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(2023). Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity.

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(2023). Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy.

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(2023). Learning-Rate-Free Learning by D-Adaptation.

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(2023). Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes.

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(2022). Super-Universal Regularized Newton Method.

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(2022). Adaptive Learning Rates for Faster Stochastic Gradient Methods.

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(2022). Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays.

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(2022). ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!. ICML.

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(2022). Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization.

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(2021). IntSGD: Adaptive Floatless Compression of Stochastic Gradients. ICLR.

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(2021). Proximal and Federated Random Reshuffling. ICML.

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(2020). Random Reshuffling: Simple Analysis with Vast Improvements. NeurIPS.

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(2020). Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. JOTA.

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(2019). Adaptive Gradient Descent without Descent. ICML.

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(2019). Tighter Theory for Local SGD on Identical and Heterogeneous Data. AISTATS.

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(2019). First Analysis of Local GD on Heterogeneous Data.

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(2019). MISO is Making a Comeback With Better Proofs and Rates.

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(2019). DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate. AISTATS.


(2019). Revisiting Stochastic Extragradient. AISTATS.

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(2019). Stochastic Distributed Learning with Gradient Quantization and Double Variance Reduction. Optimization Methods and Software.

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(2019). Distributed Learning with Compressed Gradient Differences.

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(2019). 99% of Worker-Master Communication in Distributed Optimization Is Not Needed. UAI.

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(2018). SEGA: Variance Reduction via Gradient Sketching. NeurIPS.

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(2018). A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning. ICML.


(2018). A Distributed Flexible Delay-tolerant Proximal Gradient Algorithm. SIOPT.

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