Baharan Mirzasoleiman
43 papers · 2013–2025 · 8 conferences · across top CS/AI conferences
Achievements
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πΊοΈ Taxonomy Completionist (22) π§ Keyword Pioneer π Interdisciplinary Bridge π Renaissance Researcher (6) π£ Hot Topic Early Bird
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Interdisciplinary Bridge
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Conference Polyglot
(8)
πΊοΈ
Taxonomy Completionist
(22)
π±
Topic Pioneer
π
Triple Crown
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Keyword Champion
(2)
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Grand Slam
π€
Dynamic Duo
(10)
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The Questioner
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Trend Setter
β‘
Prolific Year
(6)
ποΈ
Keyword Collector
(57)
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Century Club
(43)
Conferences
ICML (16)
NIPS (11)
ICLR (6)
AISTATS (4)
UAI (3)
AAAI (1)
ACL (1)
JMLR (1)
Top co-authors
Research topics
Keywords
submodular maximization
(5)
data summarization
(5)
submodular optimization
(4)
contrastive learning
(4)
data poisoning
(3)
subset selection
(3)
spurious correlation
(3)
approximation guarantee
(3)
simplicity bia
(3)
submodular function
(3)
gradient descent
(3)
coreset selection
(3)
stochastic gradient descent
(3)
distributed algorithm
(3)
distributed optimization
(3)
worst-group accuracy
(3)
inductive bia
(2)
data selection
(2)
non-convex optimization
(2)
adversarial robustness
(2)
Papers
Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity
ACL 2025
Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions
ICML 2025
Synthetic Text Generation for Training Large Language Models via Gradient Matching
ICML 2025
Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures
ICLR 2025
Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-training of Deep Networks
ICLR 2025
Graph Contrastive Learning under Heterophily via Graph Filters
UAI 2024
Changing the Training Data Distribution to Reduce Simplicity Bias Improves In-distribution Generalization
NIPS 2024
SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models
NIPS 2024
Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity
AISTATS 2024
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias
AISTATS 2024
Investigating the Benefits of Projection Head for Representation Learning
ICLR 2024
Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift
ICLR 2024
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality
ICLR 2024
NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction
ICML 2024
Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings
ICML 2024
Better Safe than Sorry: Pre-training CLIP against Targeted Data Poisoning and Backdoor Attacks
ICML 2024
Investigating the Impact of Model Width and Density on Generalization in Presence of Label Noise
UAI 2024
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least
ICML 2023
Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
ICML 2023
High Probability Bounds for Stochastic Continuous Submodular Maximization
AISTATS 2023
Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning
ICML 2023
Towards Sustainable Learning: Coresets for Data-efficient Deep Learning
ICML 2023
Robust Contrastive Language-Image Pretraining against Data Poisoning and Backdoor Attacks
NIPS 2023
Robust Learning with Progressive Data Expansion Against Spurious Correlation
NIPS 2023
Investigating Why Contrastive Learning Benefits Robustness against Label Noise
ICML 2022
Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attack
NIPS 2022
CrossWalk: Fairness-Enhanced Node Representation Learning
AAAI 2022
Not All Poisons are Created Equal: Robust Training against Data Poisoning
ICML 2022
Data-Efficient Augmentation for Training Neural Networks
NIPS 2022
Adaptive Second Order Coresets for Data-efficient Machine Learning
ICML 2022
Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples
UAI 2020
Coresets for Robust Training of Deep Neural Networks against Noisy Labels
NIPS 2020
Coresets for Data-efficient Training of Machine Learning Models
ICML 2020
Selection via Proxy: Efficient Data Selection for Deep Learning
ICLR 2020
Dynamic Network Model from Partial Observations
NIPS 2018
Deletion-Robust Submodular Maximization: Data Summarization with βthe Right to be Forgottenβ
ICML 2017
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains
AISTATS 2017
Fast Distributed Submodular Cover: Public-Private Data Summarization
NIPS 2016
Fast Constrained Submodular Maximization: Personalized Data Summarization
ICML 2016
Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization
ICML 2016
Distributed Submodular Maximization
JMLR 2016
Distributed Submodular Cover: Succinctly Summarizing Massive Data
NIPS 2015
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data
NIPS 2013