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ORCIDRoi Livni
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2020 – today
- 2024
[c37]Daniel Carmon, Amir Yehudayoff, Roi Livni:
The sample complexity of ERMs in stochastic convex optimization. AISTATS 2024: 3799-3807- [c36]Niva Elkin-Koren, Uri Hacohen, Roi Livni, Shay Moran:
Can Copyright Be Reduced to Privacy? FORC 2024: 3:1-3:18 - [c35]Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy:
Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing. ICML 2024 - [c34]Roi Livni:
Making Progress Based on False Discoveries. ITCS 2024: 76:1-76:18 - [i34]Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy:
Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization. CoRR abs/2402.09327 (2024) - [i33]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 abs/2403.17691 (2024) - [i32]Roi Livni:
The Sample Complexity of Gradient Descent in Stochastic Convex Optimization. CoRR abs/2404.04931 (2024) - [i31]Roi Livni, Shay Moran, Kobbi Nissim, Chirag Pabbaraju:
Credit Attribution and Stable Compression. CoRR abs/2406.15916 (2024) - [i30]Adi Haviv, Shahar Sarfaty, Uri Hacohen, Niva Elkin-Koren, Roi Livni, Amit H. Bermano:
Not Every Image is Worth a Thousand Words: Quantifying Originality in Stable Diffusion. CoRR abs/2408.08184 (2024) - 2023
- [c33]Roi Livni:
Information Theoretic Lower Bounds for Information Theoretic Upper Bounds. NeurIPS 2023 - [i29]Roi Livni:
Information Theoretic Lower Bounds for Information Theoretic Upper Bounds. CoRR abs/2302.04925 (2023) - [i28]Niva Elkin-Koren, Uri Hacohen, Roi Livni, Shay Moran:
Can Copyright be Reduced to Privacy? CoRR abs/2305.14822 (2023) - [i27]Daniel Carmon, Roi Livni, Amir Yehudayoff:
The Sample Complexity Of ERMs In Stochastic Convex Optimization. CoRR abs/2311.05398 (2023) - 2022
- [j1]Noga Alon, Mark Bun, Roi Livni, Maryanthe Malliaris, Shay Moran
:
Private and Online Learnability Are Equivalent. J. ACM 69(4): 28:1-28:34 (2022) - [c32]Idan Amir, Guy Azov, Tomer Koren, Roi Livni:
Better Best of Both Worlds Bounds for Bandits with Switching Costs. NeurIPS 2022 - [c31]Idan Amir, Roi Livni, Nati Srebro:
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization. NeurIPS 2022 - [c30]Tomer Koren, Roi Livni, Yishay Mansour, Uri Sherman:
Benign Underfitting of Stochastic Gradient Descent. NeurIPS 2022 - [i26]Idan Amir, Roi Livni, Nathan Srebro:
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization. CoRR abs/2202.13328 (2022) - [i25]Tomer Koren, Roi Livni, Yishay Mansour, Uri Sherman:
Benign Underfitting of Stochastic Gradient Descent. CoRR abs/2202.13361 (2022) - [i24]Roi Livni:
Making Progress Based on False Discoveries. CoRR abs/2204.08809 (2022) - [i23]Idan Amir, Guy Azov, Tomer Koren, Roi Livni:
Better Best of Both Worlds Bounds for Bandits with Switching Costs. CoRR abs/2206.03098 (2022) - 2021
- [c29]Idan Amir, Tomer Koren, Roi Livni:
SGD Generalizes Better Than GD (And Regularization Doesn't Help). COLT 2021: 63-92 - [c28]Steve Hanneke, Roi Livni, Shay Moran:
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. COLT 2021: 2289-2314 - [c27]Noah Golowich, Roi Livni:
Littlestone Classes are Privately Online Learnable. NeurIPS 2021: 11462-11473 - [c26]Idan Amir, Yair Carmon, Tomer Koren, Roi Livni:
