dataset-review/dataset-review huggingface dataset:
Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery
ICML 2025

Guarantees of a Preconditioned Subgradient Algorithm for
Overparameterized Asymmetric Low-rank Matrix Recovery
Paris Giampouras 1 HanQin Cai 2 Ren´e Vidal 3
Abstract
In this paper, we focus on a matrix factorization-
based approach to recover low-rank asymmet-
ric matrices from corrupted measurements. We
propose an Overparameterized Preconditioned
Subgradient Algorithm (OPSA) and provide, for
the first time in the literature, linear convergence
rates independent of the rank of the sought asym-
metric matrix in the presence of gross corrup-
tions. Our work goes beyond existing results in
preconditioned-type approaches addressing their
current limitation, i.e., the lack of convergence
guarantees in the case of asymmetric matrices
of unknown rank. By applying our approach to
(robust) matrix sensing, we highlight its merits
when the measurement operator satisfies a mixed-
norm restricted isometry property. Lastly, we
present extensive numerical experiments that val-
idate our theoretical results and demonstrate the
effectiveness of our approach for different levels
of overparameterization and outlier corruptions.
1. Introduction
Low-rank matrix recovery has been a ubiquitous problem
showing up in numerous applications in the fields of sig-
nal/image processing, machine learning, and data science
(Recht et al., 2010; Chen et al., 2013; Davenport & Romberg,
2016; Cai et al., 2021a; Smith et al., 2024; Wang et al., 2024).
For instance, problems such as matrix sensing (Jain et al.,
2013; Wei et al., 2016; Park et al., 2017; Li et al., 2020),
matrix completion (Candes & Recht, 2008; Nie et al., 2012;
1Department of Computer Science, University of Warwick,
Coventry, England, UK 2Department of Statistics and Data Sci-
ence and Department of Computer Science, University of Central
Florida, Orlando, FL, USA 3Center for Innovation in Data Engi-
neering and Science (IDEAS), Departments of ESE and Radiology,
University of Pennsylvania, Philadelphia, PA, USA. Correspon-
dence to: Re...

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Progressive Tempering Sampler with Diffusion
ICML 2025

Progressive Tempering Sampler with Diffusion
Severi Rissanen * 1 RuiKang OuYang * 2 Jiajun He 2 Wenlin Chen 2 3 Markus Heinonen 1 Arno Solin 1
Jos´e Miguel Hern´andez-Lobato 2
Abstract
Recent research has focused on designing neu-
ral samplers that amortize the process of sam-
pling from unnormalized densities. However, de-
spite significant advancements, they still fall short
of the state-of-the-art MCMC approach, Parallel
Tempering (PT), when it comes to the efficiency
of target evaluations. On the other hand, unlike a
well-trained neural sampler, PT yields only depen-
dent samples and needs to be rerun—at consider-
able computational cost—whenever new samples
are required. To address these weaknesses, we
propose the Progressive Tempering Sampler with
Diffusion (PTSD), which trains diffusion models
sequentially across temperatures, leveraging the
advantages of PT to improve the training of neu-
ral samplers. We also introduce a novel method
to combine high-temperature diffusion models to
generate approximate lower-temperature samples,
which are minimally refined using MCMC and
used to train the next diffusion model. PTSD en-
ables efficient reuse of sample information across
temperature levels while generating well-mixed,
uncorrelated samples. Our method significantly
improves target evaluation efficiency, outperform-
ing diffusion-based neural samplers.
1. Introduction
Sampling from probability distributions is a fundamental
problem in many fields of science, including Bayesian infer-
ence (Gelman et al., 2013; Welling & Teh, 2011), statistical
physics (Von Toussaint, 2011) and molecular simulations
*Equal contribution
1Department of Computer Science,
Aalto University, Finland 2Department of Engineering, Univer-
sity of Cambridge, United Kingdom 3Department of Empir-
ical Inference, Max Planck Institute for Intelligent Systems,
T¨ubingen, Germany. Correspondence to: Severi Rissanen <sev-
eri.rissanen@aalto.fi>, RuiKang OuYang <ro352@cam.ac.uk>,
Jiajun He <jh2383@...

