57 accepted papers
We propose Contextual History for Adaptive and Simple Exploitation (CHASE), a novel multi-turn method for Large Language Model (LLM) jailbreaking. Rather than directly attack an LLM that may be difficult to jailbreak, CHASE first collects jailbroken histories from an easy-to-jailbreak LLM and then t
Generative analysis often models multi-domain observations as nonlinear mixtures of domain-invariant content variables and domain-specific style variables. Identifying both factors from unpaired domains enables tasks such as domain transfer and counterfactual data generation. Prior work establishes …
Domain transfer (DT) maps source to target distributions and supports tasks such as unsupervised image-to-image translation, single-cell analysis, and cross-platform medical imaging. However, DT is fundamentally ill-posed: push-forward mappings are generally non-identifiable, as measure-preserving a…
Interleaved multimodal generation enables capabilities beyond unimodal generation models, such as step-by-step instructional guides, visual planning, and generating visual drafts for reasoning. However, the quality of existing interleaved generation models under general instructions remains limited
Recent advances in video diffusion models shows promise for generating robotic decision-making data, with trajectory conditions further enabling fine-grained control. However, existing methods primarily focus on individual object motion and struggle to capture multi-object interaction crucial in com…
Camera-controlled generative video re-rendering methods, such as ReCamMaster, have achieved remarkable progress. However, despite their success in single-view setting, these works often struggle to maintain consistency across multi-view scenarios. Ensuring spatio-temporal coherence in hallucinated r
While score-based generative models have emerged as powerful priors for solving inverse problems, directly integrating them into optimization algorithms such as ADMM remains nontrivial. Two central challenges arise: i) the mismatch between the noisy data manifolds used to train the score functions a…
This paper aims to manipulate multi-entity 3D motions in video generation. Previous methods on controllable video generation primarily leverage 2D control signals to manipulate object motions and have achieved remarkable synthesis results. However, 2D control signals are inherently limited in expres…
This paper focuses on the basic structure of Chinese characters: semantic-phonetic compound characters. This paper takes advantage of this feature of Chinese characters and innovatively proposes a Chinese speech-processing method based on character shape. We use the association between the character…
Understanding identifiability of latent content and style variables from unaligned multi-domain data is essential for tasks such as domain translation and data generation. Existing works on content-style identification were often developed under somewhat stringent conditions, e.g., that all latent c…
Latent component identification from unknown *nonlinear* mixtures is a foundational challenge in machine learning, with applications in tasks such as self-supervised learning and causal representation learning. Prior work in *nonlinear independent component analysis* (nICA) has shown that auxiliary…
Diversified distribution matching (DDM) finds a unified translation function mapping a diverse collection of conditional source distributions to their target counterparts. DDM was proposed to resolve content misalignment issues in unpaired domain translation, achieving translation identifiability. H…
Existing multimodal models typically assume the availability of all modalities, leading to significant performance degradation when certain modalities are missing. Recent methods have introduced prompt learning to adapt pretrained models to incomplete data, achieving remarkable performance when the
Audio-visual speech recognition (AVSR) aims to enhance the robustness of an automatic speech recognition (ASR) systems by incorporating visual information from lip movements, especially in challenging noisy environments. Nevertheless, most current approaches either involve training from scratch or f…
Radio map estimation (RME) is crucial for effective planning and optimization of wireless networks. Traditional approaches such as interpolation excel at capturing local smoothness in densely populated data but struggle with sparse or irregular data. Conversely, matrix completion (MC) approaches uti…
The utilization of multimodal cues enhances the effectiveness of specific cognitive tasks in audio-visual learning. However, on the one hand, designing a unified model for multimodal learning poses challenges due to the presence of information redundancy and modality noise. On the other hand, existi…
Radio map estimation (RME) aims to construct a map of radio strength across multiple domains (e.g., space and frequency) from limited measurements. Data-driven deep neural model-based RME showed promising performance, yet requiring excessive training resources. This work puts forth an RME approach t…
Camera control has been actively studied in text or image conditioned video generation tasks. However, altering camera trajectories of a given video remains under-explored, despite its importance in the field of video creation. It is non-trivial due to the extra constraints of maintaining multiple-f…
