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Baharan Mirzasoleiman

46 accepted papers

2026

Data Selection for Fine-tuning Vision Language Models via Cross Modal Alignment Trajectories

ICML 2026poster

Data-efficient learning aims to eliminate redundancy in large training datasets by train- ing models on smaller subsets of the most informative examples. While data selection has been extensively explored for vision models and large language models (LLMs), it remains underexplored for Large Vision-L…

Cited by 0SourceScholar
2026

Do We Need All the Synthetic Data? Targeted Image Augmentation via Diffusion Models

ICLR 2026poster

Synthetically augmenting training datasets with diffusion models has been an effective strategy for improving generalization of image classifiers. However, existing techniques struggle to ensure the diversity of generation and increase the size of the data by up to 10-30x to improve the in-distribut…

Cited by 0SourcecodeScholar
2026

Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models

ICML 2026poster

Large Language Models (LLMs) are pretrained on massive datasets and later instruction-tuned via supervised fine-tuning (SFT) or reinforcement learning (RL). Best practices emphasize large, diverse pretraining data, whereas post-training operates differently: SFT relies on smaller, high-quality datas…

Cited by 0SourceScholar
2026

Tuning the Implicit Regularizer of Masked Diffusion Language Models: Enhancing Generalization via Insights from $k$-Parity

ICML 2026poster

Masked Diffusion Language Models have recently emerged as a powerful generative paradigm, yet their generalization properties remain understudied compared to their auto-regressive counterparts. In this work, we investigate these properties within the setting of the $k$-parity problem (computing the …

Cited by 0SourceScholar
2026

Which Reasoning Traces Are Worth Generating Further? Data Curation for Training Reasoning Models

ICML 2026poster

Supervised fine-tuning (SFT) on a small high-quality set of long reasoning traces is an effective way to enable strong reasoning abilities for Large Language Models (LLMs). However, curating a high-quality SFT data requires generating a large pool of long Chain of Thoughts (CoTs), and filtering the …

Cited by 0SourceScholar
2025

Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity

ACL 2025finding

Hallucination in large language models (LLMs) can be detected by assessing the uncertainty of model outputs, typically measured using entropy. Semantic entropy (SE) enhances traditional entropy estimation by quantifying uncertainty at the semantic cluster level. However, as modern LLMs generate long…

2025

Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-training of Deep Networks

ICLR 2025poster

Dataset distillation (DD) generates small synthetic datasets that can efficiently train deep networks with a limited amount of memory and compute. Despite the success of DD methods for supervised learning, DD for self-supervised pre-training of deep models has remained unaddressed. Pre-training on u…

2025

Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures

ICLR 2025poster

Training with larger mini-batches improves the convergence rate and can yield superior performance. However, training with large mini-batches becomes prohibitive for Large Language Models (LLMs), due to the large GPU memory requirement. To address this problem, an effective approach is finding small…

Cited by 0SourcePDFScholar
2025

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions

ICML 2025poster

Weak-to-Strong Generalization (W2SG), where a weak model supervises a stronger one, serves as an important analogy for understanding how humans might guide superhuman intelligence in the future. Promising empirical results revealed that a strong model can surpass its weak supervisor. While recent w…

Cited by 0SourcePDFScholar
2025

Synthetic Text Generation for Training Large Language Models via Gradient Matching

ICML 2025poster

Synthetic data has the potential to improve the performance, training efficiency, and privacy of real training examples. Nevertheless, existing approaches for synthetic text generation are mostly heuristics and cannot generate human-readable text without compromising the privacy of real data, or pro…

2024

Better Safe than Sorry: Pre-training CLIP against Targeted Data Poisoning and Backdoor Attacks

ICML 2024poster

Contrastive Language-Image Pre-training (CLIP) on large image-caption datasets has achieved remarkable success in zero-shot classification and enabled transferability to new domains. However, CLIP is extremely more vulnerable to targeted data poisoning and backdoor attacks compared to supervised lea…

2024

Changing the Training Data Distribution to Reduce Simplicity Bias Improves In-distribution Generalization

NeurIPS 2024poster

Can we modify the training data distribution to encourage the underlying optimization method toward finding solutions with superior generalization performance on in-distribution data? In this work, we approach this question for the first time by comparing the inductive bias of gradient descent (GD)…

Cited by 1SourcePDFScholar
2024

Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality

ICLR 2024poster

Dataset distillation aims to minimize the time and memory needed for training deep networks on large datasets, by creating a small set of synthetic images that has a similar generalization performance to that of the full dataset. However, current dataset distillation techniques fall short, showing a…

2024

Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity

AISTATS 2024poster

Contrastive Language-Image Pre-training (CLIP) on large-scale image-caption datasets learns representations that can achieve remarkable zero-shot generalization. However, such models require a massive amount of pre-training data. Improving the quality of the pre-training data has been shown to be mu…

