← Authors

Volkan Cevher

156 papers · 2008–2026 · 15 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (8) 🌍 Conference Polyglot (14) 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (26) 🧭 Keyword Pioneer πŸƒ Academic Marathon (17) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🏠 Conference Loyalist (55) 🌟 Keyword Trendsetter Combo (3) 🀝 Dynamic Duo (18) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (12) πŸ† Keyword Champion (2) πŸ† Grand Slam ❓ The Questioner (3) πŸ—ƒοΈ Keyword Collector (132) πŸš€ Conference Pioneer πŸ”₯ Unstoppable (14) ⚑ Prolific Year (17) πŸ“ˆ Trend Setter πŸ’Ž Century Club (155)

Conferences

NIPS (55) ICML (45) ICLR (23) AISTATS (16) JMLR (5) ACL (2) EMNLP (2) AAAI (1) ALT (1) COLT (1) CVPR (1) EACL (1) IJCAI (1) NAACL (1) SEMEVAL (1)

Top co-authors

Grigorios Chrysos (18) Fanghui Liu (17) Ali Kavis (13) Kimon Antonakopoulos (12) Stratis Skoulakis (11) Luca Viano (11) Paul Rolland (10) Thomas Pethick (10) Jonathan Scarlett (10) Alp Yurtsever (10)

Research topics

Probability (1) Education (1)

Keywords

convex optimization (23) stochastic optimization (12) sample complexity (9) regret bound (8) stochastic gradient descent (8) non-convex optimization (7) generalization bound (7) neural network (7) bayesian optimization (6) machine unlearning (6) convergence rate (6) conditional gradient (5) primal-dual method (5) gaussian process (5) reproducing kernel hilbert space (5) variance reduction (5) neural tangent kernel (5) convex minimization (4) nash equilibrium (4) inverse reinforcement learning (4)

