Information and dimensionality of anisotropic random geometric graphs. - Ronen Eldan; Dan Mikulincer (CoRR, 2016)

Stability of the Shannon-Stam inequality via the Föllmer process. - Ronen Eldan; Dan Mikulincer (CoRR, 2019)

Network size and size of the weights in memorization with two-layers neural networks. - Sébastien Bubeck; Ronen Eldan; Yin Tat Lee; Dan Mikulincer (NeurIPS, 2020)

Network size and weights size for memorization with two-layers neural networks. - Sébastien Bubeck; Ronen Eldan; Yin Tat Lee; Dan Mikulincer (CoRR, 2020)

Non-asymptotic approximations of neural networks by Gaussian processes. - Ronen Eldan; Dan Mikulincer; Tselil Schramm (COLT, 2021)

Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions. - Omri Ben-Eliezer; Dan Mikulincer; Ilias Zadik (NeurIPS, 2022)

Community detection and percolation of information in a geometric setting. - Ronen Eldan; Dan Mikulincer; Hester Pieters (Comb. Probab. Comput., 2022)

Is This Correct? Let's Check! - Omri Ben-Eliezer; Dan Mikulincer; Elchanan Mossel; Madhu Sudan 001 (ITCS, 2023)

Integrality Gaps for Random Integer Programs via Discrepancy. - Sander Borst; Daniel Dadush; Dan Mikulincer (SODA, 2023)

Noise Stability on the Boolean Hypercube via a Renormalized Brownian Motion. - Ronen Eldan; Dan Mikulincer; Prasad Raghavendra (STOC, 2023)

Time Lower Bounds for the Metropolis Process and Simulated Annealing. - Zongchen Chen; Dan Mikulincer; Daniel Reichman 001; Alexander S. Wein (CoRR, 2023)

How to Trap a Gradient Flow. - Sébastien Bubeck; Dan Mikulincer (SIAM J. Comput., 2024)

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions. - Elisabetta Cornacchia; Dan Mikulincer; Elchanan Mossel (COLT, 2025)

Size and Depth of Monotone Neural Networks: Interpolation and Approximation. - Dan Mikulincer; Daniel Reichman 001 (IEEE Trans. Neural Networks Learn. Syst., 2025)