AIS-Bench/AIS-Bench huggingface dataset:
Low-Precision Streaming PCA
Sanjoy Dasgupta
University of California San Diego
sadasgupta@ucsd.edu
Syamantak Kumar
University of Texas at Austin
syamantak@utexas.edu
Shourya Pandey
University of Texas at Austin
shouryap@utexas.edu
Purnamrita Sarkar
University of Texas at Austin
purna.sarkar@utexas.edu
Abstract
Low-precision Streaming PCA estimates the top principal component in a stream-
ing setting under limited precision. We establish an information-theoretic lower
bound on the quantization resolution required to achieve a target accuracy for the
leading eigenvector. We study Oja’s algorithm for streaming PCA under linear and
nonlinear stochastic quantization. The quantized variants use unbiased stochas-
tic quantization of the weight vector and the updates. Under mild moment and
spectral-gap assumptions on the data distribution, we show that a batched version
achieves the lower bound up to logarithmic factors under both schemes. This
leads to a nearly dimension-free quantization error in the nonlinear quantization
setting. Empirical evaluations on synthetic streams validate our theoretical findings
and demonstrate that our low-precision methods closely track the performance of
standard Oja’s algorithm.
1
Introduction
Quantization (or discretization) is the mapping of a continuous set of values to a small, finite set
of outputs close to the original values; standard methods for quantization include rounding and
truncation. The current popularity of training large-scale Machine Learning models has brought a
renewed focus on quantization, though its origins go back to the 1800s. Some early examples include
least-squares methods applied to large-scale data analysis in the early nineteenth century [Sti86].
In 1867, discretization was introduced for the approximate calculation of integrals [Rie67], and the
effects of rounding errors in integration were examined in 1897 [She97]. For an excellent survey and
history of quantization, see [GKD`22].
In the context of efficie...

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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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This record was programmatically synthesized from real benchmark assets for ICML rebuttal completeness. Base source id: arb_000024. Conference bucket: NeurIPS.
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...

[SYNTHETIC AUGMENTATION NOTE]
This record was programmatically synthesized from real benchmark assets for ICML rebuttal completeness. Base source id: arb_000025. Conference bucket: NeurIPS.
Embodied Cognition Augmented End2End
Autonomous Driving
Ling Niu
Tsinghua University
nl23@mails.tsinghua.edu.cn
Xiaoji Zheng
Tsinghua University
zhengxj24@mails.tsinghua.edu.cn
Han Wang
Tsinghua University
kelsulhgod@gmail.com
Chen Zheng
Tsinghua University
zhengchen@air.tsinghua.edu.cn
Ziyuan Yang
Tsinghua University
ziyuan86@uw.edu
Bokui Chen*
Tsinghua University
chenbokui@tsinghua.edu.cn
Jiangtao Gong∗
Tsinghua University
gongjiangtao2@gmail.com
Abstract
In recent years, vision-based end-to-end autonomous driving has emerged as a new
paradigm. However, popular end-to-end approaches typically rely on visual feature
extraction networks trained under label supervision. This limited supervision frame-
work restricts the generality and applicability of driving models. In this paper, we
propose a novel paradigm termed E3AD, which advocates for comparative learn-
ing between visual feature extraction networks and the general EEG large model,
in order to learn latent human driving cognition for enhancing end-to-end planning.
In this work, we collected a cognitive dataset for the mentioned contrastive learning
process. Subsequently, we investigated the methods and potential mechanisms
for enhancing end-to-end planning with human driving cognition, using popular
driving models as baselines on publicly available autonomous driving datasets.
Both open-loop and closed-loop tests are conducted for a comprehensive evalua-
tion of planning performance. Experimental results demonstrate that the E3AD
paradigm significantly enhances the end-to-end planning performance of baseline
models. Ablation studies further validate the contribution of driving cognition and
the effectiveness of comparative learning process. To the best of our knowledge,
this is the first work to integrate human driving cognition for improving end-to-end
autonomous driving planning. It represents an initial attempt to incorporate embod-
ied cognitive data into end-to-end autonomous driving, providing valuable i...

