dataset-review/dataset-review huggingface dataset:
Computing Voting Rules with Improvement Feedback
ICML 2025

Computing Voting Rules with Improvement Feedback
Evi Micha 1 Vasilis Varsamis 1
Abstract
Aggregating preferences under incomplete or con-
strained feedback is a fundamental problem in
social choice and related domains. While prior
work has established strong impossibility results
for pairwise comparisons, this paper extends the
inquiry to improvement feedback, where vot-
ers express incremental adjustments rather than
complete preferences. We provide a complete
characterization of the positional scoring rules
that can be computed given improvement feed-
back. Interestingly, while plurality is learnable un-
der improvement feedback—unlike with pairwise
feedback—strong impossibility results persist for
many other positional scoring rules. Furthermore,
we show that improvement feedback, unlike pair-
wise feedback, does not suffice for the computa-
tion of any Condorcet-consistent rule. We comple-
ment our theoretical findings with experimental
results, providing further insights into the prac-
tical implications of improvement feedback for
preference aggregation.
1. Introduction
Classical social choice theory assumes that voters provide
complete rankings of all candidates, which are then aggre-
gated by a voting rule to select a winner (Brandt et al., 2016).
However, this assumption becomes impractical in many real-
world scenarios involving a large number of candidates, as
voters may be unable or unwilling to rank all of them. For
example, in deliberation platforms like Polis (Small et al.,
2021), users provide pairwise comparisons over a limited
subset of opinions rather than full rankings. Similarly, in Re-
inforcement Learning from Human Feedback (RLHF)—a
methodology widely used to fine-tune large language mod-
els (LLMs)—feedback often takes the form of comparison
1Thomas Lord Department of Computer Science, University
of Southern California, Los Angeles, California, USA. Correspon-
dence to: Evi Micha <evi.micha@usc.edu>, Vasilis Varsamis
<varsamis@usc.edu>.
Proc...
Value-Based Deep RL Scales Predictably
ICML 2025

Value-Based Deep RL Scales Predictably
Oleh Rybkin 1 Michal Nauman 1 2 Preston Fu 1 Charlie Snell 1 Pieter Abbeel 1 Sergey Levine 1 Aviral Kumar 3
Abstract
Abstract: Scaling data and compute is critical
to the success of modern ML. However, scaling
demands predictability: we want methods to not
only perform well with more compute or data, but
also have their performance be predictable from
small-scale runs, without running the large-scale
experiment. In this paper, we show that value-
based off-policy RL methods are predictable de-
spite community lore regarding their pathological
behavior. First, we show that data and compute
requirements to attain a given performance level
lie on a Pareto frontier, controlled by the updates-
to-data (UTD) ratio. By estimating this frontier,
we can predict this data requirement when given
more compute, and this compute requirement
when given more data. Second, we determine
the optimal allocation of a total resource budget
across data and compute for a given performance
and use it to determine hyperparameters that max-
imize performance for a given budget. Third, this
scaling is enabled by first estimating predictable
relationships between hyperparameters, which is
used to manage effects of overfitting and plastic-
ity loss unique to RL. We validate our approach
using three algorithms: SAC, BRO, and PQL on
DeepMind Control, OpenAI gym, and IsaacGym,
when extrapolating to higher levels of data, com-
pute, budget, or performance.
1. Introduction
Many latest advances in various areas of machine learning
have emerged from training big models on large datasets.
In this scaling guided research landscape, successfully ex-
ecuting even one single training run often requires a large
amount of data, computational resources, and wall-clock
time, such as weeks or months (Achiam et al., 2023; Team
et al., 2023; Ramesh et al., 2022; Brooks et al., 2024). To
1UC Berkeley 2University of Warsaw 3CMU. Correspondence
to: Oleh Rybkin <oleh.rybkin@gmail...
A Unified Comparative Study with Generalized Conformity Scores for Multi-Output Conformal Regression
ICML 2025

