Pasin Manurangsi
Skip to main content
Explore our many areas of focus
Explore all research areas
Applied AI & sciences
Earth AI
Health AI
Science AI
Sustainability & crisis resilience
Foundational ML & algorithms
Algorithms & theory
Information retrieval
Machine intelligence
Machine perception
Natural language processing
People, systems & quantum AI
Human-computer interaction and visualization
Networking
Quantum AI
Responsible AI
Anti abuse
Software engineering
Software systems
Learn More
Publications
Projects
Building a collaborative ecosystem
Datasets
Access high-quality datasets to accelerate your research.
Tools & services
Explore our latest AI models and products.
Open source
Discover open-source code and collaborate with the community.
Shaping the future together
See all programs
Faculty programs
Participating in the academic research community through meaningful engagement with university faculty.
Student programs
Supporting the next generation of researchers through a wide range of programming.
Locations
Find your place in our global offices and research labs.
Translating discovery into real-world impact
People
Our researchers drive advancements in computer science through both fundamental and applied research.
Teams
Collaborative groups tackling the world's most challenging AI problems.
Research
Explore our many areas of focus
Explore all research areas
Applied AI & sciences
Earth AI
Health AI
Science AI
Sustainability & crisis resilience
Foundational ML & algorithms
Algorithms & theory
Information retrieval
Machine intelligence
Machine perception
Natural language processing
People, systems & quantum AI
Human-computer interaction and visualization
Networking
Quantum AI
Responsible AI
Anti abuse
Software engineering
Software systems
Learn More
Publications
Projects
Resources
Building a collaborative ecosystem
Datasets
Access high-quality datasets to accelerate your research.
Tools & services
Explore our latest AI models and products.
Open source
Discover open-source code and collaborate with the community.
Conferences & events
Careers
Shaping the future together
See all programs
Faculty programs
Participating in the academic research community through meaningful engagement with university faculty.
Student programs
Supporting the next generation of researchers through a wide range of programming.
Locations
Find your place in our global offices and research labs.
Blog
About
Translating discovery into real-world impact
People
Our researchers drive advancements in computer science through both fundamental and applied research.
Teams
Collaborative groups tackling the world's most challenging AI problems.
Google
Research
Google AI
Learn about all our AI
Google DeepMind
Explore the frontier of AI
Google Labs
Try our AI experiments
Research
Resources
Conferences & events
Careers
Blog
About
Search
Home
People
Pasin Manurangsi
Research Areas
Algorithms and theory
Machine intelligence
Anti abuse
Authored Publications
See Filters
results
Filter by:
Clear
Publications
Google
102
Other
0
Years
2026
5
2025
16
2024
14
2023
22
2022
16
2021
19
2020
10
Research Areas
Algorithms and Theory
47
Economics and Electronic Commerce
1
Health & Bioscience
1
Human-Computer Interaction and Visualization
1
Machine Intelligence
19
Natural Language Processing
1
Responsible AI
3
Security, Privacy and Abuse Prevention
16
Teams
Sort By
Title
Title, descending
Year
Year, descending
chip template
Remove
Fair Allocation of Indivisible Goods with Variable Groups
Pasin Manurangsi
Paul Golz
Warut Suksompong
Ayumi Igarashi
AAAI (2026)
Preview
Preview abstract
We study the fair allocation of indivisible goods with variable groups. In this model, the goal is to partition the agents into groups of given sizes and allocate the goods to the groups in a fair manner. We show that for any number of groups and corresponding sizes, there always exists an envy-free up to one good (EF1) outcome, thereby generalizing an important result from the individual setting. Our result holds for arbitrary monotonic utilities and comes with an efficient algorithm. We also prove that the EF1 existence can be guaranteed even when the goods lie on a path and each group must receive a connected bundle. In addition, we consider a probabilistic model where the utilities are additive and drawn randomly from a distribution. We show that if there are n agents and the number of goods m is divisible by the number of groups k, then an envy-free outcome exists with high probability if m = ω(log n), and this bound is tight. On the other hand, if m is not divisible by k, then an envy-free outcome is unlikely to exist as long as m = o(√n).
View details
Tight Lower Bound for Multicolor Discrepancy
Pasin Manurangsi
Raghu Meka
SOSA (2026)
Preview
Preview abstract
We prove the following asymptotically tight lower bound for k-color discrepancy: For any k ≥ 2, there exists a hypergraph with n vertices such that its k-color discrepancy is at least Ω(√n). This improves on the previously known lower bound of Ω(√n/ log k) due to Caragiannis et al. [CLS25]. As an application, we show that our result implies improved lower bounds for group fair division.
