dataset-review/dataset-review huggingface dataset:
Privacy-Aware Lifelong Learning
ICLR 2025

Published as a conference paper at ICLR 2025
PRIVACY-AWARE LIFELONG LEARNING
Ozan ¨Ozdenizci1, Elmar Rueckert1, Robert Legenstein2
1 Chair of Cyber-Physical-Systems, Montanuniversit¨at Leoben, Austria
2 Institute of Machine Learning and Neural Computation, Graz University of Technology, Austria
{ozan.oezdenizci,elmar.rueckert}@unileoben.ac.at
robert.legenstein@tugraz.at
ABSTRACT
Lifelong learning algorithms enable models to incrementally acquire new knowl-
edge without forgetting previously learned information. Contrarily, the field of
machine unlearning focuses on explicitly forgetting certain previous knowledge
from pretrained models when requested, in order to comply with data privacy reg-
ulations on the right-to-be-forgotten. Enabling efficient lifelong learning with the
capability to selectively unlearn sensitive information from models presents a crit-
ical and largely unaddressed challenge with contradicting objectives. We address
this problem from the perspective of simultaneously preventing catastrophic for-
getting and allowing forward knowledge transfer during task-incremental learn-
ing, while ensuring exact task unlearning and minimizing memory requirements,
based on a single neural network model to be adapted. Our proposed solution,
privacy-aware lifelong learning (PALL), involves optimization of task-specific
sparse subnetworks with parameter sharing within a single architecture. We ad-
ditionally utilize an episodic memory rehearsal mechanism to facilitate exact un-
learning without performance degradations. We empirically demonstrate the scal-
ability of PALL across various architectures in image classification, and provide
a state-of-the-art solution that uniquely integrates lifelong learning and privacy-
aware unlearning mechanisms for responsible AI applications.
1
INTRODUCTION
Lifelong learning algorithms enhance the ability of machine learning models to incrementally ac-
quire new skills or integrate new knowledge over time from sequentially o...
Shifting Time: Time-series Forecasting with Khatri-Rao Neural Operators
ICML 2025

Shifting Time: Time-series Forecasting with Khatri-Rao Neural Operators
Srinath Dama 1 Kevin Course 1 Prasanth B. Nair 1
Abstract
We present an operator-theoretic framework for
temporal and spatio-temporal forecasting based
on learning a continuous time-shift operator. Our
operator learning paradigm offers a continuous
relaxation of the discrete lag factor used in tradi-
tional autoregressive models, enabling the history
of a system up to a given time to be mapped to its
future values. We parametrize the time-shift oper-
ator using Khatri-Rao neural operators (KRNOs),
a novel architecture based on non-stationary inte-
gral transforms with nearly linear computational
scaling. Our framework naturally handles irregu-
larly sampled observations and enables forecast-
ing at super-resolution in both space and time.
Extensive numerical studies across diverse tempo-
ral and spatio-temporal benchmarks demonstrate
that our approach achieves state-of-the-art or com-
petitive performance with leading methods.
1. Introduction
Time series forecasting is a fundamental problem in ma-
chine learning and statistics with applications to a broad
spectrum of problems encountered in all branches of sci-
ence, engineering, and finance (Roberts et al., 2013; Milani
et al., 2017; Siami-Namini & Namin, 2018). At a high-level,
time-series problems are concerned with forecasting the fu-
ture values of quantities of interest given past observations
of the same or correlated quantities.
The majority of methods for time-series forecasting largely
fall into the categories of autoregressive moving average
models and their variants (Box & Jenkins, 1976; Girard,
2004), and deep autoregressive models with memory (El-
man, 1990; Hochreiter & Schmidhuber, 1997; Salinas et al.,
2020). With the tremendous success of transformer-based
1Institute
for
Aerospace
Studies,
University
of
Toronto,
ON,
Canada.
Correspondence
to:
Srinath
Dama
<srinath.dama@mail.utoronto.ca>,
Kevin
Course
<kevin.course@mail.utoronto...
CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation
ICML 2025

