Roi Livni

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 62

Top Topics

Sample Complexity

Online Learning

Top Venues

Publications

Niva Elkin-Koren, Uri Hacohen, Roi Livni, Shay Moran
Can Copyright Be Reduced to Privacy? FORC (2024)
Roi Livni
The Sample Complexity of Gradient Descent in Stochastic Convex Optimization. CoRR (2024)
Uri Hacohen, Adi Haviv, Shahar Sarfaty, Bruria Friedman, Niva Elkin-Koren, Roi Livni, Amit H. Bermano
Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes. CoRR (2024)
Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy
Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization. CoRR (2024)
Daniel Carmon, Amir Yehudayoff, Roi Livni
The sample complexity of ERMs in stochastic convex optimization. AISTATS (2024)
Roi Livni
Making Progress Based on False Discoveries. ITCS (2024)
Roi Livni, Shay Moran, Kobbi Nissim, Chirag Pabbaraju
Credit Attribution and Stable Compression. CoRR (2024)
Niva Elkin-Koren, Uri Hacohen, Roi Livni, Shay Moran
Can Copyright be Reduced to Privacy? CoRR (2023)
Daniel Carmon, Roi Livni, Amir Yehudayoff
The Sample Complexity Of ERMs In Stochastic Convex Optimization. CoRR (2023)
Roi Livni
Information Theoretic Lower Bounds for Information Theoretic Upper Bounds. NeurIPS (2023)
Roi Livni
Information Theoretic Lower Bounds for Information Theoretic Upper Bounds. CoRR (2023)
Tomer Koren, Roi Livni, Yishay Mansour, Uri Sherman
Benign Underfitting of Stochastic Gradient Descent. CoRR (2022)
Idan Amir, Roi Livni, Nathan Srebro
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization. CoRR (2022)
Idan Amir, Guy Azov, Tomer Koren, Roi Livni
Better Best of Both Worlds Bounds for Bandits with Switching Costs. NeurIPS (2022)
Tomer Koren, Roi Livni, Yishay Mansour, Uri Sherman
Benign Underfitting of Stochastic Gradient Descent. NeurIPS (2022)
Noga Alon, Mark Bun, Roi Livni, Maryanthe Malliaris, Shay Moran
Private and Online Learnability Are Equivalent. J. ACM 69 (4) (2022)
Roi Livni
Making Progress Based on False Discoveries. CoRR (2022)
Idan Amir, Guy Azov, Tomer Koren, Roi Livni
Better Best of Both Worlds Bounds for Bandits with Switching Costs. CoRR (2022)
Idan Amir, Roi Livni, Nati Srebro
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization. NeurIPS (2022)
Noah Golowich, Roi Livni
Littlestone Classes are Privately Online Learnable. CoRR (2021)
Steve Hanneke, Roi Livni, Shay Moran
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. CoRR (2021)
Noah Golowich, Roi Livni
Littlestone Classes are Privately Online Learnable. NeurIPS (2021)
Idan Amir, Yair Carmon, Tomer Koren, Roi Livni
Never Go Full Batch (in Stochastic Convex Optimization). CoRR (2021)
Idan Amir, Tomer Koren, Roi Livni
SGD Generalizes Better Than GD (And Regularization Doesn't Help). CoRR (2021)
Idan Amir, Yair Carmon, Tomer Koren, Roi Livni
Never Go Full Batch (in Stochastic Convex Optimization). NeurIPS (2021)
Idan Amir, Tomer Koren, Roi Livni
SGD Generalizes Better Than GD (And Regularization Doesn't Help). COLT (2021)
Steve Hanneke, Roi Livni, Shay Moran
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. COLT (2021)
Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study. CoRR (2020)
Roi Livni, Shay Moran
A Limitation of the PAC-Bayes Framework. CoRR (2020)
Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study. NeurIPS (2020)
Idan Amir, Idan Attias, Tomer Koren, Yishay Mansour, Roi Livni
Prediction with Corrupted Expert Advice. NeurIPS (2020)
Pravesh K. Kothari, Roi Livni
On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes. ALT (2020)
Mark Bun, Roi Livni, Shay Moran
An Equivalence Between Private Classification and Online Prediction. CoRR (2020)
Idan Amir, Idan Attias, Tomer Koren, Roi Livni, Yishay Mansour
Prediction with Corrupted Expert Advice. CoRR (2020)
Roi Livni, Shay Moran
A Limitation of the PAC-Bayes Framework. NeurIPS (2020)
