WiMAX in the Classroom: Designing a Cellular Networking Hands-On Lab. - Jelena Marasevic; Jan Janak; Henning Schulzrinne; Gil Zussman (GREE, 2013)

A Fast Distributed Algorithm for α-Fair Packing Problems. - Jelena Marasevic; Clifford Stein 001; Gil Zussman (CoRR, 2015)

Full-duplex in a hand-held device - From fundamental physics to complex integrated circuits, systems and networks: An overview of the Columbia FlexICoN project. - Harish Krishnaswamy; Gil Zussman; Jin Zhou 001; Jelena Marasevic; Tolga Dinc; Negar Reiskarimian; Tingjun Chen (ACSSC, 2016)

A Fast Distributed Stateless Algorithm for alpha-Fair Packing Problems. - Jelena Marasevic; Clifford Stein 001; Gil Zussman (ICALP, 2016)

Full-duplex wireless: algorithms and rate improvement bounds for integrated circuit implementations. - Jelena Marasevic; Tingjun Chen; Jin Zhou 001; Negar Reiskarimian; Harish Krishnaswamy; Gil Zussman (HotWireless@MobiCom, 2016)

Full-duplex wireless based on a small-form-factor analog self-interference canceller: demo. - Tingjun Chen; Jin Zhou 001; Nicole Grimwood; Rel Fogel; Jelena Marasevic; Harish Krishnaswamy; Gil Zussman (MobiHoc, 2016)

On the capacity regions of single-channel and multi-channel full-duplex links. - Jelena Marasevic; Gil Zussman (MobiHoc, 2016)

Resource Allocation in Wireless Networks: Theory and Applications. - Jelena Marasevic (2016)

Demo abstract: Full-duplex with a compact frequency domain equalization-based RF canceller. - Tingjun Chen; Jin Zhou 001; Mahmood Baraani Dastjerdi; Jelena Diakonikolas; Harish Krishnaswamy; Gil Zussman (INFOCOM Workshops, 2017)

Max-min Fair Rate Allocation and Routing in Energy Harvesting Networks: Algorithmic Analysis. - Jelena Marasevic; Clifford Stein 001; Gil Zussman (Algorithmica, 2017)

Integrated Full Duplex Radios. - Jin Zhou 001; Negar Reiskarimian; Jelena Diakonikolas; Tolga Dinc; Tingjun Chen; Gil Zussman; Harish Krishnaswamy (IEEE Commun. Mag., 2017)

Solving Packing and Covering LPs in Õ(1/ε2) Distributed Iterations with a Single Algorithm and Simpler Analysis. - Jelena Diakonikolas; Lorenzo Orecchia (CoRR, 2017)

Resource Allocation and Rate Gains in Practical Full-Duplex Systems. - Jelena Marasevic; Jin Zhou 001; Harish Krishnaswamy; Yuan Zhong 001; Gil Zussman (IEEE/ACM Trans. Netw., 2017)

Fairness and Delay in Heterogeneous Half- and Full-Duplex Wireless Networks. - Tingjun Chen; Jelena Diakonikolas; Javad Ghaderi; Gil Zussman (ACSSC, 2018)

On Acceleration with Noise-Corrupted Gradients. - Michael Cohen 001; Jelena Diakonikolas; Lorenzo Orecchia (ICML, 2018)

Alternating Randomized Block Coordinate Descent. - Jelena Diakonikolas; Lorenzo Orecchia (ICML, 2018)

Accelerated Extra-Gradient Descent: A Novel Accelerated First-Order Method. - Jelena Diakonikolas; Lorenzo Orecchia (ITCS, 2018)

Width-Independence Beyond Linear Objectives: Distributed Fair Packing and Covering Algorithms. - Jelena Diakonikolas; Maryam Fazel; Lorenzo Orecchia (CoRR, 2018)

On the Rate Regions of Single-Channel and Multi-Channel Full-Duplex Links. - Jelena Diakonikolas; Gil Zussman (IEEE/ACM Trans. Netw., 2018)

A Hierarchical WDM-Based Scalable Data Center Network Architecture. - Maotong Xu; Jelena Diakonikolas; Eytan H. Modiano; Suresh Subramaniam 001 (ICC, 2019)

Conjugate Gradients and Accelerated Methods Unified: The Approximate Duality Gap View. - Jelena Diakonikolas; Lorenzo Orecchia (CoRR, 2019)

The Approximate Duality Gap Technique: A Unified Theory of First-Order Methods. - Jelena Diakonikolas; Lorenzo Orecchia (SIAM J. Optim., 2019)

Langevin Monte Carlo without smoothness. - Niladri S. Chatterji; Jelena Diakonikolas; Michael I. Jordan; Peter L. Bartlett (AISTATS, 2020)

Locally Accelerated Conditional Gradients. - Jelena Diakonikolas; Alejandro Carderera; Sebastian Pokutta (AISTATS, 2020)

Halpern Iteration for Near-Optimal and Parameter-Free Monotone Inclusion and Strong Solutions to Variational Inequalities. - Jelena Diakonikolas (COLT, 2020)

Lower Bounds for Parallel and Randomized Convex Optimization. - Jelena Diakonikolas; Cristóbal Guzmán (J. Mach. Learn. Res., 2020)

Fair Packing and Covering on a Relative Scale. - Jelena Diakonikolas; Maryam Fazel; Lorenzo Orecchia (SIAM J. Optim., 2020)

Hybrid Scheduling in Heterogeneous Half- and Full-Duplex Wireless Networks. - Tingjun Chen; Jelena Diakonikolas; Javad Ghaderi; Gil Zussman (IEEE/ACM Trans. Netw., 2020)

Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization. - Jelena Diakonikolas; Constantinos Daskalakis; Michael I. Jordan (AISTATS, 2021)

