Logarithmic pruning of FFT frequencies. - S. Barash; Yaacov Ritov (IEEE Trans. Signal Process., 1993)

On Series Expansions and Stochastic Matrices. - Moshe Haviv; Yaacov Ritov (SIAM J. Matrix Anal. Appl., 1993)

Estimating linear functionals of a PET image. - Peter J. Bickel; Yaacov Ritov (IEEE Trans. Medical Imaging, 1995)

Tracking Many Objects with Many Sensors. - Hanna Pasula; Stuart Russell 001; Michael Ostland; Yaacov Ritov (IJCAI, 1999)

Homogeneous Customers Renege from Invisible Queues at Random Times under Deteriorating Waiting Conditions. - Moshe Haviv; Yaacov Ritov (Queueing Syst. Theory Appl., 2001)

Minimizing flow-time on a single machine with integer batch sizes. - Gur Mosheiov; Daniel Oron; Yaacov Ritov (Oper. Res. Lett., 2005)

Some Theory for Generalized Boosting Algorithms. - Peter J. Bickel; Yaacov Ritov; Alon Zakai (J. Mach. Learn. Res., 2006)

Is Pinocchio's Nose Long or His Head Small? Learning Shape Distances for Classification. - Daniel Gill; Yaacov Ritov; Gideon Dror (ISVC (1), 2007)

How Local Should a Learning Method Be?. - Alon Zakai; Yaacov Ritov (COLT, 2008)

Semiparametric shift estimation for alignment of ECG data. - Thomas Trigano; Uri Isserles; Yaacov Ritov (EUSIPCO, 2008)

LDR-LLE: LLE with Low-Dimensional Neighborhood Representation. - Yair Goldberg; Yaacov Ritov (ISVC (2), 2008)

Manifold Learning: The Price of Normalization. - Yair Goldberg; Alon Zakai; Dan Kushnir; Yaacov Ritov (J. Mach. Learn. Res., 2008)

Consistency and Localizability. - Alon Zakai; Yaacov Ritov (J. Mach. Learn. Res., 2009)

Local procrustes for manifold embedding: a measure of embedding quality and embedding algorithms. - Yair Goldberg; Yaacov Ritov (Mach. Learn., 2009)

Semiparametric Curve Alignment and Shift Density Estimation for Biological Data. - Thomas Trigano; Uri Isserles; Yaacov Ritov (IEEE Trans. Signal Process., 2011)

Bootstrap confidence bands and partial linear quantile regression. - Song Song; Yaacov Ritov; Wolfgang K. Härdle (J. Multivar. Anal., 2012)

Sparse Regression Algorithm for Activity Estimation in γ Spectrometry. - Yann Sepulcre; Thomas Trigano; Yaacov Ritov (IEEE Trans. Signal Process., 2013)

Tie the straps: Uniform bootstrap confidence bands for semiparametric additive models. - Wolfgang K. Härdle; Yaacov Ritov; Weining Wang 004 (J. Multivar. Anal., 2015)

On conditional parity as a notion of non-discrimination in machine learning. - Yaacov Ritov; Yuekai Sun; Ruofei Zhao (CoRR, 2017)

Identifying a Minimal Class of Models for High-dimensional Data. - Daniel Nevo; Yaacov Ritov (J. Mach. Learn. Res., 2017)

Sparse Reconstruction Algorithm for Nonhomogeneous Counting Rate Estimation. - Thomas Trigano; Yann Sepulcre; Yaacov Ritov (IEEE Trans. Signal Process., 2017)

Nonparametric Empirical Bayes Estimation and Testing for Sparse and Heteroscedastic Signals. - Junhui Cai; Xu Han; Yaacov Ritov; Linda Zhao (CoRR, 2021)

Inference In High-dimensional Single-Index Models Under Symmetric Designs. - Hamid Eftekhari; Moulinath Banerjee; Yaacov Ritov (J. Mach. Learn. Res., 2021)

Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness. - Seamus Somerstep; Yaacov Ritov; Yuekai Sun (FAccT, 2024)

Learning in reverse causal strategic environments with ramifications on two sided markets. - Seamus Somerstep; Yuekai Sun; Yaacov Ritov (ICLR, 2024)

A statistical framework for weak-to-strong generalization. - Seamus Somerstep; Felipe Maia Polo; Moulinath Banerjee; Yaacov Ritov; Mikhail Yurochkin; Yuekai Sun (CoRR, 2024)

A transfer learning framework for weak to strong generalization. - Seamus Somerstep; Felipe Maia Polo; Moulinath Banerjee; Yaacov Ritov; Mikhail Yurochkin; Yuekai Sun (ICLR, 2025)