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ORCIDDan Mikulincer
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2020 – today
- 2025
[j3]Dan Mikulincer
, Daniel Reichman:
Size and Depth of Monotone Neural Networks: Interpolation and Approximation. IEEE Trans. Neural Networks Learn. Syst. 36(4): 6314-6325 (2025)- [i12]Elisabetta Cornacchia, Dan Mikulincer, Elchanan Mossel:
Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions. CoRR abs/2502.06443 (2025) - 2024
- [j2]Sébastien Bubeck, Dan Mikulincer:
How to Trap a Gradient Flow. SIAM J. Comput. 53(4): 803-824 (2024) - 2023
- [c8]Omri Ben-Eliezer, Dan Mikulincer, Elchanan Mossel, Madhu Sudan:
Is This Correct? Let's Check! ITCS 2023: 15:1-15:11 - [c7]Sander Borst, Daniel Dadush, Dan Mikulincer:
Integrality Gaps for Random Integer Programs via Discrepancy. SODA 2023: 1692-1733 - [c6]Ronen Eldan, Dan Mikulincer, Prasad Raghavendra:
Noise Stability on the Boolean Hypercube via a Renormalized Brownian Motion. STOC 2023: 661-671 - [i11]Zongchen Chen, Dan Mikulincer, Daniel Reichman, Alexander S. Wein:
Time Lower Bounds for the Metropolis Process and Simulated Annealing. CoRR abs/2312.13554 (2023) - 2022
- [j1]Ronen Eldan, Dan Mikulincer, Hester Pieters:
Community detection and percolation of information in a geometric setting. Comb. Probab. Comput. 31(6): 1048-1069 (2022) - [c5]Omri Ben-Eliezer, Dan Mikulincer, Ilias Zadik:
Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions. NeurIPS 2022 - [c4]Dan Mikulincer, Daniel Reichman:
Size and depth of monotone neural networks: interpolation and approximation. NeurIPS 2022 - [i10]Dan Mikulincer, Daniel Reichman:
Size and depth of monotone neural networks: interpolation and approximation. CoRR abs/2207.05275 (2022) - [i9]Ronen Eldan, Dan Mikulincer, Prasad Raghavendra:
Noise stability on the Boolean hypercube via a renormalized Brownian motion. CoRR abs/2208.06508 (2022) - [i8]Omri Ben-Eliezer, Dan Mikulincer, Ilias Zadik:
Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions. CoRR abs/2208.07438 (2022) - [i7]Omri Ben-Eliezer, Dan Mikulincer, Elchanan Mossel, Madhu Sudan:
Is this correct? Let's check! CoRR abs/2211.12301 (2022) - 2021
- [c3]Ronen Eldan, Dan Mikulincer, Tselil Schramm:
Non-asymptotic approximations of neural networks by Gaussian processes. COLT 2021: 1754-1775 - [i6]Ronen Eldan, Dan Mikulincer, Tselil Schramm:
Non-asymptotic approximations of neural networks by Gaussian processes. CoRR abs/2102.08668 (2021) - 2020
- [c2]Sébastien Bubeck, Dan Mikulincer:
How to Trap a Gradient Flow. COLT 2020: 940-960 - [c1]Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer:
Network size and size of the weights in memorization with two-layers neural networks. NeurIPS 2020 - [i5]Sébastien Bubeck, Dan Mikulincer:
How to trap a gradient flow. CoRR abs/2001.02968 (2020) - [i4]Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer:
Network size and weights size for memorization with two-layers neural networks. CoRR abs/2006.02855 (2020) - [i3]Ronen Eldan, Dan Mikulincer, Hester Pieters:
Community detection and percolation of information in a geometric setting. CoRR abs/2006.15574 (2020)
2010 – 2019
- 2019
- [i2]Ronen Eldan, Dan Mikulincer:
Stability of the Shannon-Stam inequality via the Föllmer process. CoRR abs/1903.07140 (2019) - 2016
- [i1]Ronen Eldan, Dan Mikulincer:
Information and dimensionality of anisotropic random geometric graphs. CoRR abs/1609.02490 (2016)
Coauthor Index
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