CACTI: Leveraging Copy Masking and Contextual Information to Improve
Tabular Data Imputation
Aditya Gorla 1 2 Ryan Wang 3 Zhengtong Liu 3 Ulzee An 1 3 Sriram Sankararaman 1 3 4
Abstract
We present CACTI, a masked autoencoding ap-
proach for imputing tabular data that leverages
the structure in missingness patterns and contex-
tual information. Our approach employs a novel
median truncated copy masking training strategy
that encourages the model to learn from empirical
patterns of missingness while incorporating se-
mantic relationships between features – captured
by column names and text descriptions – to better
represent feature dependence. These dual sources
of inductive bias enable CACTI to outperform
state-of-the-art methods – an average R2 gain of
7.8% over the next best method (13.4%, 6.1%,
and 5.3% under missing not at random, at ran-
dom and completely at random, respectively) –
across a diverse range of datasets and missing-
ness conditions. Our results highlight the value
of leveraging dataset-specific contextual informa-
tion and missingness patterns to enhance imputa-
tion performance. Code is publicly available at
github.com/sriramlab/CACTI
1. Introduction
Missingness is a pervasive problem in real-world tabular
datasets with the potential to adversely affect downstream
inferential tasks (Rubin, 1987; Schafer & Graham, 2002).
While many techniques to estimate or impute missing en-
tries have been proposed (see Section 2.1), missing data
imputation remains a challenging problem.
A primary reason underlying this challenge is that missing-
1Department of Computational Medicine, David Geffen School
of Medicine, UCLA, Los Angeles, CA, USA 2Bioinformatics
Interdepartmental Program, UCLA, Los Angeles, CA, USA
3Department of Computer Science, UCLA, Los Angeles, CA,
USA 4Department of Human Genetics, UCLA, Los Angeles, CA,
USA. Correspondence to: Aditya Gorla <adityagorla@ucla.edu>,
Sriram Sankararaman <sriram@cs.ucla.edu>.
Proceedings of the 42 nd International...
KGGen: Extracting Knowledge Graphs from Plain Text with Language Models
NeurIPS 2025

KGGen: Extracting Knowledge Graphs from Plain
Text with Language Models
Belinda Mo∗1, Kyssen Yu∗2, Joshua Kazdan∗1, Joan Cabezas, Proud Mpala1, Lisa Yu2, Chris Cundy3,
Charilaos Kanatsoulis1, Sanmi Koyejo1
1Stanford University
2University of Toronto
3 FAR AI
Abstract
Recent interest in building foundation models for knowledge graphs has highlighted
a fundamental challenge: knowledge graph data is scarce. The best-known knowl-
edge graphs are primarily human-labeled, created by pattern-matching, or extracted
using early NLP techniques. While human-generated knowledge graphs are in
short supply, automatically extracted ones are of questionable quality. We present
KGGen, a novel text-to-knowledge-graph generator that uses language models to
extract high-quality graphs from plain text with a novel entity resolution approach
that clusters related entities, significantly reducing the sparsity problem that plagues
existing extractors. Unlike other KG generators, KGGen clusters and de-duplicates
related entities to reduce sparsity in extracted KGs. Along with KGGen, we release
Measure of Information in Nodes and Edges (MINE), the first benchmark to test an
extractor’s ability to produce a useful KG from plain text. We benchmark our new
tool against leading existing generators such as Microsoft’s GraphRAG; we achieve
comparable retrieval accuracy on the generated graphs and better information re-
tention. Moreover, our graphs exhibit more concise and generalizable entities and
relations. Our code is open-sourced at https://github.com/stair-lab/kg-gen/.
1
Introduction
Knowledge graph (KG) applications and graph-based Retrieval-Augmented Generation (RAG)
systems are increasingly bottlenecked by the scarcity and incompleteness of available KGs. KGs
consist of a set of subject-predicate-object triples, and have become a fundamental data structure
for information retrieval [Schneider, 1973]. Most real-world KGs, including Wikidata [contributors,
2024], DBpedia [Lehmann et al., 20...
Logits are All We Need to Adapt Closed Models
ICML 2025