A Unified Comparative Study with Generalized Conformity Scores for
Multi-Output Conformal Regression
Victor Dheur 1 Matteo Fontana 2 Yorick Estievenart 1 Naomi Desobry 1 Souhaib Ben Taieb 1 3
Abstract
Conformal prediction provides a powerful frame-
work for constructing distribution-free prediction
regions with finite-sample coverage guarantees.
While extensively studied in univariate settings,
its extension to multi-output problems presents
additional challenges, including complex output
dependencies and high computational costs, and
remains relatively underexplored. In this work,
we present a unified comparative study of nine
conformal methods with different multivariate
base models for constructing multivariate predic-
tion regions within the same framework. This
study highlights their key properties while also ex-
ploring the connections between them. Addition-
ally, we introduce two novel classes of conformity
scores for multi-output regression that generalize
their univariate counterparts. These scores ensure
asymptotic conditional coverage while maintain-
ing exact finite-sample marginal coverage. One
class is compatible with any generative model, of-
fering broad applicability, while the other is com-
putationally efficient, leveraging the properties of
invertible generative models. Finally, we conduct
a comprehensive empirical evaluation across 13
tabular datasets, comparing all the multi-output
conformal methods explored in this work. To en-
sure a fair and consistent comparison, all methods
are implemented within a unified code base1.
1Department of Computer Science, University of Mons, Mons,
Belgium 2Department of Computer Science, Royal Holloway, Uni-
versity of London, Egham, United Kingdom 3Department of Statis-
tics and Data Science, Mohamed bin Zayed University of Artificial
Intelligence, Abu Dhabi, United Arab Emirates. Correspondence
to: Victor Dheur <victor.dheur@umons.ac.be>.
Proceedings of the 42 nd International Conference on Machine
Learning,...
Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
ICML 2025

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
Adrien Cort´es * R´emi Rehm Victor Letzelter * 1 2
Abstract
We introduce TimeMCL, a method leveraging the
Multiple Choice Learning (MCL) paradigm to
forecast multiple plausible time series futures.
Our approach employs a neural network with
multiple heads and utilizes the Winner-Takes-All
(WTA) loss to promote diversity among predic-
tions. MCL has recently gained attention due to
its simplicity and ability to address ill-posed and
ambiguous tasks. We propose an adaptation of
this framework for time-series forecasting, pre-
senting it as an efficient method to predict diverse
futures, which we relate to its implicit quanti-
zation objective. We provide insights into our
approach using synthetic data and evaluate it on
real-world time series, demonstrating its promis-
ing performance at a light computational cost.
1. Introduction
Predicting the weather of the upcoming weekend or the
stock prices of next month with perfect accuracy would un-
doubtedly be useful. Unfortunately, time-series forecasting
is a highly ill-posed problem. In many cases, the available
input information is insufficient to reduce our uncertainty
about the estimation of the underlying stochastic process
and the data itself may contain noise. Consequently, the best
a forecaster can do is estimate plausible future trajectories,
ideally along with the probability of each outcome.
Because temporal data are highly structured and typically
come with input–output pairs that require no additional man-
ual annotation, autoregressive neural networks have become
the de facto standard for forecasting high-dimensional time
series from historical data and exogenous covariates (Ran-
gapuram et al., 2018; Salinas et al., 2019; Benidis et al.,
2020). To capture predictive uncertainty, practitioners often
*Equal contribution
1LTCI, T´el´ecom Paris, Institut Polytech-
nique de Paris, France 2Valeo.ai, Paris, France. Correspondence
to: Adrien Co...
Topological Schrödinger Bridge Matching
ICLR 2025

Published as a conference paper at ICLR 2025
TOPOLOGICAL SCHR ¨ODINGER BRIDGE MATCHING
Maosheng Yang
Delft University of Technology
m.yang-2@tudelft.nl
ABSTRACT
Given two boundary distributions, the Schr¨odinger Bridge (SB) problem seeks the
“most likely” random evolution between them with respect to a reference process.
It has revealed rich connections to recent machine learning methods for genera-
tive modeling and distribution matching. While these methods perform well in
Euclidean domains, they are not directly applicable to topological domains such
as graphs and simplicial complexes, which are crucial for data defined over net-
work entities, such as node signals and edge flows. In this work, we propose the
Topological Schr¨odinger Bridge problem (T SBP) for matching signal distribu-
tions on a topological domain, where we set the reference process to follow some
linear tractable topology-aware stochastic dynamics such as topological heat dif-
fusion. For the case of Gaussian boundary distributions, we derive a closed-form
Gaussian topological SB in terms of its time-marginal and stochastic differential.
In the general case, leveraging the well-known result, we show that the optimal
process follows the forward-backward topological dynamics governed by some
unknowns. Building on these results, we develop T SB-based models for match-
ing topological signals by parameterizing the unknowns in the optimal process as
(topological) neural networks and learning them through likelihood training. We
validate the theoretical results and demonstrate the practical applications of T SB-
based models on both synthetic and real-world networks, emphasizing the role of
topology. Additionally, we discuss the connections of T SB-based models to other
emerging models, and outline future directions for topological signal matching.
1
INTRODUCTION
As a fundamental problem in statistics and optimization, matching distributions aims to find a map
that transforms one distribution to anot...
Measuring In-Context Computation Complexity via Hidden State Prediction
ICML 2025