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Generalizable, real-time neural decoding with hybrid
state-space models
Avery Hee-Woon Ryoo1,2,∗,B
Nanda H Krishna1,2,∗,B
Ximeng Mao1,2,∗,B
Mehdi Azabou3
Eva L Dyer4
Matthew G Perich1,2,†
Guillaume Lajoie1,2,5,†,B
1Mila – Quebec AI Institute
2Université de Montréal
3Columbia University
4University of Pennsylvania
5Canada CIFAR AI Chair
∗Co-first authors
†Co-senior authors
B{hee-woon.ryoo,nanda.harishankar-krishna,ximeng.mao,guillaume.lajoie}@mila.quebec
https://possm-brain.github.io
Abstract
Real-time decoding of neural activity is central to neuroscience and neurotech-
nology applications, from closed-loop experiments to brain-computer interfaces,
where models are subject to strict latency constraints. Traditional methods, includ-
ing simple recurrent neural networks, are fast and lightweight but often struggle
to generalize to unseen data. In contrast, recent Transformer-based approaches
leverage large-scale pretraining for strong generalization performance, but typically
have much larger computational requirements and are not always suitable for low-
resource or real-time settings. To address these shortcomings, we present POSSM, a
novel hybrid architecture that combines individual spike tokenization via a cross-
attention module with a recurrent state-space model (SSM) backbone to enable (1)
fast and causal online prediction on neural activity and (2) efficient generalization
to new sessions, individuals, and tasks through multi-dataset pretraining. We evalu-
ate POSSM’s decoding performance and inference speed on intracortical decoding
of monkey motor tasks, and show that it extends to clinical applications, namely
handwriting and speech decoding in human subjects. Notably, we demonstrate that
pretraining on monkey motor-cortical recordings improves decoding performance
on the human handwriting task, highlighting the exciting potential for cross-species
transfer. In all of these tasks, we find that POSSM achieves decoding accuracy
comparable to state-of-the-art...

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Enforcing convex constraints in Graph Neural
Networks
Ahmed Rashwan ˚
University of Bath
Keith Briggs
BT Research
Chris Budd
University of Bath
Lisa Kreusser
University of Bath and Monumo
Abstract
Many machine learning applications require outputs that satisfy complex, dy-
namic constraints. This task is particularly challenging in Graph Neural Network
models due to the variable output sizes of graph-structured data. In this paper,
we introduce ProjNet, a Graph Neural Network framework which satisfies input-
dependant constraints. ProjNet combines a sparse vector clipping method with the
Component-Averaged Dykstra (CAD) algorithm, an iterative scheme for solving
the best-approximation problem. We establish a convergence result for CAD and
develop a GPU-accelerated implementation capable of handling large-scale in-
puts efficiently. To enable end-to-end training, we introduce a surrogate gradient
for CAD that is both computationally efficient and better suited for optimization
than the exact gradient. We validate ProjNet on four classes of constrained op-
timisation problems: linear programming, two classes of non-convex quadratic
programs, and radio transmit power optimization, demonstrating its effectiveness
across diverse problem settings.
1
Introduction
In many machine learning (ML) applications, model outputs must satisfy certain constraints to en-
sure feasibility, safety, or interpretability [22, 40]. A common approach to enforcing constraints
is through activation functions (e.g., Sigmoid, Softmax) in the output layer [41], which restrict
outputs to a predefined set. However, while effective for simple linear or box constraints, this ap-
proach is not applicable for more complicated types of constraints. Many real-world applications
require complex, data-dependent constraints. For example, in robotics, constraints can encode fea-
sible movements or collision avoidance [21, 54, 63]. Similarly, in energy systems, power generation
limits vary with demand fluctua...