View details
On the PTAS Complexity of Multidimensional Knapsack
Ilan Doron-Arad
Ariel Kulik
Pasin Manurangsi
ITCS (2026)
Preview
Preview abstract
We study the d-dimensional knapsack problem. We are given a set of items, each with a d-dimensional cost vector and a profit, along with a d-dimensional budget vector. The goal is to select a set of items that do not exceed the budget in all dimensions and maximize the total profit. A polynomial-time approximation scheme (PTAS) with running time n^{Θ(d/{ε})} has long been known for this problem, where {ε} is the error parameter and n is the encoding size. Despite decades of active research, the best running time of a PTAS has remained O(n^{⌈ d/{ε} ⌉ - d}). Unfortunately, existing lower bounds only cover the special case with two dimensions d = 2, and do not answer whether there is a n^{o(d/({ε)})}-time PTAS for larger values of d.
In this work, we show that the running times of the best-known PTAS cannot be improved up to a polylogarithmic factor assuming the Exponential Time Hypothesis (ETH). Our techniques are based on a robust reduction from 2-CSP, which embeds 2-CSP constraints into a desired number of dimensions. Then, using a recent result of [Bafna Karthik and Minzer, STOC'25], we succeed in exhibiting tight trade-off between d and {ε} for all regimes of the parameters assuming d is sufficiently large. Informally, our result also shows that under ETH, for any function f there is no f(d/({ε)}) ⋅ n^{õ(d/({ε)})}-time (1-{ε})-approximation for d-dimensional knapsack, where n is the number of items and õ hides polylogarithmic factors in d/({ε)}.
View details
Discrepancy Beyond Additive Functions with Applications to Fair Division
Pasin Manurangsi
Raghu Meka
Alexandros Hollender
Warut Suksompong
ITCS (2026)
Preview
Preview abstract
We consider a setting where we have a ground set ℳ together with real-valued set functions f₁, … , f_n, and the goal is to partition ℳ into two sets S₁,S₂ such that |f_i(S₁) - f_i(S₂)| is small for every i. Many results in discrepancy theory can be stated in this form with the functions f_i being additive. In this work, we initiate the study of the unstructured case where f_i is not assumed to be additive. We show that even without the additivity assumption, the upper bound remains at most O(√{n log n}).
Our result has implications on the fair allocation of indivisible goods. In particular, we show that a consensus halving up to O(√{n log n}) goods always exists for n agents with monotone utilities. Previously, only an O(n) bound was known for this setting.
View details
Improved Differentially Private Algorithms for Rank Aggregation
Pasin Manurangsi
Phanu Vajanopath
Quentin Hillebrand
Vorapong Suppakitpaisarn
AAAI (2026)
Preview
Preview abstract
Rank aggregation is a task of combining the rankings of items from multiple users into a single ranking that best represents the users' rankings. Alabi et al. (AAAI'22) presents differentially-private (DP) polynomial-time approximation schemes (PTASes) and 5-approximation algorithms with certain additive errors for the Kemeny rank aggregation problem in both central and local models. In this paper, we present improved DP PTASes with smaller additive error in the central model. Furthermore, we are first to study the footrule rank aggregation problem under DP. We give a near-optimal algorithm for this problem; as a corollary, this leads to 2-approximation algorithms with the same additive error as the 5-approximation algorithms of Alabi et al. for the Kemeny rank aggregation problem in both central and local models.
View details
Asymptotic Analysis of Weighted Fair Division
Pasin Manurangsi
Warut Suksompong
Tomohiko Yokoyama
IJCAI (2025)
Preview
Preview abstract
Several resource allocation settings involve agents with unequal entitlements represented by weights. We analyze weighted fair division from an asymptotic perspective: if m items are divided among n agents whose utilities are independently sampled from a probability distribution, when is it likely that a fair allocation exist? We show that if the ratio between the weights is bounded, a weighted envy-free allocation exists with high probability provided that m = Ω(n log n/ log log n), generalizing a prior unweighted result. For weighted proportionality, we establish a sharp threshold of m = n/(1 − μ) for the transition from non-existence to existence, where μ ∈ (0, 1) denotes the mean of the distribution. In addition, we prove that for two agents, a weighted envy-free (and weighted proportional) allocation is likely to exist if m = ω(√r), where r denotes the ratio between the two weights.