Measuring In-Context Computation Complexity via Hidden State Prediction
Vincent Herrmann 1 R´obert Csord´as 2 J¨urgen Schmidhuber 3 1
Abstract
Detecting when a neural sequence model does “in-
teresting” computation is an open problem. The
next token prediction loss is a poor indicator: Low
loss can stem from trivially predictable sequences
that are uninteresting, while high loss may reflect
unpredictable but also irrelevant information that
can be ignored by the model. We propose a better
metric: measuring the model’s ability to predict
its own future hidden states. We show empirically
that this metric—in contrast to the next token pre-
diction loss—correlates with the intuitive inter-
estingness of the task. To measure predictability,
we introduce the architecture-agnostic “predic-
tion of hidden states” (PHi) layer that serves as
an information bottleneck on the main pathway
of the network (e.g., the residual stream in Trans-
formers). We propose a novel learned predictive
prior that enables us to measure the novel infor-
mation gained in each computation step, which
serves as our metric. We show empirically that
our metric predicts the description length of for-
mal languages learned in-context, the complexity
of mathematical reasoning problems, and the cor-
rectness of self-generated reasoning chains.
1. Introduction
Neural sequence models—especially large language models
(LLMs)—have demonstrated remarkable capabilities, such
as solving complex reasoning tasks (Lewkowycz et al., 2022;
Wei et al., 2022; Zelikman et al., 2022) and performing in-
context learning (Brown et al., 2020; Zhao et al., 2021;
Liu et al., 2022; Kirsch et al., 2022). Recent progress in
mechanistic interpretability has offered glimpses into how
these models process information internally: for instance, by
identifying specific induction heads (Olsson et al., 2022) or
1IDSIA/USI/SUPSI, Lugano, Switzerland 2Stanford University,
Stanford, USA 3KAUST, Thuwal, Saudi-Arabia. Correspondence
to: Vinc...
Feature-aware Modulation for Learning from Temporal Tabular Data
NeurIPS 2025

Feature-aware Modulation for Learning from
Temporal Tabular Data
Hao-Run Cai
Han-Jia Ye
School of Artificial Intelligence, Nanjing University, China
National Key Laboratory for Novel Software Technology, Nanjing University, China
{caihr, yehj}@lamda.nju.edu.cn
Abstract
While tabular machine learning has achieved remarkable success, temporal distribu-
tion shifts pose significant challenges in real-world deployment, as the relationships
between features and labels continuously evolve. Static models assume fixed map-
pings to ensure generalization, whereas adaptive models may overfit to transient
patterns, creating a dilemma between robustness and adaptability. In this paper,
we analyze key factors essential for constructing an effective dynamic mapping for
temporal tabular data. We discover that evolving feature semantics—particularly
objective and subjective meanings—introduce concept drift over time. Crucially,
we identify that feature transformation strategies are able to mitigate discrepancies
in feature representations across temporal stages. Motivated by these insights, we
propose a feature-aware temporal modulation mechanism that conditions feature
representations on temporal context, modulating statistical properties such as scale
and skewness. By aligning feature semantics across time, our approach achieves
a lightweight yet powerful adaptation, effectively balancing generalizability and
adaptability. Benchmark evaluations validate the effectiveness of our method in
handling temporal shifts in tabular data.
1
Introduction
Tabular data, structured in rows and columns, forms the foundation of critical decision-making across
diverse domains such as healthcare [39], finance [42], and e-commerce [33]. Traditional machine
learning models for tabular data—including tree-based methods [5, 7, 22, 36] and modern deep
architectures [2, 10–13, 16–18, 52]—commonly assume independent and identically distributed
(i.i.d.) data [3, 30]. However, in real-world scenarios [54],...
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups
ICML 2025

