← Authors

Vladimir braverman

47 papers · 2017–2026 · 15 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (8) πŸ—ΊοΈ Taxonomy Completionist (14) 🐝 Cross-Pollinator (4) 🌈 Renaissance Researcher (9) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (14) 🐣 Hot Topic Early Bird πŸ† Keyword Champion (3) 🀝 Dynamic Duo (17) πŸ—ƒοΈ Keyword Collector (179) ❓ The Questioner ⚑ Prolific Year (6) πŸš€ Conference Pioneer πŸ’Ž Century Club (46) πŸ”₯ Unstoppable (9) πŸ“ˆ Trend Setter

Conferences

ICML (15) NIPS (11) AISTATS (3) ICLR (3) MIDL (3) ACML (2) COLT (2) ACL (1) EACL (1) EMNLP (1) JMLR (1) NAACL (1) NSDI (1) OSDI (1) UAI (1)

Top co-authors

Jingfeng Wu (17) Quanquan Gu (9) Difan Zou (9) Samson Zhou (8) Lin Yang (6) Robert Krauthgamer (6) Sham Kakade (5) Ion Stoica (4) Dan Feldman (4) Raman Arora (4)

Research topics

Differential Privacy (2) Optimization (1)

Keywords

stochastic gradient descent (8) coreset construction (6) linear regression (4) federated learning (3) iterate averaging (3) excess risk (3) large language model (3) distributed learning (3) streaming algorithm (3) sparse matrix (2) communication efficiency (2) approximation algorithm (2) stochastic optimization (2) gradient descent (2) matrix approximation (2) ridge regression (2) catastrophic forgetting (2) differential privacy (2) learning theory (2) k-means clustering (2)

Papers

FaithLM: Towards Faithful Explanations for Large Language Models EACL 2026
Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time AISTATS 2025
CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems ACL 2025
Learning-Augmented Hierarchical Clustering ICML 2025
Relative Error Fair Clustering in the Weak-Strong Oracle Model ICML 2025
Self-Ensemble: Mitigating Confidence Distortion for Large Language Models EMNLP 2025
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR 2024
KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache ICML 2024
Towards a Collective Medical Imaging AI: Enabling Continual Learning from Peers MIDL 2024
Provable Data Subset Selection For Efficient Neural Networks Training ICML 2023
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron ICML 2023
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability NIPS 2023
Benign Overfitting of Constant-Stepsize SGD for Linear Regression JMLR 2023
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging MIDL 2023
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance NIPS 2023
AutoCoreset: An Automatic Practical Coreset Construction Framework ICML 2023
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression ICML 2022
Pretrained Models for Multilingual Federated Learning NAACL 2022
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift NIPS 2022
Gap-Dependent Unsupervised Exploration for Reinforcement Learning AISTATS 2022
New Coresets for Projective Clustering and Applications AISTATS 2022
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime NIPS 2022
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning NIPS 2021
Adversarial Robustness of Streaming Algorithms through Importance Sampling NIPS 2021
The Benefits of Implicit Regularization from SGD in Least Squares Problems NIPS 2021
Coresets for Clustering with Missing Values NIPS 2021
Efficient Coreset Constructions via Sensitivity Sampling ACML 2021
Lifelong Learning with Sketched Structural Regularization ACML 2021
Near-Optimal Entrywise Sampling of Numerically Sparse Matrices COLT 2021
Benign Overfitting of Constant-Stepsize SGD for Linear Regression COLT 2021
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate ICLR 2021
Twenty Years After: Hierarchical Core-Stateless Fair Queueing NSDI 2021
On the Noisy Gradient Descent that Generalizes as SGD ICML 2020
FetchSGD: Communication-Efficient Federated Learning with Sketching ICML 2020
Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension ICML 2020
Coresets for Clustering in Graphs of Bounded Treewidth ICML 2020
Multitask radiological modality invariant landmark localization using deep reinforcement learning MIDL 2020
Data-Independent Neural Pruning via Coresets ICLR 2020
Obtaining Adjustable Regularization for Free via Iterate Averaging ICML 2020
Online Factorization and Partition of Complex Networks by Random Walk UAI 2019
Coresets for Ordered Weighted Clustering ICML 2019
Communication-efficient Distributed SGD with Sketching NIPS 2019
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order ICML 2018
ASAP: Fast, Approximate Graph Pattern Mining at Scale OSDI 2018
The Physical Systems Behind Optimization Algorithms NIPS 2018
Differentially Private Robust Low-Rank Approximation NIPS 2018
Clustering High Dimensional Dynamic Data Streams ICML 2017