View details
Improved FPT Approximation Scheme and Approximate Kernel for Biclique-Free Max k-Weight SAT: Greedy Strikes Back
Pasin Manurangsi
Theoretical Computer Science, 1028 (2025)
Preview
Preview abstract
In the Max k-Weight SAT (aka Max SAT with Cardinality Constraint) problem, we are given a CNF formula with n variables and m clauses together with a positive integer k. The goal is to find an assignment where at most k variables are set to one that satisfies as many constraints as possible. Recently, Jain et al. (SODA 2023) gave an FPT approximation scheme (FPT-AS) with running time 2^O((dk/ε)^d) * (n + m)^O(1) for Max k-Weight SAT when the incidence graph is K_{d,d}-free. They asked whether a polynomial-size approximate kernel exists. In this work, we answer this question positively by giving an (1 − ε)-approximate kernel with (dk/ε)^O(d) variables. This also implies an improved FPT-AS with running time (dk/ε)^O(dk) * (n+m)^O(1)-time algorithm for the problem. Our approximate kernel is based mainly on a couple of greedy strategies together with a sunflower lemma-style reduction rule.
View details
Private Hyperparameter Tuning with Ex-Post Guarantee
Badih Ghazi
Pritish Kamath
Alexander Knop
Ravi Kumar
Pasin Manurangsi
Chiyuan Zhang
NeurIPS (2025)
Preview
Preview abstract
The conventional approach in differential privacy (DP) literature formulates the privacy-utility tradeoff with a "privacy-first" perspective: for a predetermined level of privacy, a certain utility is achievable. However, practitioners often operate under a "utility-first" paradigm, prioritizing a desired level of utility and then determining the corresponding privacy cost.
Wu et al. [2019] initiated a formal study of this ``utility-first'' perspective by introducing ex-post DP. They demonstrated that by adding correlated Laplace noise and progressively reducing it on demand, a sequence of increasingly accurate estimates of a private parameter can be generated, with the privacy cost attributed only to the least noisy iterate released. This led to a Laplace mechanism variant that achieves a specified utility with minimal privacy loss. However, their work, and similar findings by Whitehouse et al. [2023], are primarily limited to simple mechanisms based on Laplace or Gaussian noise.
In this paper, we significantly generalize these results. In particular, we extend the findings of Wu et al. [2019] and Liu and Talwar [2019] to support any sequence of private estimators, incurring at most a doubling of the original privacy budget. Furthermore, we demonstrate that hyperparameter tuning for these estimators, including the selection of an optimal privacy budget, can be performed without additional privacy cost. Finally, we extend our results to ex-post R'{e}nyi DP, further broadening the applicability of utility-first privacy mechanisms.
View details
Differential Privacy on Trust Graphs
Badih Ghazi
Ravi Kumar
Pasin Manurangsi
Serena Wang
ITCS (2025)
Preview
Preview abstract
We study differential privacy (DP) in a multi-party setting where each party only trusts a (known) subset of the other parties with its data. Specifically, given a trust graph where vertices correspond to parties and neighbors are mutually trusting, we give a DP algorithm for aggregation with a much better privacy-utility trade-off than in the well-studied local model of DP (where each party trusts no other party). We further study a robust variant where each party trusts all but an unknown subset of at most t of its neighbors (where t is a given parameter), and give an algorithm for this setting. We complement our algorithms with lower bounds, and discuss implications of our work to other tasks in private learning and analytics.
View details
Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models
Lynn Chua
Badih Ghazi
Yangsibo Huang
Pritish Kamath
Ravi Kumar
Pasin Manurangsi
Amer Sinha
Chulin Xie
Chiyuan Zhang
COLM (2025)
Preview
Preview abstract
Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, i.e., be crosslingual? This study evaluates state-of-the-art LLMs on inherently crosslingual tasks. We observe that while these models show promising surface-level crosslingual abilities on machine translation and embedding space analyses, they struggle with deeper crosslingual knowledge transfer, revealing a crosslingual knowledge barrier in both general (MMLU benchmark) and domain-specific (Harry Potter quiz and TOFU benchmark) contexts. Since simple inference-time mitigation methods offer only limited improvement, we propose fine-tuning of LLMs on mixed-language data, which effectively reduces these gaps, even when using out-of-domain datasets like WikiText. Our findings suggest the need for explicit optimization to unlock the full crosslingual potential of LLMs. Our code is available at https://github.com/google-research/crosslingual-knowledge-barriers.
View details
1
2
3
…
of 11
of 11 pages
Search on Google Scholar
Join us
We're always looking for more talented, passionate people.
See opportunities
Follow us
Explore our other initiatives
Google AI
Discover how Google AI is committed to enriching knowledge and solving complex challenges
Products
Build
Research
Responsibility
Societal Impact
About
Google Cloud
High-performance infrastructure for cloud computing, data analytics & machine learning
Overview
Solutions
Products
Pricing
Resources
Google DeepMind
Our mission is to build AI responsibly to benefit humanity
Models
Research
Science
About
Google Labs
Explore the future of AI responsibly with Google Labs
About
Experiments
Stay connected
About Google
Google Products
Privacy
Terms
Cookies management controls
×