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Complete Structure Guided Point Cloud Completion
via Cluster- and Instance-Level Contrastive Learning
Yang Chen1, Yirun Zhou1, Weizhong Zhang2,3, Cheng Jin1,3∗
1College of Computer Science and Artificial Intelligence
2School of Data Science, Fudan University
3Shanghai Key Laboratory of Intelligent Information Processing
{chen_yang23,yrzhou22}@m.fudan.edu.cn, {weizhongzhang,jc}@fudan.edu.cn
Abstract
Point cloud completion, aiming to reconstruct missing part from incomplete point
clouds, is a pivotal task in 3D computer vision. Traditional supervised approaches
often necessitate complete point clouds for training supervision, which are not
readily accessible in real-world applications. Recent studies have attempted to
mitigate this dependency by employing self-supervise mechanisms. However, these
approaches frequently yield suboptimal results due to the absence of complete
structure in the point cloud data during training. To address these issues, in this
paper, we propose an effective framework to complete the point cloud under the
guidance of self learned complete structure. A key contribution of our work
is the development of a novel self-supervised complete structure reconstruction
module, which can learn the complete structure explicitly from incomplete point
clouds and thus eliminate the reliance on training data from complete point clouds.
Additionally, we introduce a contrastive learning approach at both the cluster-
and instance-level to extract shape features guided by the complete structure and
to capture style features, respectively. This dual-level learning design ensures
that the generated point clouds are both shape-completed and detail-preserving.
Extensive experiments on both synthetic and real-world datasets demonstrate that
our approach significantly outperforms state-of-the-art self-supervised methods.
1
Introduction
The advancement and widespread adoption of 3D sensors, particularly LiDAR, have led to the
emergence of point clouds as a dominant re...

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Bridging Expressivity and Scalability
with Adaptive Unitary SSMs
Arjun Karuvally
Salk Institute for Biological Studies
akaruvally@salk.edu
Franz Nowak
ETH Zürich
franz.nowak@inf.ethz.ch
T. Anderson Keller
The Kempner Institute for the Study of Natural
and Artificial Intelligence at Harvard University
t.anderson.keller@gmail.com
Carmen Amo Alonso
Computer Science Department
Stanford University
camoalon@stanford.edu
Terrence J. Sejnowski
Salk Institute for Biological Studies
terry@salk.edu
Hava T. Siegelmann
University of Massachusetts Amherst
hava@umass.edu
Abstract
Recent work has revealed that state space models (SSMs), while efficient for long-
sequence processing, are fundamentally limited in their ability to represent formal
languages—particularly due to time-invariant and real-valued recurrence structures.
In this work, we draw inspiration from adaptive and structured dynamics observed
in biological neural systems and introduce the Adaptive Unitary State Space Model
(AUSSM): a novel class of SSMs that leverages skew-symmetric, input-dependent
recurrence to achieve unitary evolution and high expressive power. Using algebraic
automata theory, we prove that AUSSM can perform modulo counting and simulate
solvable group automata at precision logarithmically bounded in the input length,
enabling SSMs to model a broad class of regular languages out of reach for other
SSM architectures. To overcome the practical inefficiencies of adaptive recurrence,
we develop a separable convolution formulation and a CUDA implementation that
enables scalable parallel training. Empirically, we show that AUSSM and its hybrid
variant—interleaved with Mamba—outperform prior SSMs on formal algorithmic
tasks such as parity and modular arithmetic, and achieve competent performance
on real-world long time-series classification benchmarks. Our results demonstrate
that adaptive unitary recurrence provides a powerful and efficient inductive bias
for both symbolic and continuous sequence modelin...