Never Go Full Batch (in Stochastic Convex Optimization). NeurIPS 2021: 25033-25043 - [i22]Idan Amir, Tomer Koren, Roi Livni:
SGD Generalizes Better Than GD (And Regularization Doesn't Help). CoRR abs/2102.01117 (2021) - [i21]Steve Hanneke, Roi Livni, Shay Moran:
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. CoRR abs/2102.01646 (2021) - [i20]Noah Golowich, Roi Livni:
Littlestone Classes are Privately Online Learnable. CoRR abs/2106.13513 (2021) - [i19]Idan Amir, Yair Carmon, Tomer Koren, Roi Livni:
Never Go Full Batch (in Stochastic Convex Optimization). CoRR abs/2107.00469 (2021) - 2020
- [c25]Pravesh K. Kothari, Roi Livni:
On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes. ALT 2020: 422-450 - [c24]Mark Bun, Roi Livni, Shay Moran:
An Equivalence Between Private Classification and Online Prediction. FOCS 2020: 389-402 - [c23]Idan Amir, Idan Attias, Tomer Koren, Yishay Mansour, Roi Livni:
Prediction with Corrupted Expert Advice. NeurIPS 2020 - [c22]Olivier Bousquet, Roi Livni, Shay Moran:
Synthetic Data Generators - Sequential and Private. NeurIPS 2020 - [c21]Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni:
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study. NeurIPS 2020 - [c20]Roi Livni, Shay Moran:
A Limitation of the PAC-Bayes Framework. NeurIPS 2020 - [i18]Idan Amir, Idan Attias, Tomer Koren, Roi Livni, Yishay Mansour:
Prediction with Corrupted Expert Advice. CoRR abs/2002.10286 (2020) - [i17]Mark Bun, Roi Livni, Shay Moran:
An Equivalence Between Private Classification and Online Prediction. CoRR abs/2003.00563 (2020) - [i16]Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni:
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study. CoRR abs/2003.06152 (2020) - [i15]Roi Livni, Shay Moran:
A Limitation of the PAC-Bayes Framework. CoRR abs/2006.13508 (2020)
2010 – 2019
- 2019
- [c19]Brian Bullins, Elad Hazan, Adam Kalai, Roi Livni:
Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning. ALT 2019: 235-246 - [c18]Daniel Kane, Roi Livni, Shay Moran, Amir Yehudayoff:
On Communication Complexity of Classification Problems. COLT 2019: 1903-1943 - [c17]Roi Livni, Yishay Mansour:
Graph-based Discriminators: Sample Complexity and Expressiveness. NeurIPS 2019: 6696-6705 - [c16]Noga Alon, Roi Livni, Maryanthe Malliaris, Shay Moran:
Private PAC learning implies finite Littlestone dimension. STOC 2019: 852-860 - [i14]Olivier Bousquet, Roi Livni, Shay Moran:
Passing Tests without Memorizing: Two Models for Fooling Discriminators. CoRR abs/1902.03468 (2019) - [i13]Pravesh K. Kothari, Roi Livni:
On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes. CoRR abs/1902.04782 (2019) - [i12]Roi Livni, Yishay Mansour:
Graph-based Discriminators: Sample Complexity and Expressiveness. CoRR abs/1906.00264 (2019) - 2018
- [c15]Elad Hazan, Roi Livni:
Open problem: Improper learning of mixtures of Gaussians. COLT 2018: 3399-3402 - [c14]Pravesh K. Kothari, Roi Livni:
Improper Learning by Refuting. ITCS 2018: 55:1-55:10 - [i11]Noga Alon, Roi Livni, Maryanthe Malliaris, Shay Moran:
Private PAC learning implies finite Littlestone dimension. CoRR abs/1806.00949 (2018) - 2017
- [c13]Amir Globerson, Roi Livni, Shai Shalev-Shwartz:
