AIS-Bench/AIS-Bench huggingface dataset:
DUET: Dual-Perspective Pseudo Labeling and
Uncertainty-aware Exploration & Exploitation
Training for Source-Free Domain Adaptation
Jae Yun Lee1
JaeHyeon Park2,
Gyoomin Lee2,
Bogyeong Kim3,
Min Hee Cha3,
Hyeok Nam1,
Joo Hyeon Jeon1,
Hyunse Lee2,
Sung In Cho1∗
1 Department of Artificial Intelligence at Sogang University
35 Baekbeom-ro, Mapo-gu, Seoul 04107, Republic of Korea
{stcaft, skagur10, jhbox7, csi2267}@sogang.ac.kr
2,3 Department of Computer Science and Artificial Intelligence at Dongguk University
30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
2{pjh0011, exceleaf, sae4394}@dongguk.edu
3{qhruddl51, rninicha}@dgu.ac.kr
Abstract
Source-free domain adaptation (SFDA) aims to adapt a pre-trained source model
to an unlabeled target domain without requiring labeled source data. In a self-
supervised setting, relying on pseudo labels on target domain samples facilitates
the domain adaptation performance providing strong supervision. However, a crit-
ical problem of this approach is the inherent instability of the pre-trained source
model in the target domain, leading to unreliable pseudo labels for the target do-
main data. To tackle this, we propose a novel Dual-perspective pseudo labeling
strategy that jointly leverages a task-specific perspective and a domain-invariant
perspective, assigning pseudo labels only to target samples on which the target
model’s predictions and CLIP’s predictions agree. To further enhance representa-
tion learning without introducing noisy supervision, we apply consistency training
to uncertain samples. Additionally, we introduce a Tsallis mutual information
(TMI)-based vision optimization strategy guided by an Uncertainty-based adapta-
tion index (UAI), which dynamically modulates entropy sensitivity based on the
model’s adaptation uncertainty. The UAI-based training paradigm enables stable
and adaptive domain alignment by effectively balancing exploration and exploita-
tion processes during the optimization process. Our pr...
Published as a conference paper at ICLR 2025
Agent-Oriented Planning in Multi-Agent Systems
Ao Li1,2,∗Yuexiang Xie3 Songze Li4,5 Fugee Tsung1,2 Bolin Ding3 Yaliang Li3,†
1The Hong Kong University of Science and Technology (Guangzhou)
2The Hong Kong University of Science and Technology 3Alibaba Group 4Southeast University
5Engineering Research Center of Blockchain Application, Supervision and Management
(Southeast University), Ministry of Education
aliat@connect.ust.hk, songzeli@seu.edu.cn, season@ust.hk,
{yuexiang.xyx,bolin.ding,yaliang.li}@alibaba-inc.com
ABSTRACT
Through the collaboration of multiple LLM-empowered agents possessing diverse
expertise and tools, multi-agent systems achieve impressive progress in solving
real-world problems. Given the user queries, the meta-agents, serving as the brain
within multi-agent systems, are required to decompose the queries into multi-
ple sub-tasks that can be allocated to suitable agents capable of solving them,
so-called agent-oriented planning. In this study, we identify three critical de-
sign principles of agent-oriented planning, including solvability, completeness,
and non-redundancy, to ensure that each sub-task can be effectively resolved, re-
sulting in satisfactory responses to user queries. These principles further inspire
us to propose AOP, a novel framework for agent-oriented planning in multi-agent
systems, leveraging a fast task decomposition and allocation process followed by
an effective and efficient evaluation via a reward model. According to the eval-
uation results, the meta-agent is also responsible for promptly making necessary
adjustments to sub-tasks and scheduling. Besides, we integrate a feedback loop
into AOP to further enhance the effectiveness and robustness of such a problem-
solving process. Extensive experiments demonstrate the advancement of AOP
in solving real-world problems compared to both single-agent systems and exist-
ing planning strategies for multi-agent systems. The source code ...
Improved Theoretically-Grounded Evolutionary Algorithms
for Subset Selection with a Linear Cost Constraint
Dan-Xuan Liu 1 2 Chao Qian 1 2
Abstract
The subset selection problem with a monotone
and submodular objective function under a lin-
ear cost constraint has wide applications, such
as maximum coverage, influence maximization,
and feature selection, just to name a few. Various
greedy algorithms have been proposed with good
performance both theoretically and empirically.
Recently, evolutionary algorithms (EAs), inspired
by Darwin’s evolution theory, have emerged
as a prominent methodology, offering both em-
pirical advantages and theoretical guarantees.