Olivier Bousquet, Roi Livni, Shay Moran
Synthetic Data Generators - Sequential and Private. NeurIPS (2020)
Mark Bun, Roi Livni, Shay Moran
An Equivalence Between Private Classification and Online Prediction. FOCS (2020)
Noga Alon, Roi Livni, Maryanthe Malliaris, Shay Moran
Private PAC learning implies finite Littlestone dimension. STOC (2019)
Roi Livni, Yishay Mansour
Graph-based Discriminators: Sample Complexity and Expressiveness. CoRR (2019)
Olivier Bousquet, Roi Livni, Shay Moran
Passing Tests without Memorizing: Two Models for Fooling Discriminators. CoRR (2019)
Daniel Kane, Roi Livni, Shay Moran, Amir Yehudayoff
On Communication Complexity of Classification Problems. COLT (2019)
Brian Bullins, Elad Hazan, Adam Kalai, Roi Livni
Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning. ALT (2019)
Roi Livni, Yishay Mansour
Graph-based Discriminators: Sample Complexity and Expressiveness. NeurIPS (2019)
Pravesh K. Kothari, Roi Livni
On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes. CoRR (2019)
Elad Hazan, Roi Livni
Open problem: Improper learning of mixtures of Gaussians. COLT (2018)
Noga Alon, Roi Livni, Maryanthe Malliaris, Shay Moran
Private PAC learning implies finite Littlestone dimension. CoRR (2018)
Pravesh K. Kothari, Roi Livni
Improper Learning by Refuting. ITCS (2018)
Tomer Koren, Roi Livni, Yishay Mansour
Bandits with Movement Costs and Adaptive Pricing. CoRR (2017)
Roi Livni, Daniel Carmon, Amir Globerson
Learning Infinite Layer Networks Without the Kernel Trick. ICML (2017)
Daniel M. Kane, Roi Livni, Shay Moran, Amir Yehudayoff
On Communication Complexity of Classification Problems. Electron. Colloquium Comput. Complex. 24 (2017)
Pravesh K. Kothari, Roi Livni
Learning by Refuting. CoRR (2017)
Amir Globerson, Roi Livni, Shai Shalev-Shwartz
Effective Semisupervised Learning on Manifolds. COLT (2017)
Tomer Koren, Roi Livni, Yishay Mansour
Multi-Armed Bandits with Metric Movement Costs. CoRR (2017)
Tomer Koren, Roi Livni
Affine-Invariant Online Optimization and the Low-rank Experts Problem. NIPS (2017)
Tomer Koren, Roi Livni, Yishay Mansour
Bandits with Movement Costs and Adaptive Pricing. COLT (2017)
Daniel M. Kane, Roi Livni, Shay Moran, Amir Yehudayoff
On Communication Complexity of Classification Problems. CoRR (2017)
Tomer Koren, Roi Livni, Yishay Mansour
Multi-Armed Bandits with Metric Movement Costs. NIPS (2017)
Michal Feldman, Tomer Koren, Roi Livni, Yishay Mansour, Aviv Zohar
Online Pricing with Strategic and Patient Buyers. NIPS (2016)
Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson
Improper Deep Kernels. AISTATS (2016)
Amir Globerson, Roi Livni
Learning Infinite-Layer Networks: Beyond the Kernel Trick. CoRR (2016)
Elad Hazan, Tomer Koren, Roi Livni, Yishay Mansour
Online Learning with Low Rank Experts. CoRR (2016)
Elad Hazan, Tomer Koren, Roi Livni, Yishay Mansour
Online Learning with Low Rank Experts. COLT (2016)
Elad Hazan, Roi Livni, Yishay Mansour
Classification with Low Rank and Missing Data. CoRR (2015)
Elad Hazan, Roi Livni, Yishay Mansour
Classification with Low Rank and Missing Data. ICML (2015)
Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
On the Computational Efficiency of Training Neural Networks. NIPS (2014)
Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
On the Computational Efficiency of Training Neural Networks. CoRR (2014)
Roi Livni, Pierre Simon
Honest Compressions and Their Application to Compression Schemes. COLT (2013)
Roi Livni, David Lehavi, Sagi Schein, Hila Nachlieli, Shai Shalev-Shwartz, Amir Globerson
Vanishing Component Analysis. ICML (1) (2013)
Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
A Provably Efficient Algorithm for Training Deep Networks CoRR (2013)
Roi Livni, Koby Crammer, Amir Globerson
A Simple Geometric Interpretation of SVM using Stochastic Adversaries. AISTATS (2012)