Logits are All We Need to Adapt Closed Models
Gaurush Hiranandani 1 Haolun Wu* 2 3 Subhojyoti Mukherjee† 4 Sanmi Koyejo 2
Abstract
Many commercial Large Language Models
(LLMs) are often closed-source, limiting devel-
opers to prompt tuning for aligning content gen-
eration with specific applications. While these
models currently do not provide access to token
logits, we argue that if such access were available,
it would enable more powerful adaptation tech-
niques beyond prompt engineering. In this paper,
we propose a token-level probability reweighting
framework that, given access to logits and a small
amount of task-specific data, can effectively steer
black-box LLMs toward application-specific con-
tent generation. Our approach views next-token
prediction through the lens of supervised classi-
fication. We show that aligning black-box LLMs
with task-specific data can be formulated as a la-
bel noise correction problem, leading to Plugin
model – an autoregressive probability reweighting
model that operates solely on logits. We provide
theoretical justification for why reweighting logits
alone is sufficient for task adaptation. Extensive
experiments with multiple datasets, LLMs, and
reweighting models demonstrate the effectiveness
of our method, advocating for broader access to
token logits in closed-source models. We provide
our code at this https URL.
1. Introduction
The rise of Large Language Models (LLMs) has revolution-
ized generative Artificial Intelligence, yet the most capable
models are often closed-source or black-box (Achiam et al.,
2023; Bai et al., 2022a). These models generate text based
on input prompts but keep their internal weights and training
data undisclosed, limiting transparency and customization.
*Work done while visiting Stanford University. †Work done
while PhD Candidate at UW Madison. 1Typeface 2Stanford Univer-
sity 3Mila - Quebec AI Institute 4Adobe Research. Correspondence
to: Gaurush Hiranandani <gaurush@typeface.ai>, Haolun Wu
<haol...
TRoVe: Discovering Error-Inducing Static Feature Biases in Temporal Vision-Language Models
NeurIPS 2025

TROVE: Discovering Error-Inducing Static Feature
Biases in Temporal Vision-Language Models
Maya Varma1
Jean-Benoit Delbrouck1,2
Sophie Ostmeier1
Akshay Chaudhari1
Curtis Langlotz1
1Stanford University
2HOPPR
mayavarma@cs.stanford.edu
Abstract
Vision-language models (VLMs) have made great strides in addressing temporal
understanding tasks, which involve characterizing visual changes across a sequence
of images. However, recent works have suggested that when making predictions,
VLMs may rely on static feature biases, such as background or object features,
rather than dynamic visual changes. Static feature biases are a type of shortcut
and can contribute to systematic prediction errors on downstream tasks; as a
result, identifying and characterizing error-inducing static feature biases is critical
prior to real-world model deployment. Existing approaches for identifying such
systematic failure modes in trained models (i) are typically designed for non-
temporal settings and (ii) are challenging to evaluate in temporal settings due to
the lack of quantitative evaluation frameworks. In this work, we address these
challenges by introducing TROVE, an automated approach for discovering error-
inducing static feature biases learned by temporal VLMs. Given a trained VLM
and an annotated validation dataset associated with a downstream classification
task, TROVE extracts candidate static features from the dataset and scores each
feature by (i) the effect of the feature on classification errors as well as (ii) the
extent to which the VLM relies on the feature when making predictions. In
order to quantitatively evaluate TROVE, we introduce an evaluation framework
consisting of 101 trained temporal VLMs paired with ground-truth annotations for
learned static feature biases. We use this framework to demonstrate that TROVE
can accurately identify error-inducing static feature biases in VLMs, achieving
a 28.6% improvement over the closest baseline. Finally, we apply TROVE to
7 off-th...
Dynamic Similarity Graph Construction with Kernel Density Estimation
ICML 2025

Dynamic Similarity Graph Construction
with Kernel Density Estimation
Steinar Laenen 1 Peter Macgregor 2 He Sun 1
Abstract
In the kernel density estimation (KDE) problem,
we are given a set X of data points in Rd, a kernel
function k : Rd × Rd →R, and a query point
q ∈Rd, and the objective is to quickly output an
estimate of P
x∈X k(q, x). In this paper, we con-
sider KDE in the dynamic setting, and introduce
a data structure that efficiently maintains the esti-
mates for a set of query points as data points are
added to X over time. Based on this, we design
a dynamic data structure that maintains a sparse
approximation of the fully connected similarity
graph on X, and develop a fast dynamic spectral
clustering algorithm. We further evaluate the ef-
fectiveness of our algorithms on both synthetic
and real-world datasets.
1. Introduction
Given a set X
= {x1, . . . , xn} of data points, a set
Q = {q1, . . . , qm} of query points, and a kernel func-
tion k : Rd × Rd →R⩾0, the KDE problem is to quickly
approximate µq ≜P
xi∈X k(q, xi) for every q ∈Q. As a
basic question in computer science, this problem has been
actively studied since the 1990s (Greengard & Strain, 1991)
and has comprehensive applications in machine learning
and statistics (Bakshi et al., 2023; Genovese et al., 2014;
Scholkopf & Smola, 2018; Schubert et al., 2014).
In this paper we first study the KDE problem in the dynamic
setting, where both the sets of data and query points change
over time. The objective is to dynamically update all the
KDE estimates µq for every q ∈Q as data points are added
to X. Building on the framework for static KDE developed
by Charikar et al. (2020), our algorithm processes: (i) in-
sertions and deletions of query points, and (ii) insertions of
1University of Edinburgh, United Kingdom 2University of
St Andrews, United Kingdom.
Correspondence to: He Sun
<h.sun@ed.ac.uk>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Co...
Counterfactual Effect Decomposition in Multi-Agent Sequential Decision Making
ICML 2025

