Mohammadhossein Bateni — accepted AI-conference papers

2026

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

Dang Nguyen, Nilay Naharas, Neslihan Bulut, MohammadHossein Bateni, Vahab Mirrokni, Baharan Mirzasoleiman

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…

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2026

Networked Information Aggregation for Binary Classification

MohammadHossein Bateni, Zahra Hadizadeh, MohammadTaghi Hajiaghayi, Mahdi JafariRaviz, Shayan Taherijam

ICML 2026poster

We study networked binary classification on a directed acyclic graph (DAG) where each agent observes only a subset of the feature columns of a shared finite dataset. Agents act sequentially along the DAG: each receives prediction columns from its parents (if any), augments its local features with th…

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2026

Resilient Coresets and Consistent Clustering

Ashkan Norouzi-Fard, Silvio Lattanzi, MohammadHossein Bateni, Morteza Monemizadeh

ICML 2026poster

Many machine learning problems are geometric at their core, relying on metric representations of data for tasks such as clustering, prototype selection, nearest-neighbor search, and graph-based learning. Furthermore, data is constantly evolving and it is routinely transformed through dimensionality …

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2025

Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation

Michal Lukasik, Lin Chen, Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Felix X. Yu +4

ICML 2025poster

Bipartite ranking is a fundamental supervised learning problem, with the goal of learning a ranking over instances with maximal area under the ROC curve (AUC) against a single binary target label. However, one may often observe multiple binary target labels, e.g., from distinct human annotators. How…

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2025

DeepCrossAttention: Supercharging Transformer Residual Connections

Mike Heddes, Adel Javanmard, Kyriakos Axiotis, Gang Fu, Mohammadhossein Bateni, Vahab Mirrokni

ICML 2025poster

Transformer networks have achieved remarkable success across diverse domains, leveraging a variety of architectural innovations, including residual connections. However, traditional residual connections, which simply sum the outputs of previous layers, can dilute crucial information. This work intro…

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2025

Replicable Online pricing

Kiarash Banihashem, Mohammadhossein Bateni, Hossein Esfandiari, Samira Goudarzi, MohammadTaghi Hajiaghayi

NeurIPS 2025poster

We explore the concept of replicability, which ensures algorithmic consistency despite input data variations, for online pricing problems, specifically prophet inequalities and delegation. Given the crucial role of replicability in enhancing transparency in economic decision-making, we present a rep…

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2025

Synthetic Text Generation for Training Large Language Models via Gradient Matching

Dang Nguyen, Zeman Li, Mohammadhossein Bateni, Vahab Mirrokni, Meisam Razaviyayn, Baharan Mirzasoleiman

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…

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2024

A Scalable Algorithm for Individually Fair k-Means Clustering

MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi

AISTATS 2024poster

We present a scalable algorithm for the individually fair ($p$, $k$)-clustering problem introduced by Jung et al. and Mahabadi et al. Given $n$ points $P$ in a metric space, let $\delta(x)$ for $x\in P$ be the radius of the smallest ball around $x$ containing at least $n / k$ points. A clustering is…

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2024

Efficient Centroid-Linkage Clustering

Mohammadhossein Bateni, Laxman Dhulipala, Willem Fletcher, Kishen N Gowda, D Ellis Hershkowitz, Rajesh Jayaram +1

NeurIPS 2024poster

We give an algorithm for Centroid-Linkage Hierarchical Agglomerative Clustering (HAC), which computes a $c$-approximate clustering in roughly $n^{1+O(1/c^2)}$ time. We obtain our result by combining a new centroid-linkage HAC algorithm with a novel fully dynamic data structure for nearest neighbor s…

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2024

Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval

Yanfei Chen, Jinsung Yoon, Devendra Singh Sachan, Qingze Wang, Vincent Cohen-Addad, Mohammadhossein Bateni +2

EMNLP 2024finding

Recent advances in large language models (LLMs) have enabled autonomous agents with complex reasoning and task-fulfillment capabilities using a wide range of tools. However, effectively identifying the most relevant tools for a given task becomes a key bottleneck as the toolset size grows, hindering…