Papers

BLUR: A Bi-Level Optimization Approach for LLM Unlearning EACL 2026
SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models SEMEVAL 2025
Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate NAACL 2025
Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning ICML 2025
IL-SOAR : Imitation Learning with Soft Optimistic Actor cRitic ICML 2025
Accelerating Spectral Clustering under Fairness Constraints ICML 2025
Training Deep Learning Models with Norm-Constrained LMOs ICML 2025
Layer-wise Quantization for Quantized Optimistic Dual Averaging ICML 2025
Best of Both Worlds: Regret Minimization versus Minimax Play ICML 2025
Continuous-Time Analysis of Heavy Ball Momentum in Min-Max Games ICML 2025
Generalization of noisy SGD in unbounded non-convex settings ICML 2025
Adversarial Training for Defense Against Label Poisoning Attacks ICLR 2025
Certified Robustness Under Bounded Levenshtein Distance ICLR 2025
Faster Inference of Flow-Based Generative Models via Improved Data-Noise Coupling ICLR 2025
Efficient Interpolation between Extragradient and Proximal Methods for Weak MVIs ICLR 2025
Addressing Label Shift in Distributed Learning via Entropy Regularization​ ICLR 2025
How Gradient descent balances features: A dynamical analysis for two-layer neural networks ICLR 2025
Quantum-PEFT: Ultra parameter-efficient fine-tuning ICLR 2025
LUME: LLM Unlearning with Multitask Evaluations EMNLP 2025
SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models ACL 2025
Not Every Token Needs Forgetting: Selective Unlearning Balancing Forgetting and Utility in Large Language Models EMNLP 2025
Universal Gradient Methods for Stochastic Convex Optimization ICML 2024
$\boldsymbol{\mu}\mathbf{P^2}$: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling NIPS 2024
SAMPa: Sharpness-aware Minimization Parallelized NIPS 2024
On Feature Learning in Structured State Space Models NIPS 2024
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces NIPS 2024
Membership Inference Attacks against Large Vision-Language Models NIPS 2024
Extreme Miscalibration and the Illusion of Adversarial Robustness ACL 2024
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate AISTATS 2024
Efficient Continual Finite-Sum Minimization ICLR 2024
Advancing the Lower Bounds: an Accelerated, Stochastic, Second-order Method with Optimal Adaptation to Inexactness ICLR 2024
Multilinear Operator Networks ICLR 2024
Generalization of Scaled Deep ResNets in the Mean-Field Regime ICLR 2024
Robust NAS under adversarial training: benchmark, theory, and beyond ICLR 2024
Adversarial Training Should Be Cast as a Non-Zero-Sum Game ICLR 2024
Efficient local linearity regularization to overcome catastrophic overfitting ICLR 2024
Revisiting Character-level Adversarial Attacks for Language Models ICML 2024
REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates ICML 2024
High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization ICML 2024
Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations ICML 2024
Truly No-Regret Learning in Constrained MDPs ICML 2024
MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descent ICML 2024
Learning to Remove Cuts in Integer Linear Programming ICML 2024
Imitation Learning in Discounted Linear MDPs without exploration assumptions ICML 2024
Improving SAM Requires Rethinking its Optimization Formulation ICML 2024
On the Generalization of Stochastic Gradient Descent with Momentum JMLR 2024
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks JMLR 2024
Benign Overfitting in Deep Neural Networks under Lazy Training ICML 2023
Stable Nonconvex-Nonconcave Training via Linear Interpolation NIPS 2023
On the Convergence of Encoder-only Shallow Transformers NIPS 2023
Distributed Extra-gradient with Optimal Complexity and Communication Guarantees ICLR 2023
Solving stochastic weak Minty variational inequalities without increasing batch size ICLR 2023
Alternation makes the adversary weaker in two-player games NIPS 2023
Finding Actual Descent Directions for Adversarial Training ICLR 2023
Regularization of Polynomial Networks for Image Recognition CVPR 2023
What can online reinforcement learning with function approximation benefit from general coverage conditions? ICML 2023
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks NIPS 2023
Semi Bandit dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees. ICML 2023
Exponential Lower Bounds for Fictitious Play in Potential Games NIPS 2023
Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling NIPS 2023
DiGress: Discrete Denoising diffusion for graph generation ICLR 2023
Maximum Independent Set: Self-Training through Dynamic Programming NIPS 2023
Efficient Online Clustering with Moving Costs NIPS 2023
When do Minimax-fair Learning and Empirical Risk Minimization Coincide? ICML 2023
Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration NIPS 2022
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning NIPS 2022
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization) NIPS 2022
On the Double Descent of Random Features Models Trained with SGD NIPS 2022
Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods NIPS 2022
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study NIPS 2022
Proximal Point Imitation Learning NIPS 2022
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models ICML 2022
UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees ICML 2022
A Natural Actor-Critic Framework for Zero-Sum Markov Games ICML 2022
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization AISTATS 2022
Generalization Properties of NAS under Activation and Skip Connection Search NIPS 2022
High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize ICLR 2022
The Spectral Bias of Polynomial Neural Networks ICLR 2022
Controlling the Complexity and Lipschitz Constant improves Polynomial Nets ICLR 2022
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems ICLR 2022
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization NIPS 2022
No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation NIPS 2022
Sound and Complete Verification of Polynomial Networks NIPS 2022
Sifting through the noise: Universal first-order methods for stochastic variational inequalities NIPS 2021
The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets ICML 2021
Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach ICML 2021
STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization NIPS 2021
The Effect of the Intrinsic Dimension on the Generalization of Quadratic Classifiers NIPS 2021
Convergence of adaptive algorithms for constrained weakly convex optimization NIPS 2021
Subquadratic Overparameterization for Shallow Neural Networks NIPS 2021
A first-order primal-dual method with adaptivity to local smoothness NIPS 2021
Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch NIPS 2021
A new regret analysis for Adam-type algorithms ICML 2020
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems NIPS 2020
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics NIPS 2020
Lipschitz constant estimation of Neural Networks via sparse polynomial optimization ICLR 2020
Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections JMLR 2020
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms JMLR 2020
Random extrapolation for primal-dual coordinate descent ICML 2020
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks ICML 2020
Double-Loop Unadjusted Langevin Algorithm ICML 2020
Conditional gradient methods for stochastically constrained convex minimization ICML 2020
Fast and Provable ADMM for Learning with Generative Priors NIPS 2019
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization NIPS 2019
Iterative Classroom Teaching AAAI 2019
Almost surely constrained convex optimization ICML 2019
Finding Mixed Nash Equilibria of Generative Adversarial Networks ICML 2019
On Certifying Non-Uniform Bounds against Adversarial Attacks ICML 2019
Efficient learning of smooth probability functions from Bernoulli tests with guarantees ICML 2019
A Conditional-Gradient-Based Augmented Lagrangian Framework ICML 2019
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator ICML 2019
Interactive Teaching Algorithms for Inverse Reinforcement Learning IJCAI 2019
Stochastic Frank-Wolfe for Composite Convex Minimization NIPS 2019
An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints NIPS 2019
Adversarially Robust Optimization with Gaussian Processes NIPS 2018
Stochastic Three-Composite Convex Minimization with a Linear Operator AISTATS 2018
Dimension-free Information Concentration via Exp-Concavity ALT 2018
Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games ICML 2018
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods ICML 2018
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces ICML 2018
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming ICML 2018
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups AISTATS 2018
Robust Maximization of Non-Submodular Objectives AISTATS 2018
Online Adaptive Methods, Universality and Acceleration NIPS 2018
Combinatorial Penalties: Which structures are preserved by convex relaxations? AISTATS 2018
Mirrored Langevin Dynamics NIPS 2018
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach NIPS 2017
Lower Bounds on Active Learning for Graphical Model Selection AISTATS 2017
Faster Coordinate Descent via Adaptive Importance Sampling AISTATS 2017
Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage AISTATS 2017
Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization COLT 2017
Phase Transitions in the Pooled Data Problem NIPS 2017
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization NIPS 2017
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data NIPS 2017
Robust Submodular Maximization: A Non-Uniform Partitioning Approach ICML 2017
Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices AISTATS 2016
An Efficient Streaming Algorithm for the Submodular Cover Problem NIPS 2016
Stochastic Three-Composite Convex Minimization NIPS 2016
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation NIPS 2016
Time-Varying Gaussian Process Bandit Optimization AISTATS 2016
Convex Block-sparse Linear Regression with Expanders – Provably AISTATS 2016
Sparsistency of \ell_1-Regularized M-Estimators AISTATS 2015
A totally unimodular view of structured sparsity AISTATS 2015
WASP: Scalable Bayes via barycenters of subset posteriors AISTATS 2015
Composite Self-Concordant Minimization JMLR 2015
Stochastic Spectral Descent for Restricted Boltzmann Machines AISTATS 2015
A Universal Primal-Dual Convex Optimization Framework NIPS 2015
Preconditioned Spectral Descent for Deep Learning NIPS 2015
Time--Data Tradeoffs by Aggressive Smoothing NIPS 2014
Constrained convex minimization via model-based excessive gap NIPS 2014
High-Dimensional Gaussian Process Bandits NIPS 2013
Sparse projections onto the simplex ICML 2013
A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions ICML 2013
Active Learning of Multi-Index Function Models NIPS 2012
Learning with Compressible Priors NIPS 2009
Sparse Signal Recovery Using Markov Random Fields NIPS 2008