Parameter-free Locally Accelerated Conditional Gradients. - Alejandro Carderera; Jelena Diakonikolas; Cheuk Yin Lin; Sebastian Pokutta (ICML, 2021)

Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums. - Chaobing Song; Stephen J. Wright 001; Jelena Diakonikolas (ICML, 2021)

Complementary Composite Minimization, Small Gradients in General Norms, and Applications to Regression Problems. - Jelena Diakonikolas; Cristóbal Guzmán (CoRR, 2021)

Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization. - Jelena Diakonikolas; Puqian Wang (CoRR, 2021)

Fast Cyclic Coordinate Dual Averaging with Extrapolation for Generalized Variational Inequalities. - Chaobing Song; Jelena Diakonikolas (CoRR, 2021)

Generalized Momentum-Based Methods: A Hamiltonian Perspective. - Jelena Diakonikolas; Michael I. Jordan (SIAM J. Optim., 2021)

Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions. - Xufeng Cai; Chaobing Song; Cristóbal Guzmán; Jelena Diakonikolas (NeurIPS, 2022)

A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data. - Jelena Diakonikolas; Chenghui Li; Swati Padmanabhan; Chaobing Song (NeurIPS, 2022)

Coordinate Linear Variance Reduction for Generalized Linear Programming. - Chaobing Song; Cheuk Yin Lin; Stephen J. Wright 001; Jelena Diakonikolas (NeurIPS, 2022)

A Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusion Problems. - Xufeng Cai; Chaobing Song; Cristóbal Guzmán; Jelena Diakonikolas (CoRR, 2022)

Potential Function-Based Framework for Minimizing Gradients in Convex and Min-Max Optimization. - Jelena Diakonikolas; Puqian Wang (SIAM J. Optim., 2022)

Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise. - Ilias Diakonikolas; Jelena Diakonikolas; Daniel M. Kane; Puqian Wang; Nikos Zarifis (COLT, 2023)

Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization. - Xufeng Cai; Chaobing Song; Stephen J. Wright 001; Jelena Diakonikolas (ICML, 2023)

Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization. - Cheuk Yin Lin; Chaobing Song; Jelena Diakonikolas (ICML, 2023)

Robustly Learning a Single Neuron via Sharpness. - Puqian Wang; Nikos Zarifis; Ilias Diakonikolas; Jelena Diakonikolas (ICML, 2023)

Block-Coordinate Methods and Restarting for Solving Extensive-Form Games. - Darshan Chakrabarti; Jelena Diakonikolas; Christian Kroer (NeurIPS, 2023)

Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise. - Ilias Diakonikolas; Jelena Diakonikolas; Daniel Kane 001; Puqian Wang; Nikos Zarifis (NeurIPS, 2023)

Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing. - Shuyao Li 001; Yu Cheng 002; Ilias Diakonikolas; Jelena Diakonikolas; Rong Ge 001; Stephen J. Wright 001 (NeurIPS, 2023)

Empirical Risk Minimization with Shuffled SGD: A Primal-Dual Perspective and Improved Bounds. - Xufeng Cai; Cheuk Yin Lin; Jelena Diakonikolas (CoRR, 2023)

Cyclic Coordinate Dual Averaging with Extrapolation. - Chaobing Song; Jelena Diakonikolas (SIAM J. Optim., 2023)

Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions. - Xufeng Cai; Ahmet Alacaoglu; Jelena Diakonikolas (ICLR, 2024)

Robustly Learning Single-Index Models via Alignment Sharpness. - Nikos Zarifis; Puqian Wang; Ilias Diakonikolas; Jelena Diakonikolas (ICML, 2024)

Tighter Convergence Bounds for Shuffled SGD via Primal-Dual Perspective. - Xufeng Cai; Cheuk Yin Lin; Jelena Diakonikolas (NeurIPS, 2024)

Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise. - Shuyao Li 001; Sushrut Karmalkar; Ilias Diakonikolas; Jelena Diakonikolas (NeurIPS, 2024)

Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization. - Ronak Mehta; Jelena Diakonikolas; Zaïd Harchaoui (NeurIPS, 2024)

Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models. - Puqian Wang; Nikos Zarifis; Ilias Diakonikolas; Jelena Diakonikolas (NeurIPS, 2024)

Last Iterate Convergence of Incremental Methods and Applications in Continual Learning. - Xufeng Cai; Jelena Diakonikolas (CoRR, 2024)

A Primal-Dual Algorithm for Faster Distributionally Robust Optimization. - Ronak Mehta; Jelena Diakonikolas; Zaïd Harchaoui (CoRR, 2024)

Optimization on a Finer Scale: Bounded Local Subgradient Variation Perspective. - Jelena Diakonikolas; Cristóbal Guzmán (CoRR, 2024)

Complementary composite minimization, small gradients in general norms, and applications. - Jelena Diakonikolas; Cristóbal Guzmán (Math. Program., 2024)

Robustly Learning Monotone Generalized Linear Models via Data Augmentation. - Nikos Zarifis; Puqian Wang; Ilias Diakonikolas; Jelena Diakonikolas (COLT, 2025)

Last Iterate Convergence of Incremental Methods as a Model of Forgetting. - Xufeng Cai; Jelena Diakonikolas (ICLR, 2025)

Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm. - Xufeng Cai; Jason M. Altschuler; Jelena Diakonikolas (CoRR, 2025)

Distributionally Robust Optimization with Adversarial Data Contamination. - Shuyao Li 001; Ilias Diakonikolas; Jelena Diakonikolas (CoRR, 2025)

Robustly Learning Monotone Single-Index Models. - Puqian Wang; Nikos Zarifis; Ilias Diakonikolas; Jelena Diakonikolas (CoRR, 2025)