Among these, the multi-objective EA, POMC, has
demonstrated the best empirical performance to
date, achieving an approximation guarantee of
(1/2)(1 −1/e). However, there remains a gap
in the approximation bounds of EAs compared
to greedy algorithms, and their full theoretical
potential is yet to be realized. In this paper, we re-
analyze the approximation performance of POMC
theoretically, and derive an improved guarantee of
1/2, which thus provides theoretical justification
for its encouraging empirical performance. Fur-
thermore, we propose a novel multi-objective EA,
EPOL, which not only achieves the best-known
practical approximation guarantee of 0.6174, but
also delivers superior empirical performance in
applications of maximum coverage and influence
maximization. We hope this work can help bet-
ter solving the subset selection problem, but also
enhance our theoretical understanding of EAs.
1. Introduction
The subset selection problem aims to select a subset X
from a given ground set V that maximizes a specific func-
1National Key Laboratory for Novel Software Technology,
Nanjing University, Nanjing 210023, China 2School of Artificial
Intelligence, Nanjing University, Nanjing 210023, China. Corre-
spondence to: Chao Qian <qianc@nju.edu.cn>.
Proceedings of the 42 nd International Conference on Machine
Learning,...
Controlling Large Language Model with Latent Action
Chengxing Jia 1 Ziniu Li 2 Pengyuan Wang 1 Yi-Chen Li 1 Zhenyu Hou 3 Yuxiao Dong 3 Yang Yu 1 4
Abstract
Adapting Large Language Models (LLMs) to
downstream tasks using Reinforcement Learning
(RL) has proven to be an effective approach. How-
ever, LLMs do not inherently define the structure
of an agent for RL training, particularly in terms
of specifying the action space. This paper stud-
ies learning a compact latent action space to en-
hance the controllability and exploration of RL
for LLMs. Inspired by reinforcement learning
from observations, we propose Controlling Large
Language Models with Latent Actions (CoLA),
a framework that integrates a latent action space
into pre-trained LLMs. CoLA employs an in-
verse dynamics model to extract latent actions
conditioned on future tokens, ensuring that the
next token prediction is partially influenced by
these actions. Simultaneously, CoLA fine-tunes
the pre-trained LLM to function as a language
world model, capable of incorporating latent ac-
tions as inputs. Additionally, CoLA trains a pol-
icy model to generate actions within this language
world model. The policy model can be trained
via behavior cloning to mimic a standard lan-
guage model or through RL to maximize task-
specific rewards. In this work, we apply CoLA
to the Llama-3.1-8B model. Our experiments
demonstrate that, compared to RL with token-
level actions, CoLA’s latent actions enable greater
semantic diversity. For enhancing downstream
tasks, we show that CoLA with RL achieves a
score of 42.4 on the math500 benchmark, sur-
passing the baseline score of 38.2, and reaches
68.2 when augmented with a Monte Carlo Tree
Search variant. Furthermore, CoLA with RL con-
sistently improves performance on agent-based
1National Key Laboratory for Novel Software Technol-
ogy, School of Artificial Intelligence, Nanjing University, Nan-
jing, China 2The Chinese University of Hong Kong, Shenzhen,
China 3Tsinghua Universi...
Multi-Agent Imitation by Learning and Sampling
from Factorized Soft Q-Function
Yi-Chen Li1,2, Zhongxiang Ling1,2, Tao Jiang1,2,4, Fuxiang Zhang3, Pengyuan Wang1,2,
Lei Yuan1,2,4, Zongzhang Zhang1,2, Yang Yu1,2,4∗
1 National Key Laboratory for Novel Software Technology, Nanjing University, China,
2 School of Artificial Intelligence, Nanjing University, Nanjing, China,
3 Nanyang Technological University, Singapore,
4 Polixir Technologies, Nanjing, China,
liyc@lamda.nju.edu.cn, lingzx@smail.nju.edu.cn, fuxiang001@e.ntu.edu.sg
{wangpy, yuanl}@lamda.nju.edu.cn, zzzhang@nju.edu.cn, yuy@nju.edu.cn
Abstract
Learning from multi-agent expert demonstrations, known as Multi-Agent Imitation
Learning (MAIL), provides a promising approach to sequential decision-making.