Laplace Transform Based Low-Complexity Learning
of Continuous Markov Semigroups
Vladimir R. Kostic * 1 2 Karim Lounici * 3 H´el`ene Halconruy * 4 5
Timoth´ee Devergne 1 Pietro Novelli 1 Massimiliano Pontil 1 6
Abstract
Markov processes serve as universal models for
many real-world random processes. This paper
presents a data-driven approach to learning these
models through the spectral decomposition of the
infinitesimal generator (IG) of the Markov semi-
group. Its unbounded nature complicates tradi-
tional methods such as vector-valued regression
and Hilbert-Schmidt operator analysis. Existing
techniques, including physics-informed kernel re-
gression, are computationally expensive and lim-
ited in scope, with no recovery guarantees for
transfer operator methods when the time-lag is
small. We propose a novel method leveraging the
IG’s resolvent, characterized by the Laplace trans-
form of transfer operators. This approach is robust
to time-lag variations, ensuring accurate eigen-
value learning even for small time-lags. Our statis-
tical analysis applies to a broader class of Markov
processes than current methods while reducing
computational complexity from quadratic to linear
in the state dimension. Finally, we demonstrate
our theoretical findings in several experiments.
1. Introduction
Markov semigroups play a critical role in modeling dynam-
ics of complex systems across various fields, including op-
tion pricing in finance (Karatzas & Shreve, 1991), molecular
dynamics (Sch¨utte & Huisinga, 2003) and climate modeling
(Majda et al., 2009), where understanding long-term behav-
ior is essential for accurate forecasting and interpretation.
*Equal contribution
1CSML, Istituto Italiano di Tecnologia,
Genova, Italy 2Faculty of Science, University of Novi Sad, Ser-
bia 3CMAP, ´Ecole Polytechnique, Paris, France 4SAMOVAR,
T´el´ecom Sud-Paris, Institut Polytechnique de Paris, France
5MODAL’X, Universit´e Paris Nanterre, France 6AI Center, Univer-
sity College London, UK. ...
Schwarz–Schur Involution: Lightspeed Differentiable Sparse Linear Solvers
ICML 2025

Schwarz–Schur Involution: Lightspeed Differentiable Sparse Linear Solvers
Yu Wang 1 2 3 S. Mazdak Abulnaga 1 2 Ya¨el Balbastre 1 4 Bruce Fischl 1 2
Abstract
Sparse linear solvers or generalized deconvolution
are fundamental to science and engineering, ap-
plied in partial differential equations (PDEs), sci-
entific computing, computer vision, and beyond.
Indirect solvers possess characteristics that make
them undesirable as stable differentiable modules;
existing direct solvers, though reliable, are too
expensive to be adopted in neural architectures.
We substantially accelerate direct sparse solvers
by up to three orders of magnitude, violating com-
mon assumptions that direct solvers are too slow.
We “condense” a sparse Laplacian matrix into a
dense tensor, a compact data structure that batch-
wise stores the Dirichlet-to-Neumann matrices,
reducing the sparse solving to recursively merg-
ing pairs of dense matrices that are much smaller.
The batched small dense systems are sliced and
inverted in parallel to take advantage of dense
GPU BLAS kernels, highly optimized in the era
of deep learning. Our method is efficient, quali-
fied as a strong zero-shot baseline for AI-based
PDE solving, and a reliable differentiable mod-
ule integrable into machine learning pipelines.
Our code is available at https://github.com/
wangyu9/Schwarz_Schur_Involution.
1. Introduction
In this paper, we propose a conceptually simple approach
that reduces solving certain sparse linear system (A, b) to
“involuting” tensors (α, β), leading to significant improve-
ments. Global linear systems in computer vision and scien-
tific computing, such as the Laplacian or Hessian systems on
image domains, are usually very large yet sparse, as induced
by pixel affinity. For a sparse A, direct solvers find the
exact solution: x = A−1b, for which we present the first
1Athinoula Martinos Center, Massachusetts General Hospital,
Harvard Medical School 2Massachusetts Institute of Technology,
MIT CSAIL, USA 3S...
Wasserstein Flow Matching: Generative Modeling Over Families of Distributions
ICML 2025

