← Authors

Amin Karbasi

99 papers · 2013–2025 · 13 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (28) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (12) 🌍 Conference Polyglot (13) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (14) 🏠 Conference Loyalist (36) 🀝 Dynamic Duo (15) πŸ‘‘ Triple Crown πŸ† Keyword Champion (10) πŸ† Grand Slam 🌱 Topic Pioneer πŸ”¬ Deep Specialist (24) ❓ The Questioner (3) πŸš€ Conference Pioneer ⚑ Prolific Year (12) πŸ”₯ Unstoppable (13) πŸ—ƒοΈ Keyword Collector (91) πŸ’Ž Century Club (99) πŸ“ˆ Trend Setter

Conferences

NIPS (36) ICML (24) AISTATS (14) ICLR (7) COLT (6) JMLR (3) AAAI (2) UAI (2) AACL (1) ACL (1) ALT (1) IJCAI (1) IJCNLP (1)

Top co-authors

Andreas Krause (15) Lin Chen (15) Moran Feldman (14) Hamed Hassani (14) Grigoris Velegkas (14) Ehsan Kazemi (11) Alkis Kalavasis (8) Vahab Mirrokni (7) Mingrui Zhang (6) Baharan Mirzasoleiman (6)

Research topics

Differential Privacy (1) Statistics (1)

Keywords

submodular maximization (22) approximation algorithm (14) submodular optimization (12) data summarization (10) greedy algorithm (9) streaming algorithm (8) submodular function (7) regret bound (7) approximation guarantee (7) combinatorial optimization (5) learning theory (4) cardinality constraint (4) active learning (4) distributed algorithm (4) continuous optimization (4) determinantal point process (4) pac learning (4) distributed optimization (4) learning rate (4) frank-wolfe algorithm (4)