However, existing MAIL methods including Behavior Cloning (BC) and Adver-
sarial Imitation Learning (AIL) face significant challenges: BC suffers from the
compounding error issue, while the very nature of adversarial optimization makes
AIL prone to instability. In this work, we propose Multi-Agent imitation by learn-
ing and sampling from FactorIzed Soft Q-function (MAFIS), a novel method that
addresses these limitations for both online and offline MAIL settings. Built upon
the single-agent IQ-Learn framework, MAFIS introduces the value decomposition
network to factorize the imitation objective at agent level, thus enabling scalable
training for multi-agent systems. Moreover, we observe that the soft Q-function
implicitly defines the optimal policy as an energy-based model, from which we
can sample actions via stochastic gradient Langevin dynamics. This allows us to
estimate the gradient of the factorized optimization objective for continuous control
tasks, avoiding the adversarial optimization between the soft Q-function and the pol-
icy required by prior work. By doing so, we obtain a tractable and non-adversarial
objective for both discrete and continuous multi-agent control. Experiments on
common benchmarks incl...
TabPFN Unleashed: A Scalable and Effective Solution to
Tabular Classification Problems
Si-Yang Liu 1 2 Han-Jia Ye 1 2
Abstract
TabPFN has emerged as a promising in-context
learning model for tabular data, capable of di-
rectly predicting the labels of test samples given
labeled training examples. It has demonstrated
competitive performance, particularly on small-
scale classification tasks. However, despite its
effectiveness, TabPFN still requires further re-
finement in several areas, including aligning with
downstream datasets, handling high-dimensional
features, and scaling to larger datasets. In this
paper, we revisit existing variants of TabPFN
and observe that most approaches focus either
on reducing bias or variance, often neglecting
the need to address the other side, while also
increasing inference overhead. To fill this gap,
we propose BETA (Bagging and Encoder-based
Fine-tuning for TabPFN Adaptation), a novel
and effective method designed to minimize both
bias and variance.
To reduce bias, we intro-
duce a lightweight encoder to better align down-
stream tasks with the pre-trained TabPFN. By in-
creasing the number of encoders in a lightweight
manner, BETA mitigate variance, thereby fur-
ther improving the performance of the pretrained
TabPFN. Additionally, bootstrapped sampling is
employed to further reduce the impact of data
perturbations on the model, all while maintaining
computational efficiency during inference. Our
approach enhances TabPFN’s ability to handle
high-dimensional data and scale to larger datasets.
Experimental results on over 200 benchmark clas-
sification datasets demonstrate that BETA either
outperforms or matches state-of-the-art methods.
1School of Artificial Intelligence, Nanjing University, China
2National Key Laboratory for Novel Software Technology,
Nanjing University, China.
Correspondence to: Han-Jia Ye
<yehj@lamda.nju.edu.cn>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267,...
Understanding the Limits of Deep Tabular Methods with Temporal Shift
Hao-Run Cai 1 2 Han-Jia Ye 1 2
Abstract
Deep tabular models have demonstrated remark-
able success on i.i.d. data, excelling in a variety
of structured data tasks. However, their perfor-
mance often deteriorates under temporal distri-
bution shifts, where trends and periodic patterns
are present in the evolving data distribution over
time. In this paper, we explore the underlying rea-
sons for this failure in capturing temporal depen-
dencies. We begin by investigating the training
protocol, revealing a key issue in how model se-
lection performs. While existing approaches use
temporal ordering for splitting validation set, we
show that even a random split can signiﬁcantly
improve model performance. By minimizing the
time lag between training data and test time, while
reducing the bias in validation, our proposed train-
ing protocol signiﬁcantly improves generalization
across various methods. Furthermore, we analyze
how temporal data affects deep tabular represen-
tations, uncovering that these models often fail
to capture crucial periodic and trend information.
To address this gap, we introduce a plug-and-play
temporal embedding method based on Fourier se-
ries expansion to learn and incorporate temporal
patterns, offering an adaptive approach to han-
dle temporal shifts. Our experiments demonstrate
that this temporal embedding, combined with the
improved training protocol, provides a more ef-
fective and robust framework for learning from
temporal tabular data.
1. Introduction
Tabular data is one of the most prevalent data formats in a
wide range of real-world applications, such as e-commerce
(Nederstigt et al., 2014) and healthcare (Hassan et al., 2020).
1School of Artiﬁcial Intelligence, Nanjing University, China
2National Key Laboratory for Novel Software Technology,
Nanjing University, China.
Correspondence to: Han-Jia Ye
<yehj@lamda.nju.edu.cn>.
Proceedings of the 42 nd International Confere...
Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device
Learning
Le-Trung Nguyen 1 A¨el Qu´elennec 1 Van-Tam Nguyen 1 Enzo Tartaglione 1
Abstract
On-device learning has emerged as a promising
direction for AI development, particularly be-
cause of its potential to reduce latency issues
and mitigate privacy risks associated with device-
server communication, while improving energy
efficiency. Despite these advantages, significant
memory and computational constraints still repre-
sent major challenges for its deployment. Draw-
ing on previous studies on low-rank decompo-
sition methods that address activation memory
bottlenecks in backpropagation, we propose a
novel shortcut approach as an alternative. Our
analysis and experiments demonstrate that our
method can reduce activation memory usage,
even up to 120.09× compared to vanilla training,
while also reducing overall training FLOPs up to
1.86× when evaluated on traditional benchmarks.
The code is available at https://github.com/Le-
TrungNguyen/ICML2025-ASI.git.
1. Introduction
Recent significant advances in deep learning have allowed
artificial intelligence to penetrate deeply into human activi-
ties, enhancing the quality of life for users more than ever
before. Deep learning has found applications in a wide
variety of fields, such as healthcare through tumor image de-
tection (Hu et al., 2018) or finance through quantitative trad-
ing (Ozbayoglu et al., 2020). Most notable are applications
of large language models (LLMs), such as GPT (Radford
et al., 2018) or Gemini (Gemini Team et al., 2023).
In that regard, one of the current prominent research
branches is on-device learning, where models are fine-tuned
directly on edge devices, eliminating the need for commu-
1LTCI,
T´el´ecom
Paris,
Institut
Polytechnique
de
Paris,
France.
Correspondence
to:
Le-Trung
Nguyen
<le-trung.nguyen@telecom-paris.fr>,
Enzo
Tartaglione
<enzo.tartaglione@telecom-paris.fr>.
Proceedings of the 42 nd International Conferen...
Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation
Tianyi Zhang * 1 2 Junda Su * 1 Aditya Desai 3 Oscar Wu 2 Zhaozhuo Xu 4 Anshumali Shrivastava 1 2 5 6
Abstract
Adapting pre-trained large language models
(LLMs) is crucial but challenging due to their
enormous size. Parameter-efficient fine-tuning
(PEFT) techniques typically employ additive
adapters applied to frozen model weights. To fur-
ther reduce memory usage, model weights are
often compressed through quantization. How-
ever, existing PEFT methods often yield subop-
timal model quality because they rely on restric-
tive assumptions, such as low-rank constraints
on adapters to limit the number of trainable pa-
rameters. We find that sketching, a popular data
compression technique, can serve as an efficient
LLM adaptation strategy while avoiding the low-
rank assumption. We introduce SketchTune, a
compressive adaptation strategy that compresses
LLM weights into compact fine-tunable sketches,
integrating compression and adaptation into a
unified framework. This integration eliminates
the need for complex two-path computation in
existing PEFT techniques, enabling faster and
more memory-efficient training and inference.
SketchTune is supported by mathematical insights
into matrix classes that are better approximated
using sketching rather than low-rank methods.
Our extensive evaluations with Llama and Mis-
tral models demonstrate that SketchTune outper-
forms leading PEFT methods across diverse tasks
while using substantially smaller base models and
comparable trainable parameters. As a highlight,
SketchTune outperforms LoRA, DoRA, and S2FT
on commonsense and math benchmarks using 2.6-
3.5× smaller base models and exceeds LoftQ in
accuracy by 14.48% on GSM8K with 7.3× fewer
trainable parameters.
*Equal contribution 1Rice University, Houston, TX 2xMAD.ai
3University of California, Berkeley, Berkeley, CA 4Stevens Insti-
tute of Technology, Hoboken, NJ 5ThirdAI Corp. 6Ken Kennedy
Institute. Correspond...
Rethinking PCA Through Duality
Jan Quan∗
ESAT-STADIUS & Leuven.AI
KU Leuven, Belgium
jan.quan@kuleuven.be
Johan Suykens
ESAT-STADIUS & Leuven.AI
KU Leuven, Belgium
johan.suykens@kuleuven.be
Panagiotis Patrinos
ESAT-STADIUS & Leuven.AI
KU Leuven, Belgium
panos.patrinos@kuleuven.be
Abstract
Motivated by the recently shown connection between self-attention and (kernel)
principal component analysis (PCA), we revisit the fundamentals of PCA. Using the
difference-of-convex (DC) framework, we present several novel formulations and
provide new theoretical insights. In particular, we show the kernelizability and out-
of-sample applicability for a PCA-like family of problems. Moreover, we uncover
that simultaneous iteration, which is connected to the classical QR algorithm, is an
instance of the difference-of-convex algorithm (DCA), offering an optimization
perspective on this longstanding method. Further, we describe new algorithms
for PCA and empirically compare them with state-of-the-art methods. Lastly,
we introduce a kernelizable dual formulation for a robust variant of PCA that
minimizes the l1-deviation of the reconstruction errors.