Counterfactual Effect Decomposition in
Multi-Agent Sequential Decision Making
Stelios Triantafyllou 1 Aleksa Sukovic 1 Yasaman Zolfimoselo 1 Goran Radanovic 1
Abstract
We address the challenge of explaining counter-
factual outcomes in multi-agent Markov decision
processes. In particular, we aim to explain the to-
tal counterfactual effect of an agent’s action on the
outcome of a realized scenario through its influ-
ence on the environment dynamics and the agents’
behavior. To achieve this, we introduce a novel
causal explanation formula that decomposes the
counterfactual effect by attributing to each agent
and state variable a score reflecting their respec-
tive contributions to the effect. First, we show that
the total counterfactual effect of an agent’s action
can be decomposed into two components: one
measuring the effect that propagates through all
subsequent agents’ actions and another related
to the effect that propagates through the state
transitions. Building on recent advancements in
causal contribution analysis, we further decom-
pose these two effects as follows. For the former,
we consider agent-specific effects – a causal con-
cept that quantifies the counterfactual effect of an
agent’s action that propagates through a subset
of agents. Based on this notion, we use Shapley
value to attribute the effect to individual agents.
For the latter, we consider the concept of structure-
preserving interventions and attribute the effect
to state variables based on their “intrinsic” contri-
butions. Through extensive experimentation, we
demonstrate the interpretability of our approach
in a Gridworld environment with LLM-assisted
agents and a sepsis management simulator.
1. Introduction
Applying counterfactual reasoning to retrospectively an-
alyze the impact of different actions in decision making
1Max Planck Institute for Software Systems, Germany. Corre-
spondence to: Stelios Triantafyllou <strianta@mpi-sws.org>.
Proceedings of the 42 nd International Conference...
Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting
NeurIPS 2025

Not All Data are Good Labels:
On the Self-supervised Labeling for Time Series
Forecasting
Yuxuan Yang1,2, Dalin Zhang3, Yuxuan Liang4, Hua Lu5, Gang Chen1, 2, Huan Li1,2 B
1The State Key Laboratory of Blockchain and Data Security, Zhejiang University
2Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security
3Space Information Research Institute, Hangzhou Dianzi University
4The Hong Kong University of Science and Technology (Guangzhou)
5Department of Computer Science, Aalborg University, Denmark
{yangyuxuan, cg, lihuan.cs}@zju.edu.cn
zhangdalin@hdu.edu.cn, yuxliang@outlook.com, luhua@cs.aau.dk
Abstract
Time Series Forecasting (TSF) is a crucial task in various domains, yet existing TSF
models rely heavily on high-quality data and insufficiently exploit all available data.
This paper explores a novel self-supervised approach to re-label time series datasets
by inherently constructing candidate datasets. During the optimization of a simple
reconstruction network, intermediates are used as pseudo labels in a self-supervised
paradigm, improving generalization for any predictor. We introduce the Self-
Correction with Adaptive Mask (SCAM), which discards overfitted components
and selectively replaces them with pseudo labels generated from reconstructions.
Additionally, we incorporate Spectral Norm Regularization (SNR) to further sup-
press overfitting from a loss landscape perspective. Our experiments on eleven
real-world datasets demonstrate that SCAM consistently improves the performance
of various backbone models. This work offers a new perspective on constructing
datasets and enhancing the generalization of TSF models through self-supervised
learning. The code is available at https://github.com/SuDIS-ZJU/SCAM.
1
Introduction
Time Series Forecasting (TSF) is a crucial task with extensive applications in energy, finance,
engineering, and many other domains. Recent advances in deep learning have resulted in TSF
methods that outperform traditional metho...
An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination
NeurIPS 2025