2024

Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings

ICML 2024poster

Pretrained machine learning models need to be adapted to distribution shifts when deployed in new target environments. When obtaining labeled data from the target distribution is expensive, few-shot adaptation with only a few examples from the target distribution becomes essential. In this work, we…

Cited by 1SourcePDFScholar
2024

Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias

AISTATS 2024poster

Neural networks trained with (stochastic) gradient descent have an inductive bias towards learning simpler solutions. This makes them highly prone to learning spurious correlations in the training data, that may not hold at test time. In this work, we provide the first theoretical analysis of the ef…

Cited by 29SourcePDFScholar
2024

Investigating the Benefits of Projection Head for Representation Learning

ICLR 2024poster

An effective technique for obtaining high-quality representations is adding a projection head on top of the encoder during training, then discarding it and using the pre-projection representations. Despite its proven practical effectiveness, the reason behind the success of this technique is poorly…

Cited by 9SourcePDFScholar
2024

Investigating the Impact of Model Width and Density on Generalization in Presence of Label Noise

UAI 2024poster

Increasing the size of overparameterized neural networks has been a key in achieving state-of-the-art performance. This is captured by the double descent phenomenon, where the test loss follows a decreasing-increasing-decreasing pattern (or sometimes monotonically decreasing) as model width increase…

Cited by 3SourcePDFScholar
2024

NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction

ICML 2024poster

We present NeWRF, a novel deep-learning-based framework for predicting wireless channels. Wireless channel prediction is a long-standing problem in the wireless community and is a key technology for improving the coverage of wireless network deployments. Today, a wireless deployment is evaluated by…

Cited by 7SourcePDFScholar
2024

SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models

NeurIPS 2024poster

Despite the effectiveness of data selection for pretraining and instruction fine-tuning large language models (LLMs), improving data efficiency in supervised fine-tuning (SFT) for specialized domains poses significant challenges due to the complexity of fine-tuning data. To bridge this gap, we intro…

2024

Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift

ICLR 2024poster

Recently, multimodal contrastive learning (MMCL) approaches, such as CLIP, have achieved a remarkable success in learning representations that are robust against distribution shift and generalize to new domains. Despite the empirical success, the mechanism behind learning such generalizable represen…

Cited by 9SourcePDFScholar
2023

Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least

ICML 2023poster

Self-supervised learning (SSL) learns high-quality representations from large pools of unlabeled training data. As datasets grow larger, it becomes crucial to identify the examples that contribute the most to learning such representations. This enables efficient SSL by reducing the volume of data re…

Cited by 24SourcePDFScholar
2023

High Probability Bounds for Stochastic Continuous Submodular Maximization

AISTATS 2023poster

We consider maximization of stochastic monotone continuous submodular functions (CSF) with a diminishing return property. Existing algorithms only guarantee the performance in expectation, and do not bound the probability of getting a bad solution. This implies that for a particular run of the algor…

Cited by 0SourcePDFScholar
2023

Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning

ICML 2023poster

Spurious correlations that degrade model generalization or lead the model to be right for the wrong reasons are one of the main robustness concerns for real-world deployments. However, mitigating these correlations during pre-training for large-scale models can be costly and impractical, particularl…

Cited by 45SourcePDFScholar
2023

Robust Contrastive Language-Image Pretraining against Data Poisoning and Backdoor Attacks

NeurIPS 2023poster

Contrastive vision-language representation learning has achieved state-of-the-art performance for zero-shot classification, by learning from millions of image-caption pairs crawled from the internet. However, the massive data that powers large multimodal models such as CLIP, makes them extremely vul…

2023

Robust Learning with Progressive Data Expansion Against Spurious Correlation

NeurIPS 2023poster

While deep learning models have shown remarkable performance in various tasks, they are susceptible to learning non-generalizable _spurious features_ rather than the core features that are genuinely correlated to the true label. In this paper, beyond existing analyses of linear models, we theoretica…

2023

Towards Sustainable Learning: Coresets for Data-efficient Deep Learning

ICML 2023poster

To improve the efficiency and sustainability of learning deep models, we propose CREST, the first scalable framework with rigorous theoretical guarantees to identify the most valuable examples for training non-convex models, particularly deep networks. To guarantee convergence to a stationary point…

2023

Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression

ICML 2023oral

Contrastive learning (CL) has emerged as a powerful technique for representation learning, with or without label supervision. However, supervised CL is prone to collapsing representations of subclasses within a class by not capturing all their features, and unsupervised CL may suppress harder class-…

Cited by 25SourcePDFScholar
2022

Adaptive Second Order Coresets for Data-efficient Machine Learning

ICML 2022spotlight

Training machine learning models on massive datasets incurs substantial computational costs. To alleviate such costs, there has been a sustained effort to develop data-efficient training methods that can carefully select subsets of the training examples that generalize on par with the full training…