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2024

SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization

Taisuke Yasuda, Kyriakos Axiotis, Gang Fu, Mohammadhossein Bateni, Vahab Mirrokni

NeurIPS 2024poster

Neural network pruning is a key technique towards engineering large yet scalable, interpretable, and generalizable models. Prior work on the subject has developed largely along two orthogonal directions: (1) differentiable pruning for efficiently and accurately scoring the importance of parameters,…

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2023

Agile Modeling: From Concept to Classifier in Minutes

Otilia Stretcu, Edward Vendrow, Kenji Hata, Krishnamurthy Viswanathan, Vittorio Ferrari, Sasan Tavakkol +12

ICCV 2023poster

The application of computer vision methods to nuanced, subjective concepts is growing. While crowdsourcing has served the vision community well for most objective tasks (such as labeling a "zebra"), it now falters on tasks where there is substantial subjectivity in the concept (such as identifying "…

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2023

Sequential Attention for Feature Selection

Taisuke Yasuda, Mohammadhossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni

ICLR 2023poster

Feature selection is the problem of selecting a subset of features for a machine learning model that maximizes model quality subject to a budget constraint. For neural networks, prior methods, including those based on $\ell_1$ regularization, attention, and other techniques, typically select the ent…

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2023

SubMix: Learning to Mix Graph Sampling Heuristics

Sami Abu-El-Haija, Joshua V. Dillon, Bahare Fatemi, Kyriakos Axiotis, Neslihan Bulut, Johannes Gasteiger +2

UAI 2023poster

Sampling subgraphs for training Graph Neural Networks (GNNs) is receiving much attention from the GNN community. While a variety of methods have been proposed, each method samples the graph according to its own heuristic. However, there has been little work in mixing these heuristics in an end-to-en…

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2021

Extreme k-Center Clustering

MohammadHossein Bateni, Hossein Esfandiari, Manuela Fischer, Vahab Mirrokni

AAAI 2021technical

Metric clustering is a fundamental primitive in machine learning with several applications for mining massive datasets. An important example of metric clustering is the k-center problem. While this problem has been extensively studied in distributed settings, all previous algorithms use Ω(k) space p…

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2021

Streaming Belief Propagation for Community Detection

Yuchen Wu, Jakab Tardos, Mohammadhossein Bateni, André Linhares, Filipe Miguel Goncalves de Almeida, Andrea Montanari +1

NeurIPS 2021poster

The community detection problem requires to cluster the nodes of a network into a small number of well-connected ‘communities’. There has been substantial recent progress in characterizing the fundamental statistical limits of community detection under simple stochastic block models. However, in re…

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2019

Categorical Feature Compression via Submodular Optimization

Mohammadhossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab Mirrokni, Afshin Rostamizadeh

ICML 2019oral

In the era of big data, learning from categorical features with very large vocabularies (e.g., 28 million for the Criteo click prediction dataset) has become a practical challenge for machine learning researchers and practitioners. We design a highly-scalable vocabulary compression algorithm that se…

Cited by 23 Source ↗PDF ↗Scholar ↗

2019

Distributed Weighted Matching via Randomized Composable Coresets

Sepehr Assadi, Mohammadhossein Bateni, Vahab Mirrokni

ICML 2019oral

Maximum weight matching is one of the most fundamental combinatorial optimization problems with a wide range of applications in data mining and bioinformatics. Developing distributed weighted matching algorithms has been challenging due to the sequential nature of efficient algorithms for this probl…

Cited by 7 Source ↗PDF ↗Scholar ↗

2017

Affinity Clustering: Hierarchical Clustering at Scale

Mohammadhossein Bateni, Soheil Behnezhad, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Raimondas Kiveris, Silvio Lattanzi +1

NeurIPS 2017poster

Graph clustering is a fundamental task in many data-mining and machine-learning pipelines. In particular, identifying a good hierarchical structure is at the same time a fundamental and challenging problem for several applications. The amount of data to analyze is increasing at an astonishing rate e…

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