Effective Semisupervised Learning on Manifolds. COLT 2017: 978-1003 - [c12]Tomer Koren, Roi Livni, Yishay Mansour:
Bandits with Movement Costs and Adaptive Pricing. COLT 2017: 1242-1268 - [c11]Roi Livni, Daniel Carmon, Amir Globerson:
Learning Infinite Layer Networks Without the Kernel Trick. ICML 2017: 2198-2207 - [c10]Tomer Koren, Roi Livni, Yishay Mansour:
Multi-Armed Bandits with Metric Movement Costs. NIPS 2017: 4119-4128 - [c9]Tomer Koren, Roi Livni:
Affine-Invariant Online Optimization and the Low-rank Experts Problem. NIPS 2017: 4747-4755 - [i10]Tomer Koren, Roi Livni, Yishay Mansour:
Bandits with Movement Costs and Adaptive Pricing. CoRR abs/1702.07444 (2017) - [i9]Pravesh K. Kothari, Roi Livni:
Learning by Refuting. CoRR abs/1709.03871 (2017) - [i8]Tomer Koren, Roi Livni, Yishay Mansour:
Multi-Armed Bandits with Metric Movement Costs. CoRR abs/1710.08997 (2017) - [i7]Daniel M. Kane, Roi Livni, Shay Moran, Amir Yehudayoff:
On Communication Complexity of Classification Problems. CoRR abs/1711.05893 (2017) - [i6]Daniel M. Kane, Roi Livni, Shay Moran, Amir Yehudayoff:
On Communication Complexity of Classification Problems. Electron. Colloquium Comput. Complex. TR17 (2017) - 2016
- [b1]Roi Livni:
Representation learning theory (שער נוסף בעברית: תורת למידת הייצוג). Hebrew University of Jerusalem, Israel, 2016 - [c8]Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson:
Improper Deep Kernels. AISTATS 2016: 1159-1167 - [c7]Elad Hazan, Tomer Koren, Roi Livni, Yishay Mansour:
Online Learning with Low Rank Experts. COLT 2016: 1096-1114 - [c6]Michal Feldman, Tomer Koren, Roi Livni, Yishay Mansour, Aviv Zohar:
Online Pricing with Strategic and Patient Buyers. NIPS 2016: 3864-3872 - [i5]Elad Hazan, Tomer Koren, Roi Livni, Yishay Mansour:
Online Learning with Low Rank Experts. CoRR abs/1603.06352 (2016) - [i4]Amir Globerson, Roi Livni:
Learning Infinite-Layer Networks: Beyond the Kernel Trick. CoRR abs/1606.05316 (2016) - 2015
- [c5]Elad Hazan, Roi Livni, Yishay Mansour:
Classification with Low Rank and Missing Data. ICML 2015: 257-266 - [i3]Elad Hazan, Roi Livni, Yishay Mansour:
Classification with Low Rank and Missing Data. CoRR abs/1501.03273 (2015) - 2014
- [c4]Roi Livni, Shai Shalev-Shwartz, Ohad Shamir:
On the Computational Efficiency of Training Neural Networks. NIPS 2014: 855-863 - [i2]Roi Livni, Shai Shalev-Shwartz, Ohad Shamir:
On the Computational Efficiency of Training Neural Networks. CoRR abs/1410.1141 (2014) - 2013
- [c3]Roi Livni, Pierre Simon:
Honest Compressions and Their Application to Compression Schemes. COLT 2013: 77-92 - [c2]Roi Livni, David Lehavi, Sagi Schein, Hila Nachlieli, Shai Shalev-Shwartz, Amir Globerson:
Vanishing Component Analysis. ICML (1) 2013: 597-605 - [i1]Roi Livni, Shai Shalev-Shwartz, Ohad Shamir:
A Provably Efficient Algorithm for Training Deep Networks. CoRR abs/1304.7045 (2013) - 2012
- [c1]Roi Livni, Koby Crammer, Amir Globerson:
A Simple Geometric Interpretation of SVM using Stochastic Adversaries. AISTATS 2012: 722-730
Coauthor Index
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last updated on 2024-09-25 01:42 CEST by the dblp team
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