An Evidence-Based Post-Hoc Adjustment Framework
for Anomaly Detection Under Data Contamination
Sukanya Patra
Department of Computer Science
University of Mons
Mons, Belgium
sukanya.patra@umons.ac.be
Souhaib Ben Taieb∗
Department of Statistics and Data Science
Mohamed bin Zayed University of Artificial Intelligence
Abu Dhabi, United Arab Emirates
souhaib.bentaieb@mbzuai.ac.ae
Abstract
Unsupervised anomaly detection (AD) methods typically assume clean training
data, yet real-world datasets often contain undetected or mislabeled anomalies,
leading to significant performance degradation. Existing solutions require access
to the training pipelines, data or prior knowledge of the proportions of anomalies
in the data, limiting their real-world applicability. To address this challenge, we
propose EPHAD, a simple yet effective test-time adaptation framework that updates
the outputs of AD models trained on contaminated datasets using evidence gath-
ered at test time. Our approach integrates the prior knowledge captured by the
AD model trained on contaminated datasets with evidence derived from multi-
modal foundation models like Contrastive Language-Image Pre-training (CLIP),
classical AD methods like the Local Outlier Factor or domain-specific knowledge.
We illustrate the intuition behind EPHAD using a synthetic toy example and vali-
date its effectiveness through comprehensive experiments across eight visual AD
datasets, twenty-six tabular AD datasets, and a real-world industrial AD dataset.
Additionally, we conduct an ablation study to analyse hyperparameter influence
and robustness to varying contamination levels, demonstrating the versatility and
robustness of EPHAD across diverse AD models and evidence pairs. To ensure
reproducibility, our code is publicly available2.
1
Introduction
Anomaly detection (AD) is the basis of many critical applications, including cybersecurity (Xiao
et al., 2024; Li et al., 2023a), healthcare (Bijlani et al., 2024; Huang et al., 2024), and i...
Multiaccuracy and Multicalibration via Proxy Groups
ICML 2025

Multiaccuracy and Multicalibration via Proxy Groups
Beepul Bharti 1 2 Mary Versa Clemens-Sewall 3 Paul Yi 4 Jeremias Sulam 1 2 5
Abstract
As the use of predictive machine learning algo-
rithms increases in high-stakes decision-making,
it is imperative that these algorithms are fair
across sensitive groups. However, measuring and
enforcing fairness in real-world applications can
be challenging due to missing or incomplete sensi-
tive group information. Proxy-sensitive attributes
have been proposed as a practical and effective so-
lution in these settings, but only for parity-based
fairness notions. Knowing how to evaluate and
control for fairness with missing sensitive group
data for newer, different, and more flexible frame-
works, such as multiaccuracy and multicalibra-
tion, remains unexplored. In this work, we ad-
dress this gap by demonstrating that in the ab-
sence of sensitive group data, proxy-sensitive at-
tributes can provably be used to derive actionable
upper bounds on the true multiaccuracy and mul-
ticalibration violations, providing insights into a
predictive model’s potential worst-case fairness
violations. Additionally, we show that adjusting
models to satisfy multiaccuracy and multicalibra-
tion across proxy-sensitive attributes can signifi-
cantly mitigate these violations for the true, but
unknown, sensitive groups. Through several ex-
periments on real-world datasets, we illustrate that
approximate multiaccuracy and multicalibration
can be achieved even when sensitive group data
is incomplete or unavailable.
1Department of Biomedical Engineering, Johns Hopkins Uni-
versity, Baltimore, USA 2Mathematical Institute of Data Science,
Johns Hopkins University, Baltimore, USA 3Department of Ap-
plied Mathematics & Statistics, Johns Hopkins University, Bal-
timore, USA 4St. Jude Children’s Research Hospital, Arlington,
USA 5Department of Computer Science, Johns Hopkins Univer-
sity, Baltimore, USA. Correspondence to: Beepul Bharti <bb-
harti1@jhu.edu>, J...