2022)
Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales. COLT (2022)
Brian Axelrod, Shivam Garg, Yanjun Han, Vatsal Sharan, Gregory Valiant
On the Statistical Complexity of Sample Amplification. CoRR (2022)
Kai Sheng Tai, Peter Bailis, Gregory Valiant
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training. ICML (2021)
Mingda Qiao, Gregory Valiant
Exponential Weights Algorithms for Selective Learning. CoRR (2021)
Annie Marsden, John C. Duchi, Gregory Valiant
On Misspecification in Prediction Problems and Robustness via Improper Learning. CoRR (2021)
Annie Marsden, John C. Duchi, Gregory Valiant
Misspecification in Prediction Problems and Robustness via Improper Learning. AISTATS (2021)
Kai Sheng Tai, Peter Bailis, Gregory Valiant
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training. CoRR (2021)
Mingda Qiao, Gregory Valiant
Exponential Weights Algorithms for Selective Learning. COLT (2021)
Kartik Chandra, Chuma Kabaghe, Gregory Valiant
Beyond Laurel/Yanny: An Autoencoder-Enabled Search for Polyperceivable Audio. ACL/IJCNLP (2) (2021)
Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales. CoRR (2021)
Mingda Qiao, Gregory Valiant
Stronger calibration lower bounds via sidestepping. STOC (2021)
Weihao Kong, Emma Brunskill, Gregory Valiant
Sublinear Optimal Policy Value Estimation in Contextual Bandits. AISTATS (2020)
Guy Blanc, Neha Gupta, Gregory Valiant, Paul Valiant
Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process. COLT (2020)
Sen Wu, Hongyang R. Zhang, Gregory Valiant, Christopher Ré
On the Generalization Effects of Linear Transformations in Data Augmentation. CoRR (2020)
Mingda Qiao, Gregory Valiant
Stronger Calibration Lower Bounds via Sidestepping. CoRR (2020)
Sen Wu, Hongyang R. Zhang, Gregory Valiant, Christopher Ré
On the Generalization Effects of Linear Transformations in Data Augmentation. ICML (2020)
Justin Y. Chen, Gregory Valiant, Paul Valiant
Worst-Case Analysis for Randomly Collected Data. NeurIPS (2020)
Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant
Sample Amplification: Increasing Dataset Size even when Learning is Impossible. ICML (2020)
Melody Y. Guan, Gregory Valiant
A Surprising Density of Illusionable Natural Speech. CoRR (2019)
Vatsal Sharan, Aaron Sidford, Gregory Valiant
Memory-sample tradeoffs for linear regression with small error. STOC (2019)
Mingda Qiao, Gregory Valiant
A Theory of Selective Prediction. COLT (2019)
Melody Y. Guan, Gregory Valiant
A Surprising Density of Illusionable Natural Speech. CogSci (2019)
Justin Y. Chen, Gregory Valiant, Paul Valiant
How bad is worst-case data if you know where it comes from? CoRR (2019)
Vatsal Sharan, Aaron Sidford, Gregory Valiant
Memory-Sample Tradeoffs for Linear Regression with Small Error. CoRR (2019)
Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant
Sample Amplification: Increasing Dataset Size even when Learning is Impossible. CoRR (2019)
Mingda Qiao, Gregory Valiant
A Theory of Selective Prediction. CoRR (2019)
Kai Sheng Tai, Peter Bailis, Gregory Valiant
Equivariant Transformer Networks. ICML (2019)
Antonio Ginart, Melody Y. Guan, Gregory Valiant, James Zou
Making AI Forget You: Data Deletion in Machine Learning. NeurIPS (2019)
Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham M. Kakade
Maximum Likelihood Estimation for Learning Populations of Parameters. ICML (2019)
Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. ICML (2019)
Antonio Ginart, Melody Y. Guan, Gregory Valiant, James Zou
Making AI Forget You: Data Deletion in Machine Learning. CoRR (2019)
Weihao Kong, Gregory Valiant, Emma Brunskill
Sublinear Optimal Policy Value Estimation in Contextual Bandits. CoRR (2019)
Guy Blanc, Neha Gupta, Gregory Valiant, Paul Valiant
Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process. CoRR (2019)
Kai Sheng Tai, Peter Bailis, Gregory Valiant
Equivariant Transformer Networks. CoRR (2019)
Brian Axelrod, Ilias Diakonikolas, Alistair Stewart, Anastasios Sidiropoulos, Gregory Valiant
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families. NeurIPS (2019)
Brian Axelrod, Ilias Diakonikolas, Anastasios Sidiropoulos, Alistair Stewart, Gregory Valiant
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families. CoRR (2019)
Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham M. Kakade
Maximum Likelihood Estimation for Learning Populations of Parameters. CoRR (2019)
Qingqing Huang, Sham M. Kakade, Weihao Kong, Gregory Valiant
Recovering Structured Probability Matrices. ITCS (2018)
Weihao Kong, Gregory Valiant
Estimating Learnability in the Sublinear Data Regime. CoRR (2018)
Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant
Prediction with a short memory. STOC (2018)
Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant
Sketching Linear Classifiers over Data Streams. SIGMOD Conference (2018)
Michela Meister, Gregory Valiant
A Data Prism: Semi-verified learning in the small-alpha regime. COLT (2018)
Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant
A Spectral View of Adversarially Robust Features. CoRR (2018)
Weihao Kong, Gregory Valiant
Estimating Learnability in the Sublinear Data Regime. NeurIPS (2018)
Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant
A Spectral View of Adversarially Robust Features. NeurIPS (2018)
Brian Axelrod, Gregory Valiant
An Efficient Algorithm for High-Dimensional Log-Concave Maximum Likelihood. CoRR (2018)
David Cohen-Steiner, Weihao Kong, Christian Sohler, Gregory Valiant
Approximating the Spectrum of a Graph. KDD (2018)
Mingda Qiao, Gregory Valiant
Learning Discrete Distributions from Untrusted Batches. ITCS (2018)
Jacob Steinhardt, Moses Charikar, Gregory Valiant
Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers. ITCS (2018)
Mingda Qiao, Gregory Valiant
Learning Discrete Distributions from Untrusted Batches. CoRR (2017)
Aditi Raghunathan, Gregory Valiant, James Zou
Estimating the unseen from multiple populations. ICML (2017)
Jacob Steinhardt, Moses Charikar, Gregory Valiant
Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers. CoRR (2017)
Vatsal Sharan, Gregory Valiant
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use. ICML (2017)
Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant
Learning Overcomplete HMMs. NIPS (2017)
Vatsal Sharan, Gregory Valiant
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use. CoRR (2017)
Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant
Finding Heavily-Weighted Features in Data Streams. CoRR (2017)
Kevin Tian, Weihao Kong, Gregory Valiant
Optimally Learning Populations of Parameters. CoRR (2017)
Gregory Valiant, Paul Valiant
An Automatic Inequality Prover and Instance Optimal Identity Testing. SIAM J. Comput. 46 (1) (2017)
Aditi Raghunathan, Gregory Valiant, James Zou
Estimating the unseen from multiple populations. CoRR (2017)
David Cohen-Steiner, Weihao Kong, Christian Sohler, Gregory Valiant
Approximating the Spectrum of a Graph. CoRR (2017)
Gregory Valiant, Paul Valiant
Estimating the Unseen: Improved Estimators for Entropy and Other Properties. J. ACM 64 (6) (2017)
Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant
There and Back Again: A General Approach to Learning Sparse Models. CoRR (2017)
Michela Meister, Gregory Valiant
A Data Prism: Semi-Verified Learning in the Small-Alpha Regime. CoRR (2017)
Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant
Learning Overcomplete HMMs. CoRR (2017)
Kevin Tian, Weihao Kong, Gregory Valiant
Learning Populations of Parameters. NIPS (2017)
Moses Charikar, Jacob Steinhardt, Gregory Valiant
Learning from untrusted data. STOC (2017)
Jacob Steinhardt, Gregory Valiant, Moses Charikar
Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction. CoRR (2016)
Michael S. Crouch, Andrew McGregor, Gregory Valiant, David P. Woodruff
Stochastic Streams: Sample Complexity vs. Space Complexity. ESA (2016)
Jacob Steinhardt, Gregory Valiant, Moses Charikar
Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction. NIPS (2016)
Jacob Steinhardt, Gregory Valiant, Stefan Wager
Memory, Communication, and Statistical Queries. COLT (2016)
Weihao Kong, Gregory Valiant
Spectrum Estimation from Samples. CoRR (2016)
Sham M. Kakade, Percy Liang, Vatsal Sharan, Gregory Valiant
Prediction with a Short Memory. CoRR (2016)
Moses Charikar, Jacob Steinhardt, Gregory Valiant
Learning from Untrusted Data. CoRR (2016)
Gregory Valiant, Paul Valiant
Information Theoretically Secure Databases. CoRR (2016)
Gregory Valiant, Paul Valiant
Instance optimal learning of discrete distributions. STOC (2016)
Qingqing Huang, Sham M. Kakade, Weihao Kong, Gregory Valiant
Recovering Structured Probability Matrices. CoRR (2016)
Gregory Valiant, Paul Valiant
Information Theoretically Secure Databases. Electron. Colloquium Comput. Complex. 23 (2016)
Jacob Steinhardt, Gregory Valiant, Stefan Wager
Memory, Communication, and Statistical Queries. Electron. Colloquium Comput. Complex. 22 (2015)
Gregory Valiant
Finding Correlations in Subquadratic Time, with Applications to Learning Parities and the Closest Pair Problem. J. ACM 62 (2) (2015)
Gregory Valiant, Paul Valiant
Instance Optimal Learning. CoRR (2015)
Bhaswar B. Bhattacharya, Gregory Valiant
Testing Closeness With Unequal Sized Samples. NIPS (2015)
Bhaswar B. Bhattacharya, Gregory Valiant
Testing Closeness With Unequal Sized Samples. CoRR (2015)
Gregory Valiant, Paul Valiant
An Automatic Inequality Prover and Instance Optimal Identity Testing. FOCS (2014)

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Publication Activity (10 Years)

Top Topics

Top Venues

Gregory Valiant