Papers

Large Language Models Encode Semantics and Alignment in Linearly Separable Representations IJCNLP 2025
Procurement Auctions via Approximately Optimal Submodular Optimization ICML 2025
Large Language Models Encode Semantics and Alignment in Linearly Separable Representations AACL 2025
Learning Task Representations from In-Context Learning ACL 2025
Intelligence at the Edge of Chaos ICLR 2025
Adversarial Reasoning at Jailbreaking Time ICML 2025
Submodular Minimax Optimization: Finding Effective Sets AISTATS 2024
Universal Rates for Regression: Separations between Cut-Off and Absolute Loss COLT 2024
Cell2Sentence: Teaching Large Language Models the Language of Biology ICML 2024
TSDS: Data Selection for Task-Specific Model Finetuning NIPS 2024
Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models NIPS 2024
Tree of Attacks: Jailbreaking Black-Box LLMs Automatically NIPS 2024
HyperAttention: Long-context Attention in Near-Linear Time ICLR 2024
Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning ICLR 2024
Universal Rates for Active Learning NIPS 2024
The Impossibility of Parallelizing Boosting ALT 2024
Replicable Learning of Large-Margin Halfspaces ICML 2024
On the Computational Landscape of Replicable Learning NIPS 2024
Replicable Clustering NIPS 2023
Replicability in Reinforcement Learning NIPS 2023
Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD ICLR 2023
Replicable Bandits ICLR 2023
How Do You Want Your Greedy: Simultaneous or Repeated? JMLR 2023
Exact Gradient Computation for Spiking Neural Networks via Forward Propagation AISTATS 2023
KDEformer: Accelerating Transformers via Kernel Density Estimation ICML 2023
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning ICML 2023
Statistical Indistinguishability of Learning Algorithms ICML 2023
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization NIPS 2023
Optimal Learners for Realizable Regression: PAC Learning and Online Learning NIPS 2023
Fast Neural Kernel Embeddings for General Activations NIPS 2022
Self-Consistency of the Fokker Planck Equation COLT 2022
Federated Functional Gradient Boosting AISTATS 2022
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes ICML 2022
Submodular Maximization in Clean Linear Time NIPS 2022
Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes NIPS 2022
Universal Rates for Interactive Learning NIPS 2022
On Optimal Learning Under Targeted Data Poisoning NIPS 2022
Black-Box Generalization: Stability of Zeroth-Order Learning NIPS 2022
Reinforcement Learning with Logarithmic Regret and Policy Switches NIPS 2022
Scalable Sampling for Nonsymmetric Determinantal Point Processes ICLR 2022
Learning Distributionally Robust Models at Scale via Composite Optimization ICLR 2022
Learning and certification under instance-targeted poisoning UAI 2021
Multiple Descent: Design Your Own Generalization Curve NIPS 2021
Parallelizing Thompson Sampling NIPS 2021
Submodular + Concave NIPS 2021
An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks NIPS 2021
Regret Bounds for Batched Bandits AAAI 2021
Meta Learning in the Continuous Time Limit AISTATS 2021
Adaptivity in Adaptive Submodularity COLT 2021
Regularized Submodular Maximization at Scale ICML 2021
The curious case of adversarially robust models: More data can help, double descend, or hurt generalization UAI 2021
More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models ICML 2020
One Sample Stochastic Frank-Wolfe AISTATS 2020
Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition NIPS 2020
Online MAP Inference of Determinantal Point Processes NIPS 2020
Continuous Submodular Maximization: Beyond DR-Submodularity NIPS 2020
Submodular Maximization Through Barrier Functions NIPS 2020
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free AISTATS 2020
Black Box Submodular Maximization: Discrete and Continuous Settings AISTATS 2020
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization JMLR 2020
Streaming Submodular Maximization under a k-Set System Constraint ICML 2020
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback NIPS 2019
Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity ICML 2019
Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications ICML 2019
Projection-Free Bandit Convex Optimization AISTATS 2019
Adaptive Sequence Submodularity NIPS 2019
Eliminating Latent Discrimination: Train Then Mask AAAI 2019
Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match NIPS 2019
Do Less, Get More: Streaming Submodular Maximization with Subsampling NIPS 2018
Online Continuous Submodular Maximization AISTATS 2018
Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap AISTATS 2018
Comparison Based Learning from Weak Oracles AISTATS 2018
Submodularity on Hypergraphs: From Sets to Sequences AISTATS 2018
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy? ICML 2018
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity ICML 2018
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints ICML 2018
Data Summarization at Scale: A Two-Stage Submodular Approach ICML 2018
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings ICML 2018
Gradient Methods for Submodular Maximization NIPS 2017
Probabilistic Submodular Maximization in Sub-Linear Time ICML 2017
Differentially Private Submodular Maximization: Data Summarization in Disguise ICML 2017
Interactive Submodular Bandit NIPS 2017
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly NIPS 2017
Deletion-Robust Submodular Maximization: Data Summarization with β€œthe Right to be Forgotten” ICML 2017
Greed Is Good: Near-Optimal Submodular Maximization via Greedy Optimization COLT 2017
Estimating the Size of a Large Network and its Communities from a Random Sample NIPS 2016
Distributed Submodular Maximization JMLR 2016
Fast Constrained Submodular Maximization: Personalized Data Summarization ICML 2016
Fast Distributed Submodular Cover: Public-Private Data Summarization NIPS 2016
Fast Mixing for Discrete Point Processes COLT 2015
Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning AISTATS 2015
Non-Monotone Adaptive Submodular Maximization IJCAI 2015
Distributed Submodular Cover: Succinctly Summarizing Massive Data NIPS 2015
Sequential Information Maximization: When is Greedy Near-optimal? COLT 2015
Near Optimal Bayesian Active Learning for Decision Making AISTATS 2014
Near-Optimally Teaching the Crowd to Classify ICML 2014
Iterative Learning and Denoising in Convolutional Neural Associative Memories ICML 2013
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data NIPS 2013
Noise-Enhanced Associative Memories NIPS 2013