← Authors

Hamed Hassani

65 papers · 2015–2025 · 11 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (10) 🌍 Conference Polyglot (11) 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (12) πŸ—ΊοΈ Taxonomy Completionist (23) 🏠 Conference Loyalist (21) πŸ”¬ Deep Specialist (14) πŸ‘‘ Triple Crown 🧬 Topic Evolution πŸ† Keyword Champion 🀝 Dynamic Duo (17) πŸ—ƒοΈ Keyword Collector (68) ⚑ Prolific Year (10) πŸ”₯ Unstoppable (11) ❓ The Questioner πŸ’Ž Century Club (65)

Conferences

NIPS (21) ICML (16) AISTATS (10) ICLR (5) EMNLP (3) L4DC (3) COLT (2) JMLR (2) AACL (1) IJCNLP (1) NAACL (1)

Top co-authors

George J. Pappas (17) Amin Karbasi (14) Aryan Mokhtari (14) Alexander Robey (11) Zebang Shen (9) Edgar Dobriban (8) Alejandro Ribeiro (6) Arman Adibi (5) Behrad Moniri (5) Xinmeng Huang (5)

Keywords

submodular maximization (7) submodular optimization (5) language model (4) adversarial robustness (4) adversarial training (4) regret bound (4) stochastic optimization (4) large language model (4) convex optimization (4) federated learning (4) wasserstein distance (3) distributed optimization (3) discrete optimization (3) model evaluation (3) gradient descent (3) domain generalization (3) uncertainty quantification (3) representation learning (3) continuous optimization (3) communication efficiency (3)

Papers

Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents ICML 2025
Adversarial Reasoning at Jailbreaking Time ICML 2025
Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing AACL 2025
Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding ICLR 2025
Watermark Smoothing Attacks against Language Models EMNLP 2025
Adaptively profiling models with task elicitation EMNLP 2025
Evaluating the Performance of Large Language Models via Debates NAACL 2025
On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning ICML 2025
Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing IJCNLP 2025
Asymptotics of Linear Regression with Linearly Dependent Data L4DC 2025
Adversarial Training Should Be Cast as a Non-Zero-Sum Game ICLR 2024
Conformal Prediction with Learned Features ICML 2024
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks ICML 2024
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models NIPS 2024
One-Shot Safety Alignment for Large Language Models via Optimal Dualization NIPS 2024
Length Optimization in Conformal Prediction NIPS 2024
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks ICML 2024
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling AISTATS 2024
Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth ICML 2024
Uncertainty in Language Models: Assessment through Rank-Calibration EMNLP 2024
Demystifying Disagreement-on-the-Line in High Dimensions ICML 2023
Linear Stochastic Bandits over a Bit-Constrained Channel L4DC 2023
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods ICML 2023
T-Cal: An Optimal Test for the Calibration of Predictive Models JMLR 2023
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning ICLR 2023
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints NIPS 2022
Self-Consistency of the Fokker Planck Equation COLT 2022
Probabilistically Robust Learning: Balancing Average and Worst-case Performance ICML 2022
An Agnostic Approach to Federated Learning with Class Imbalance ICLR 2022
Do deep networks transfer invariances across classes? ICLR 2022
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning NIPS 2022
Probable Domain Generalization via Quantile Risk Minimization NIPS 2022
Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds NIPS 2022
Minimax Optimization: The Case of Convex-Submodular AISTATS 2022
Federated Functional Gradient Boosting AISTATS 2022
Exploiting Shared Representations for Personalized Federated Learning ICML 2021
Model-Based Domain Generalization NIPS 2021
Adversarial Robustness with Semi-Infinite Constrained Learning NIPS 2021
Optimal Algorithms for Submodular Maximization with Distributed Constraints L4DC 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients NIPS 2021
Precise Tradeoffs in Adversarial Training for Linear Regression COLT 2020
Sinkhorn Barycenter via Functional Gradient Descent NIPS 2020
Sinkhorn Natural Gradient for Generative Models NIPS 2020
Submodular Meta-Learning NIPS 2020
Black Box Submodular Maximization: Discrete and Continuous Settings AISTATS 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization AISTATS 2020
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free AISTATS 2020
One Sample Stochastic Frank-Wolfe AISTATS 2020
Quantized Decentralized Stochastic Learning over Directed Graphs ICML 2020
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization JMLR 2020
Hessian Aided Policy Gradient ICML 2019
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback NIPS 2019
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks NIPS 2019
Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match NIPS 2019
Robust and Communication-Efficient Collaborative Learning NIPS 2019
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs ICML 2019
Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap AISTATS 2018
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings ICML 2018
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity ICML 2018
Online Continuous Submodular Maximization AISTATS 2018
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests AISTATS 2017
Stochastic Submodular Maximization: The Case of Coverage Functions NIPS 2017
Gradient Methods for Submodular Maximization NIPS 2017
Fast and Provably Good Seedings for k-Means NIPS 2016
Sampling from Probabilistic Submodular Models NIPS 2015