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Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
ICML 2025

Generating Hypotheses of Dynamic Causal Graphs in Neuroscience:
Leveraging Generative Factor Models of Observed Time Series
Zachary C. Brown 1 David Carlson 1 2
Abstract
The field of hypothesis generation promises to re-
duce costs in neuroscience by narrowing the range
of interventional studies needed to study various
phenomena. Existing machine learning methods
can generate scientific hypotheses from complex
datasets, but many approaches assume causal rela-
tionships are static over time, limiting their appli-
cability to systems with dynamic, state-dependent
behavior, such as the brain. While some tech-
niques attempt dynamic causal discovery through
factor models, they often restrict relationships to
linear patterns or impose other simplifying as-
sumptions. We propose a novel method that mod-
els dynamic graphs as a conditionally weighted
superposition of static graphs, where each static
graph can capture nonlinear relationships. This
approach enables the detection of complex, time-
varying interactions between variables beyond lin-
ear limitations. Our method improves f1-scores
of predicted dynamic causal patterns by roughly
22-28% on average over baselines in some of our
experiments, with some improvements reaching
well over 60%. A case study on real brain data
demonstrates our method’s ability to uncover re-
lationships linked to specific behavioral states,
offering valuable insights into neural dynamics.
1. Introduction
Causal discovery methods applied to brain dynamics hold
great promise for designing targeted interventions in var-
ious neurological diseases. A typical approach is to gen-
erate hypotheses about causal relationships between brain
1Department of Electrical and Computer Engineering, Duke
University, Durham, NC 15213, USA 2Departments of Civil and
Environmental Engineering; Biostatistics and Bioinformatics; and
Computer Science, Duke University, Durham, NC 15213, USA.
Correspondence to: Zachary C. Brown <zac.brown@duke.edu>,
David Carlson <dav...

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World Model Implanting for Test-time Adaptation of Embodied Agents
ICML 2025

World Model Implanting for Test-time Adaptation of Embodied Agents
Minjong Yoo 1 Jinwoo Jang 1 Sihyung Yoon 1 Honguk Woo 1
Abstract
In embodied AI, a persistent challenge is enabling
agents to robustly adapt to novel domains with-
out requiring extensive data collection or retrain-
ing. To address this, we present a world model
implanting framework (WorMI) that combines
the reasoning capabilities of large language mod-
els (LLMs) with independently learned, domain-
specific world models through test-time com-
position.
By allowing seamless implantation
and removal of the world models, the embod-
ied agent’s policy achieves and maintains cross-
domain adaptability. In the WorMI framework,
we employ a prototype-based world model re-
trieval approach, utilizing efficient trajectory-
based abstract representation matching, to incor-
porate relevant models into test-time composition.
We also develop a world-wise compound attention
method that not only integrates the knowledge
from the retrieved world models but also aligns
their intermediate representations with the rea-
soning model’s representation within the agent’s
policy. This framework design effectively fuses
domain-specific knowledge from multiple world
models, ensuring robust adaptation to unseen do-
mains. We evaluate our WorMI on the Virtual-
Home and ALFWorld benchmarks, demonstrating
superior zero-shot and few-shot performance com-
pared to several LLM-based approaches across a
range of unseen domains. These results highlight
the framework’s potential for scalable, real-world
deployment in embodied agent scenarios where
adaptability and data efficiency are essential.
1. Introduction
In recent years, policies powered by large language models
(LLMs) have demonstrated remarkable success in embodied
1Department
of
Computer
Science
and
Engineering,
Sungkyunkwan University. Correspondence to: Honguk Woo
<hwoo@skku.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR ...

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Branches: Efficiently Seeking Optimal Sparse Decision Trees via AO*
ICML 2025

Branches: Efficiently Seeking Optimal Sparse Decision Trees via AO*
Ayman Chaouki 1 Jesse Read 1 Albert Bifet 2 3
Abstract
Decision Tree (DT) Learning is a fundamental
problem in Interpretable Machine Learning, yet it
poses a formidable optimisation challenge. Prac-
tical algorithms have recently emerged, primarily
leveraging Dynamic Programming and Branch &
Bound. However, most of these approaches rely
on a Depth-First-Search strategy, which is ineffi-
cient when searching for DTs at high depths and
requires the definition of a maximum depth hyper-
parameter. Best-First-Search was also employed
by other methods to circumvent these issues. The
downside of this strategy is its higher memory
consumption, as such, it has to be designed in a
fully efficient manner that takes full advantage of
the problem’s structure. We formulate the prob-
lem within an AND/OR graph search framework
and we solve it with a novel AO*-type algorithm
called BRANCHES. We prove both optimality and
complexity guarantees for BRANCHES and we
show that it is more efficient than the state of
the art theoretically and on a variety of experi-
ments. Furthermore, BRANCHES supports non-
binary features unlike the other methods, we show
that this property can further induce larger gains
in computational efficiency.
1. Introduction
Black-box models are ill-suited for contexts where decisions
carry substantial ramifications. In healthcare for example,
misdiagnoses can delay crucial treatments and lead to severe
outcomes. Likewise, in the criminal justice system, black-
box models can obscure biases and result in discriminatory
rulings. Such risks highlight the necessity of adopting inter-
pretable models in sensitive domains.
Decision Trees (DTs) are highly interpretable due to their
1LIX, Ecole Polytechnique, IP Paris 2AI Institute, University
of Waikato 3LTCI, T´el´ecom Paris, IP Paris. Correspondence to:
Ayman Chaouki <chaoukiayman2@gmail.com>.
Proceedings of the 42 nd International Conference on Mach...