Wasserstein Flow Matching: Generative Modeling Over Families of Distributions
Doron Haviv * 1 2 Aram-Alexandre Pooladian * 3 Dana Pe’er 2 4 Brandon Amos 5
Abstract
Generative modeling typically concerns transport-
ing a single source distribution to a target distri-
bution via simple probability flows. However, in
fields like computer graphics and single-cell ge-
nomics, samples themselves can be viewed as dis-
tributions, where standard flow matching ignores
their inherent geometry. We propose Wasserstein
flow matching (WFM), which lifts flow matching
onto families of distributions using the Wasser-
stein geometry. Notably, WFM is the first algo-
rithm capable of generating distributions in high
dimensions, whether represented analytically (as
Gaussians) or empirically (as point-clouds). Our
theoretical analysis establishes that Wasserstein
geodesics constitute proper conditional flows over
the space of distributions, making for a valid FM
objective. Our algorithm leverages optimal trans-
port theory and the attention mechanism, demon-
strating versatility across computational regimes:
exploiting closed-form optimal transport paths for
Gaussian families, while using entropic estimates
on point-clouds for general distributions. WFM
successfully generates both 2D & 3D shapes
and high-dimensional cellular microenvironments
from spatial transcriptomics data. Code is avail-
able at Wasserstein Flow Matching.
1. Introduction
Today’s abundance of data and scalability of training mas-
sive neural networks has made it possible to generate hyper-
realistic images on the basis of training examples (OpenAI,
2022), as well as video and audio clips (Vyas et al., 2023;
Xing et al., 2023), and, of course, text (Bubeck et al., 2023).
All of these are instances of generative modeling: given
*Equal contribution 1Weill Cornell 2Memorial Sloan–Kettering
Cancer Center 3Center for Data Science, New York University
4Howard Hughes Medical Institute 5Meta AI. Correspondence to:
Doron Haviv ...
ALMGuard: Safety Shortcuts and Where to Find Them as Guardrails for Audio–Language Models
NeurIPS 2025

ALMGuard: Safety Shortcuts and Where to Find
Them as Guardrails for Audio–Language Models
Weifei Jin1, Yuxin Cao2, Junjie Su1, Minhui Xue3,4, Jie Hao1∗,
Ke Xu5, Jin Song Dong2, Derui Wang3
1Beijing University of Posts and Telecommunications
2National University of Singapore
3CSIRO’s Data61
4 Responsible AI Research (RAIR) Centre, The University of Adelaide
5Tsinghua University
Abstract
Recent advances in Audio-Language Models (ALMs) have significantly improved
multimodal understanding capabilities. However, the introduction of the audio
modality also brings new and unique vulnerability vectors. Previous studies have
proposed jailbreak attacks that specifically target ALMs, revealing that defenses di-
rectly transferred from traditional audio adversarial attacks or text-based Large Lan-
guage Model (LLM) jailbreaks are largely ineffective against these ALM-specific
threats. To address this issue, we propose ALMGuard, the first defense framework
tailored to ALMs. Based on the assumption that safety-aligned shortcuts naturally
exist in ALMs, we design a method to identify universal Shortcut Activation Pertur-
bations (SAPs) that serve as triggers that activate the safety shortcuts to safeguard
ALMs at inference time. To better sift out effective triggers while preserving the
model’s utility on benign tasks, we further propose Mel-Gradient Sparse Mask
(M-GSM), which restricts perturbations to Mel-frequency bins that are sensitive to
jailbreaks but insensitive to speech understanding. Both theoretical analyses and
empirical results demonstrate the robustness of our method against both seen and
unseen attacks. Overall, ALMGuard reduces the average success rate of advanced
ALM-specific jailbreak attacks to 4.6% across four models, while maintaining com-
parable utility on benign benchmarks, establishing it as the new state of the art. Our
code and data are available at https://github.com/WeifeiJin/ALMGuard.
1
Introduction
Audio-Language Models (ALMs) [18, 52] are revolutio...