Vladimir braverman

FaithLM: Towards Faithful Explanations for Large Language Models EACL 2026 CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems ACL 2025 Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time AISTATS 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 AutoCoreset: An Automatic Practical Coreset Construction Framework ICML 2023 Benign Overfitting of Constant-Stepsize SGD for Linear Regression JMLR 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 NeurIPS 2023 Private Federated Frequency Estimation: Adapting to the Hardness of the Instance NeurIPS 2023 Provable Data Subset Selection For Efficient Neural Networks Training ICML 2023 Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging MIDL 2023 Gap-Dependent Unsupervised Exploration for Reinforcement Learning AISTATS 2022 Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression ICML 2022 New Coresets for Projective Clustering and Applications AISTATS 2022 Pretrained Models for Multilingual Federated Learning NAACL 2022 Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime NeurIPS 2022 The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift NeurIPS 2022 Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning NeurIPS 2021 Adversarial Robustness of Streaming Algorithms through Importance Sampling NeurIPS 2021 Benign Overfitting of Constant-Stepsize SGD for Linear Regression COLT 2021 Coresets for Clustering with Missing Values NeurIPS 2021 Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate ICLR 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 The Benefits of Implicit Regularization from SGD in Least Squares Problems NeurIPS 2021 Twenty Years After: Hierarchical Core-Stateless Fair Queueing NSDI 2021 Coresets for Clustering in Graphs of Bounded Treewidth ICML 2020 Data-Independent Neural Pruning via Coresets ICLR 2020 FetchSGD: Communication-Efficient Federated Learning with Sketching ICML 2020 Multitask radiological modality invariant landmark localization using deep reinforcement learning MIDL 2020 Obtaining Adjustable Regularization for Free via Iterate Averaging ICML 2020 On the Noisy Gradient Descent that Generalizes as SGD ICML 2020 Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension ICML 2020 Communication-efficient Distributed SGD with Sketching NeurIPS 2019 Coresets for Ordered Weighted Clustering ICML 2019 Online Factorization and Partition of Complex Networks by Random Walk UAI 2019 ASAP: Fast, Approximate Graph Pattern Mining at Scale OSDI 2018 Differentially Private Robust Low-Rank Approximation NeurIPS 2018 Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order ICML 2018 The Physical Systems Behind Optimization Algorithms NeurIPS 2018 Clustering High Dimensional Dynamic Data Streams ICML 2017