Cited by 73SourcePDFScholar
2022

Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attack

NeurIPS 2022accept

A powerful category of (invisible) data poisoning attacks modify a subset of training examples by small adversarial perturbations to change the prediction of certain test-time data. Existing defense mechanisms are not desirable to deploy in practice, as they often either drastically harm the general…

Cited by 24SourcePDFScholar
2022

Investigating Why Contrastive Learning Benefits Robustness against Label Noise

ICML 2022spotlight

Self-supervised Contrastive Learning (CL) has been recently shown to be very effective in preventing deep networks from overfitting noisy labels. Despite its empirical success, the theoretical understanding of the effect of contrastive learning on boosting robustness is very limited. In this work, w…

2022

Not All Poisons are Created Equal: Robust Training against Data Poisoning

ICML 2022oral

Data poisoning causes misclassification of test time target examples, by injecting maliciously crafted samples in the training data. Existing defenses are often effective only against a specific type of targeted attack, significantly degrade the generalization performance, or are prohibitive for sta…

2020

Coresets for Data-efficient Training of Machine Learning Models

ICML 2020poster

Incremental gradient (IG) methods, such as stochastic gradient descent and its variants are commonly used for large scale optimization in machine learning. Despite the sustained effort to make IG methods more data-efficient, it remains an open question how to select a training data subset that can t…

2020

Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples

UAI 2020poster

In a number of situations, collecting a function value for every data point may be prohibitively expensive, and random sampling ignores any structure in the underlying data. We introduce a scalable optimization algorithm with no correction steps (in contrast to Frank–Wolfe and its variants), a varia…

Cited by 9SourcePDFScholar
2020

Coresets for Robust Training of Deep Neural Networks against Noisy Labels

NeurIPS 2020poster

Modern neural networks have the capacity to overfit noisy labels frequently found in real-world datasets. Although great progress has been made, existing techniques are very limited in providing theoretical guarantees for the performance of the neural networks trained with noisy labels. To tackle th…

2020

Selection via Proxy: Efficient Data Selection for Deep Learning

ICLR 2020poster

Data selection methods, such as active learning and core-set selection, are useful tools for machine learning on large datasets. However, they can be prohibitively expensive to apply in deep learning because they depend on feature representations that need to be learned. In this work, we show that w…

Cited by 391SourcecodeScholar
2018

Dynamic Network Model from Partial Observations

NeurIPS 2018spotlight

Can evolving networks be inferred and modeled without directly observing their nodes and edges? In many applications, the edges of a dynamic network might not be observed, but one can observe the dynamics of stochastic cascading processes (e.g., information diffusion, virus propagation) occurring ov…

Cited by 14SourcePDFScholar
2017

Deletion-Robust Submodular Maximization: Data Summarization with “the Right to be Forgotten”

ICML 2017poster

How can we summarize a dynamic data stream when elements selected for the summary can be deleted at any time? This is an important challenge in online services, where the users generating the data may decide to exercise their right to restrict the service provider from using (part of) their data due…

Cited by 97SourcePDFScholar
2017

Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains

AISTATS 2017poster

Submodular continuous functions are a category of (generally) non-convex/non-concave functions with a wide spectrum of applications. We characterize these functions and demonstrate that they can be maximized efficiently with approximation guarantees. Specifically, i) We introduce the weak DR proper…

Cited by 182SourcePDFScholar
2016

Fast Constrained Submodular Maximization: Personalized Data Summarization

ICML 2016poster

Can we summarize multi-category data based on user preferences in a scalable manner? Many utility functions used for data summarization satisfy submodularity, a natural diminishing returns property. We cast personalized data summarization as an instance of a general submodular maximization problem s…

Cited by 191SourcePDFScholar
2016

Fast Distributed Submodular Cover: Public-Private Data Summarization

NeurIPS 2016poster

In this paper, we introduce the public-private framework of data summarization motivated by privacy concerns in personalized recommender systems and online social services. Such systems have usually access to massive data generated by a large pool of users. A major fraction of the data is public and…

Cited by 75SourcePDFScholar
2016

Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization

ICML 2016poster

We consider the problem of learning sparse representations of data sets, where the goal is to reduce a data set in manner that optimizes multiple objectives. Motivated by applications of data summarization, we develop a new model which we refer to as the two-stage submodular maximization problem. Th…

Cited by 39SourcePDFScholar
2015

Distributed Submodular Cover: Succinctly Summarizing Massive Data

NeurIPS 2015spotlight

How can one find a subset, ideally as small as possible, that well represents a massive dataset? I.e., its corresponding utility, measured according to a suitable utility function, should be comparable to that of the whole dataset. In this paper, we formalize this challenge as a submodular cover pro…

Cited by 72SourcePDFScholar