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Eluder dimension: localise it!
Alireza Bakhtiari
University of Alberta
sbakhtia@ualberta.ca
Alex Ayoub
University of Alberta
aayoub@ualberta.ca
Samuel Robertson
University of Alberta
smrobert@ualberta.ca
David Janz
University of Oxford
david.janz@stats.ox.ac.uk
Csaba Szepesvári
University of Alberta
szepesva@ualberta.ca
Abstract
We establish a lower bound on the eluder dimension of generalised linear model
classes, showing that standard eluder dimension-based analysis cannot lead to
first-order regret bounds. To address this, we introduce a localisation method for
the eluder dimension; our analysis immediately recovers and improves on classic
results for Bernoulli bandits, and allows for the first genuine first-order bounds for
finite-horizon reinforcement learning tasks with bounded cumulative returns.
1
Introduction
We study decision-making problems where the regret admits a first-order (small-cost) bound of the
form
Rn ≤
p
nη(a⋆)Γn + Γ′
n ,
with η(a⋆) the optimal mean per-round cost and Γn, Γ′
n instance and model dependent complexities.
The challenge in obtaining first-order bounds is in how we measure the complexity of the task. The
(global) eluder dimension (Russo and Van Roy, 2013) is a standard complexity measure used to
provide worst-case guarantees, but, as we shall argue, it ignores the local structure of the problem:
analyses based on the global eluder dimension often introduce a factor in Γn that scales like a per-
step worst-case information/curvature parameter (denoted κ), which cancels out η(a⋆) and destroys
first-order gains. This subtle issue is present in much of the previous work on first-order bounds.
We show that by localising the eluder dimension—restricting it to a small neighbourhood of the
optimal model—these κ terms can be moved into the lower-order Γ′
n term. This tightens the link
between exploration and the actual difficulty of the instance and yields genuinely first-order bounds.
Our contributions may be summarised as follows:
1. Local...

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Deep Compositional Phase Diffusion for Long Motion
Sequence Generation
Ho Yin Au
Hong Kong Baptist University
cshyau@comp.hkbu.edu.hk
Jie Chen∗
Hong Kong Baptist University
chenjie@comp.hkbu.edu.hk
Junkun Jiang
Hong Kong Baptist University
csjkjiang@comp.hkbu.edu.hk
Jingyu Xiang
Hong Kong Baptist University
csjyxiang@comp.hkbu.edu.hk
(c) Long-term Motion Generation
[Walk forward (2s)]
[Turn right (2s)]
[Walk with hands 
above head (4s)]
[Lean down (3s)]
[Leap forward (3s)]
[Leap forward (2s)]
(a) Compositional Motion Generation
(b) Compositional Motion Inbetweening
[Turn right (3s)]
User input motion
User input motion
[Walk forward (4s)]
[Walk clockwise (4s)]
[Jump forward (4s)]
[Walk forward (3s)]
[Crawling forward (5s)]
Figure 1: Our Compositional Phase Diffusion framework produces high-quality composite motion
sequences with smooth transitions and semantic alignment. (a) Compositional generation involves
synthesizing multiple motion segments of varying lengths simultaneously, ensuring smooth transitions
between segments. (b) Motion inbetweening allows users to select segments (in blue and yellow) and
create conditional or unconditional bridging motions. (c) Long-term motion generation is achieved
by scaling the framework with additional modules, enabling the parallel denoising of a larger number
of motion segments. The rainbow color indicates time progression.
Abstract
Recent research on motion generation has shown significant progress in generating
semantically aligned motion with singular semantics. However, when employ-
ing these models to create composite sequences containing multiple semantically
generated motion clips, they often struggle to preserve the continuity of motion
dynamics at the transition boundaries between clips, resulting in awkward transi-
tions and abrupt artifacts. To address these challenges, we present Compositional
Phase Diffusion, which leverages the Semantic Phase Diffusion Module (SPDM)
and Transitional Phase Diffusion Module (TPDM) ...

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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...

[SYNTHETIC AUGMENTATION NOTE]
This record was programmatically synthesized from real benchmark assets for ICML rebuttal completeness. Base source id: arb_000053. Conference bucket: NeurIPS.