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Distillation Scaling Laws
ICML 2025

Distillation Scaling Laws
Dan Busbridge 1 Amitis Shidani 2 Floris Weers 1 Jason Ramapuram 1 Etai Littwin 1 Russ Webb 1
Abstract
We propose a distillation scaling law that esti-
mates distilled model performance based on a
compute budget and its allocation between the
student and teacher. Our findings mitigate the
risks associated with large-scale distillation by
enabling compute-optimal allocation for both the
teacher and student to maximize student perfor-
mance. We provide compute-optimal distillation
recipes for two key scenarios: when a teacher
already exists, and when a teacher needs train-
ing. In settings involving many students or an
existing teacher, distillation outperforms super-
vised learning up to a compute level that scales
predictably with student size. Conversely, if only
one student is to be distilled and a teacher also re-
quires training, supervised learning is generally
preferable.
Additionally, our large-scale study
of distillation increases our understanding of the
process and helps inform experimental design.
1. Introduction
The study of scaling laws (Hestness et al., 2017; Rosen-
feld et al., 2020; Kaplan et al., 2020; Hoffmann et al.,
2022) revealed that previously trained Language Models
(LMs) could have been more capable if they had followed
a compute optimal training paradigm, which determines
the model size and the number of training tokens that give
the best performing model under a given compute budget.
Many subsequent works have followed compute optimal
training (Dey et al., 2023; Muennighoff et al., 2023b).
The size of compute optimal models grows with compute
(Hoffmann et al., 2022), which makes them challenging
to use due to the growth in inference costs. In practice,
1Apple 2University of Oxford, UK. Work done during an
internship at Apple.
For a full breakdown of contributions
see Appendix J. Correspondence to:
Dan Busbridge <dbus-
bridge@apple.com>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouve...

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Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models
NeurIPS 2025

Diffusion Adaptive Text Embedding for
Text-to-Image Diffusion Models
Byeonghu Na1
Minsang Park1
Gyuwon Sim1
Donghyeok Shin1
HeeSun Bae1
Mina Kang1
Se Jung Kwon2
Wanmo Kang1
Il-Chul Moon1,3
1KAIST, 2NAVER Cloud, 3summary.ai
{byeonghu.na,pagemu,gkwlaks4886,tlsehdgur0,cat2507,kasong13}@kaist.ac.kr,
sejung.kwon@navercorp.com , {wanmo.kang,icmoon}@kaist.ac.kr
Abstract
Text-to-image diffusion models rely on text embeddings from a pre-trained text
encoder, but these embeddings remain fixed across all diffusion timesteps, limiting
their adaptability to the generative process. We propose Diffusion Adaptive Text
Embedding (DATE), which dynamically updates text embeddings at each diffusion
timestep based on intermediate perturbed data. We formulate an optimization
problem and derive an update rule that refines the text embeddings at each sampling
step to improve alignment and preference between the mean predicted image and
the text. This allows DATE to dynamically adapts the text conditions to the
reverse-diffused images throughout diffusion sampling without requiring additional
model training. Through theoretical analysis and empirical results, we show that
DATE maintains the generative capability of the model while providing superior
text-image alignment over fixed text embeddings across various tasks, including
multi-concept generation and text-guided image editing. Our code is available at
https://github.com/aailab-kaist/DATE.
1
Introduction
Text-to-image generation has recently received significant attention due to its capability to generate
realistic and semantically accurate images from textual prompts. This progress has been largely driven
by diffusion models [18, 53], particularly with large-scale models such as DALL·E [45] and Stable
Diffusion [47]. These models use pre-trained text encoders like CLIP [42] and T5 [43] to encode
prompts into embeddings, providing crucial semantic information to diffusion models. Notably, the
quality and semantic alignment of the gener...

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Training-Free Safe Text Embedding Guidance for Text-to-Image Diffusion Models
NeurIPS 2025

