45 research outputs found
Disturbance Grassmann Kernels for Subspace-Based Learning
In this paper, we focus on subspace-based learning problems, where data
elements are linear subspaces instead of vectors. To handle this kind of data,
Grassmann kernels were proposed to measure the space structure and used with
classifiers, e.g., Support Vector Machines (SVMs). However, the existing
discriminative algorithms mostly ignore the instability of subspaces, which
would cause the classifiers misled by disturbed instances. Thus we propose
considering all potential disturbance of subspaces in learning processes to
obtain more robust classifiers. Firstly, we derive the dual optimization of
linear classifiers with disturbance subject to a known distribution, resulting
in a new kernel, Disturbance Grassmann (DG) kernel. Secondly, we research into
two kinds of disturbance, relevant to the subspace matrix and singular values
of bases, with which we extend the Projection kernel on Grassmann manifolds to
two new kernels. Experiments on action data indicate that the proposed kernels
perform better compared to state-of-the-art subspace-based methods, even in a
worse environment.Comment: This paper include 3 figures, 10 pages, and has been accpeted to
SIGKDD'1
Locality Preserving Projections for Grassmann manifold
Learning on Grassmann manifold has become popular in many computer vision
tasks, with the strong capability to extract discriminative information for
imagesets and videos. However, such learning algorithms particularly on
high-dimensional Grassmann manifold always involve with significantly high
computational cost, which seriously limits the applicability of learning on
Grassmann manifold in more wide areas. In this research, we propose an
unsupervised dimensionality reduction algorithm on Grassmann manifold based on
the Locality Preserving Projections (LPP) criterion. LPP is a commonly used
dimensionality reduction algorithm for vector-valued data, aiming to preserve
local structure of data in the dimension-reduced space. The strategy is to
construct a mapping from higher dimensional Grassmann manifold into the one in
a relative low-dimensional with more discriminative capability. The proposed
method can be optimized as a basic eigenvalue problem. The performance of our
proposed method is assessed on several classification and clustering tasks and
the experimental results show its clear advantages over other Grassmann based
algorithms.Comment: Accepted by IJCAI 201
IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion
Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing
for a precise capture of the evolution of knowledge and reflecting the dynamic
nature of the real world. Typically, TKGs contain complex geometric structures,
with various geometric structures interwoven. However, existing Temporal
Knowledge Graph Completion (TKGC) methods either model TKGs in a single space
or neglect the heterogeneity of different curvature spaces, thus constraining
their capacity to capture these intricate geometric structures. In this paper,
we propose a novel Integrating Multi-curvature shared and specific Embedding
(IME) model for TKGC tasks. Concretely, IME models TKGs into multi-curvature
spaces, including hyperspherical, hyperbolic, and Euclidean spaces.
Subsequently, IME incorporates two key properties, namely space-shared property
and space-specific property. The space-shared property facilitates the learning
of commonalities across different curvature spaces and alleviates the spatial
gap caused by the heterogeneous nature of multi-curvature spaces, while the
space-specific property captures characteristic features. Meanwhile, IME
proposes an Adjustable Multi-curvature Pooling (AMP) approach to effectively
retain important information. Furthermore, IME innovatively designs similarity,
difference, and structure loss functions to attain the stated objective.
Experimental results clearly demonstrate the superior performance of IME over
existing state-of-the-art TKGC models
Tight Analysis of Decrypton Failure Probability of Kyber in Reality
Kyber is a candidate in the third round of the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography (PQC) Standardization. However, because of the protocol\u27s independence assumption, the bound on the decapsulation failure probability resulting from the original analysis is not tight. In this work, we give a rigorous mathematical analysis of the actual failure probability calculation, and provides the Kyber security estimation in reality rather than only in a statistical sense. Our analysis does not make independency assumptions on errors, and is with respect to concrete public keys in reality. Through sample test and experiments, we also illustrate the difference between the actual failure probability and the result given in the proposal of Kyber. The experiments show that, for Kyber-512 and 768, the failure probability resulting from the original paper is relatively conservative, but for Kyber-1024, the failure probability of some public keys is worse than claimed. This failure probability calculation for concrete public keys can also guide the selection of public keys in the actual application scenarios. What\u27s more, we measure the gap between the upper bound of the failure probability and the actual failure probability, then give a tight estimate. Our work can also re-evaluate the traditional correctness in the literature, which will help re-evaluate some candidates\u27 security in NIST post-quantum cryptographic standardization
In-context Learning for Automated Driving Scenarios
One of the key challenges in current Reinforcement Learning (RL)-based
Automated Driving (AD) agents is achieving flexible, precise, and human-like
behavior cost-effectively. This paper introduces an innovative approach
utilizing Large Language Models (LLMs) to intuitively and effectively optimize
RL reward functions in a human-centric way. We developed a framework where
instructions and dynamic environment descriptions are input into the LLM. The
LLM then utilizes this information to assist in generating rewards, thereby
steering the behavior of RL agents towards patterns that more closely resemble
human driving. The experimental results demonstrate that this approach not only
makes RL agents more anthropomorphic but also reaches better performance.
Additionally, various strategies for reward-proxy and reward-shaping are
investigated, revealing the significant impact of prompt design on shaping an
AD vehicle's behavior. These findings offer a promising direction for the
development of more advanced and human-like automated driving systems. Our
experimental data and source code can be found here.Comment: 7 pages, 6 figures, 35 reference