1
Introduction
Principal component analysis (PCA) is one of the most fundamental dimensionality reduction
techniques and has seen widespread use in various fields, including machine learning [20, 10, 38, 23],
signal processing [4, 24], and computer vision [52, 78, 82], due to its ability to efficiently extract
meaningful features from high-dimensional data. Its nonlinear extension, kernel PCA [51], extends
this method through a feature mapping into a high-dimensional space (possibly infinite-dimensional),
enabling the discovery of nonlinear patterns. Crucially, this is achieved without ever computing the
transformed data explicitly, using the kernel trick [11].
In recent years, (kernel) PCA has regained interest due to its connection to the self-attention mecha-
nism [79], which lies at the heart of the widely successful transformer archite...
Local Manifold Approximation and Projection
for Manifold-Aware Diffusion Planning
Kyowoon Lee 1 Jaesik Choi 1 2
Abstract
Recent advances in diffusion-based generative
modeling have demonstrated significant promise
in tackling long-horizon, sparse-reward tasks
by leveraging offline datasets. While these ap-
proaches have achieved promising results, their
reliability remains inconsistent due to the inherent
stochastic risk of producing infeasible trajectories,
limiting their applicability in safety-critical appli-
cations. We identify that the primary cause of
these failures is inaccurate guidance during the
sampling procedure, and demonstrate the exis-
tence of manifold deviation by deriving a lower
bound on the guidance gap. To address this chal-
lenge, we propose Local Manifold Approximation
and Projection (LoMAP), a training-free method
that projects the guided sample onto a low-rank
subspace approximated from offline datasets, pre-
venting infeasible trajectory generation. We val-
idate our approach on standard offline reinforce-
ment learning benchmarks that involve challeng-
ing long-horizon planning. Furthermore, we show
that, as a standalone module, LoMAP can be in-
corporated into the hierarchical diffusion planner,
providing further performance enhancements.
1. Introduction
Planning over long horizons is crucial for autonomous sys-
tems operating in real-world settings, where rewards are
sparse and actions often entail delayed consequences (Lee
et al., 2023a). When environment dynamics are fully known,
methods such as Model Predictive Control (Tassa et al.,
2012) and Monte Carlo Tree Search (Silver et al., 2016;
2017; Lee et al., 2018) have achieved remarkable success.
In most practical applications, however, the dynamics are
1Korea Advanced Institute of Science and Technology
(KAIST)
2INEEJI. Correspondence to:
Jaesik Choi <jae-
sik.choi@kaist.ac.kr>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. ...
Projection-Manifold Regularized Latent Diffusion for
Robust General Image Fusion
Lei Cao1,
Hao Zhang1,2,*,
Chunyu Li1,
Jiayi Ma1,*
1Electronic Information School, Wuhan University, Wuhan, China
2Suzhou Institute, Wuhan University, Suzhou, China
whu.caolei@whu.edu.cn,
{zhpersonalbox, licy0089, jyma2010}@gmail.com
Abstract
This study proposes PDFuse, a robust, general training-free image fusion frame-
work built on pre-trained latent diffusion models with projection–manifold regu-
larization. By redefining fusion as a diffusion inference process constrained by
multiple source images, PDFuse can adapt to varied image modalities and produce
high-fidelity outputs utilizing the diffusion prior. To ensure both source consistency
and full utilization of generative priors, we develop novel projection–manifold
regularization, which consists of two core mechanisms. On the one hand, the Multi-
source Information Consistency Projection (MICP) establishes a projection system
between diffusion latent representations and source images, solved efficiently via
conjugate gradients to inject multi-source information into the inference. On the
other hand, the Latent Manifold-preservation Guidance (LMG) aligns the latent
distribution of diffusion variables with that of the sources, guiding generation to
respect the model’s manifold prior. By alternating these mechanisms, PDFuse
strikes an optimal balance between fidelity and generative quality, achieving su-
perior fusion performance across diverse tasks. Moreover, PDFuse constructs
a canonical interference operator set. It synergistically incorporates it into the
aforementioned dual mechanisms, effectively leveraging generative priors to ad-
dress various degradation issues during the fusion process without requiring clean
data for supervising training. Extensive experimental evidence substantiates that
PDFuse achieves highly competitive performance across diverse image fusion tasks.
The code is publicly available at https://github.com/...