Training-Free Safe Text Embedding Guidance for
Text-to-Image Diffusion Models
Byeonghu Na1
Mina Kang1
Jiseok Kwak1
Minsang Park1
Jiwoo Shin1
SeJoon Jun1
Gayoung Lee2
Jin-Hwa Kim2,3
Il-Chul Moon1,4
1KAIST, 2NAVER AI Lab, 3SNU AIIS, 4summary.ai
{byeonghu.na,kasong13,jskwak,pagemu,natu33,sjmathy,icmoon}@kaist.ac.kr,
{gayoung.lee,j1nhwa.kim}@navercorp.com
Abstract
Text-to-image models have recently made significant advances in generating realis-
tic and semantically coherent images, driven by advanced diffusion models and
large-scale web-crawled datasets. However, these datasets often contain inappropri-
ate or biased content, raising concerns about the generation of harmful outputs when
provided with malicious text prompts. We propose Safe Text embedding Guidance
(STG), a training-free approach to improve the safety of diffusion models by guid-
ing the text embeddings during sampling. STG adjusts the text embeddings based
on a safety function evaluated on the expected final denoised image, allowing the
model to generate safer outputs without additional training. Theoretically, we show
that STG aligns the underlying model distribution with safety constraints, thereby
achieving safer outputs while minimally affecting generation quality. Experiments
on various safety scenarios, including nudity, violence, and artist-style removal,
show that STG consistently outperforms both training-based and training-free base-
lines in removing unsafe content while preserving the core semantic intent of input
prompts. Our code is available at https://github.com/aailab-kaist/STG.
Warning: This paper contains model-generated content that may be disturbing.
1
Introduction
Recent advances in text-to-image models have received considerable attention for their ability to
generate realistic images that semantically align with given text prompts [30, 32, 34, 44]. These
advances have largely been driven by the development of diffusion models [9, 33] and the avail-
ability of large-scale datasets...

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On topological descriptors for graph products
NeurIPS 2025

On topological descriptors for graph products
Mattie Ji
University of Pennsylvania
mji13@sas.upenn.edu
Amauri H. Souza
Federal Institute of Ceará
amauriholanda@ifce.edu.br
Vikas Garg
Aalto University
YaiYai Ltd
vgarg@csail.mit.edu
Abstract
Topological descriptors have been increasingly utilized for capturing multiscale
structural information in relational data. In this work, we consider various filtrations
on the (box) product of graphs and the effect on their outputs on the topological de-
scriptors - the Euler characteristic (EC) and persistent homology (PH). In particular,
we establish a complete characterization of the expressive power of EC on general
color-based filtrations. We also show that the PH descriptors of (virtual) graph prod-
ucts contain strictly more information than the computation on individual graphs,
whereas EC does not. Additionally, we provide algorithms to compute the PH dia-
grams of the product of vertex- and edge-level filtrations on the graph product. We
also substantiate our theoretical analysis with empirical investigations on runtime
analysis, expressivity, and graph classification performance. Overall, this work
paves way for powerful graph persistent descriptors via product filtrations. Code is
available at https://github.com/Aalto-QuML/tda_graph_product.
1
Introduction
Message-passing GNNs are prominent models for graph representation learning [19]. However, they
are bounded in expressivity by the WL hierarchy [33–35, 50] and cannot compute fundamental graph
properties such as cycles or connected components [10, 18]. Topological descriptors (TDs) such
as those based on persistent homology (PH) and the Euler characteristics (EC) can provide such
information and thus are being increasingly employed to augment GNNs, boosting their empirical
performance [7–9, 24, 48, 51].
In this work, we study two prominent types of topological descriptors based on PH [1, 13] and EC
[40, 42, 46] respectively. Both descriptors are defined with respect ...

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Private Evolution Converges
NeurIPS 2025

Private Evolution Converges
Tomas Gonzalez
Carnegie Mellon University
tcgonzal@andrew.cmu.edu
Giulia Fanti
Carnegie Mellon University
gfanti@andrew.cmu.edu
Aaditya Ramdas
Carnegie Mellon University
aramdas@cs.cmu.edu
Abstract
Private Evolution (PE) is a promising training-free method for differentially private
(DP) synthetic data generation. While it achieves strong performance in some
domains (e.g., images and text), its behavior in others (e.g., tabular data) is less
consistent. To date, the only theoretical analysis of the convergence of PE depends
on unrealistic assumptions about both the algorithm’s behavior and the structure
of the sensitive dataset. In this work, we develop a new theoretical framework
to understand PE’s practical behavior and identify sufficient conditions for its
convergence. For d-dimensional sensitive datasets with n data points from a
convex and compact domain, we prove that under the right hyperparameter settings
and given access to the Gaussian variation API proposed in [33], PE produces an
(ε, δ)-DP synthetic dataset with expected 1-Wasserstein distance ˜O(d(nε)−1/d)
from the original; this establishes worst-case convergence of the algorithm as
n →∞. Our analysis extends to general Banach spaces as well. We also connect
PE to the Private Signed Measure Mechanism, a method for DP synthetic data
generation that has thus far not seen much practical adoption. We demonstrate the
practical relevance of our theoretical findings in experiments.
1
Introduction
Many modern machine learning applications rely on user data, making data privacy protection a
central concern. In this regard, differential privacy (DP) [17, 19] has become a de facto standard for
safeguarding sensitive information of individuals. Given a private dataset, many problems—including
regression [40, 12], deep learning [1], and stochastic optimization [6, 2]—can be performed in a DP
manner. While it is possible to design a different DP algorithm for each specific task, a reason...

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