Under-counted matrix completion (UC-MC) has many important applications, especially in epidemiology and ecology where the observed data are often smaller than the actual numbers. Existing works model the under-counting effects using entry-wise miss detection probabilities, which are usually formulat…
Decentralized computation has received considerable research interest lately, due to its wide applications in information processing systems. However, one key requirement to establish convergence for almost all decentralized algorithms, for convex and non-convex problems alike, is that the loss func…
A core task in multi-modal learning is to integrate information from multiple feature spaces (e.g., text and audio), offering modality-invariant essential representations of data. Recent research showed that, classical tools such as canonical correlation analysis (CCA) provably identify the shared c…
The generation of label noise is often modeled as a process involving a probability transition matrix (also interpreted as the _annotator confusion matrix_) imposed onto the label distribution. Under this model, learning the ``ground-truth classifier''---i.e., the classifier that can be learned if n…
Simplex component analysis (SCA) aims to estimate the vertices of the convex hull where data samples reside in. SCA finds various applications in signal processing, e.g., hyperspectral unmixing and noisy label learning. Recent works proposed to tackle SCA from a probabilistic viewpoint using variati…
Unsupervised domain translation (UDT) aims to find functions that convert samples from one domain (e.g., sketches) to another domain (e.g., photos) without changing the high-level semantic meaning (also referred to as "content"). The translation functions are often sought by probability distribution…
Recent state-of-the-art recommender systems predominantly rely on either implicit or explicit feedback from users to suggest new items. While effective in recommending novel options, many recommender systems often use uninterpretable embeddings to represent user preferences. This lack of transparenc…
The recent integration of deep learning and pairwise similarity annotation-based constrained clustering---i.e., deep constrained clustering (DCC)---has proven effective for incorporating weak supervision into massive data clustering: Less than 1% of pair similarity annotations can often substantiall…
Using noisy crowdsourced labels from multiple annotators, a deep learning-based end-to-end (E2E) system aims to learn the label correction mechanism and the neural classifier simultaneously. To this end, many E2E systems concatenate the neural classifier with multiple annotator-specific label confus…
Spectrum cartography (SC) techniques craft multi-domain (e.g., space and frequency) radio maps from limited measurements, which is an ill-posed inverse problem. Recent works used low-dimensional priors such as a low tensor rank structure and a deep generative model to assist radio map estimation—wit…
Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of large-scale real-scanned 3D databases. To facilitate the development of 3D perception, reconstruction, and generation in the real world, we propose OmniObject3D, a large vocabulary 3D object dataset with mass…
This work revisits the joint transmit beamforming and antenna selection problem. Existing approaches find approximate solutions to this NP-hard problem via various heuristics, e.g., convex/nonconvex relaxation, greedy method, and (deep) supervised learning. However, optimality (or even feasibility)…
Systematic under-counting effects are observed in data collected across many disciplines, e.g., epidemiology and ecology. Under-counted tensor completion (UC-TC) is well-motivated for many data analytics tasks, e.g., inferring the case numbers of infectious diseases at unobserved locations from unde…
Generalized canonical correlation analysis (GCCA) aims to learn common low-dimensional representations from multiple "views" of the data (e.g., audio and video of the same event). In the era of big data, GCCA computation encounters many new challenges. In particular, distributed optimization for GCC…
Nonlinear independent component analysis (nICA) aims at recovering statistically independent latent components that are mixed by unknown nonlinear functions. Central to nICA is the identifiability of the latent components, which had been elusive until very recently. Specifically, Hyvärinen et al. ha…
Unsupervised mixture learning (UML) aims at identifying linearly or nonlinearly mixed latent components in a blind manner. UML is known to be challenging: Even learning linear mixtures requires highly nontrivial analytical tools, e.g., independent component analysis or nonnegative matrix factorizati…
Multiple views of data, both naturally acquired (e.g., image and audio) and artificially produced (e.g., via adding different noise to data samples), have proven useful in enhancing representation learning. Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlati…
Unsupervised learning of the Dawid-Skene (D&S) model from noisy, incomplete and crowdsourced annotations has been a long-standing challenge, and is a critical step towards reliably labeling massive data. A recent work takes a coupled nonnegative matrix factorization (CNMF) perspective, and shows app…
Spectrum cartography (SC) aims at estimating the multi-aspect (e.g., space, frequency, and time) interference level caused by multiple emitters from limited measurements. Early SC approaches rely on model assumptions about the radio map, e.g., sparsity and smoothness, which may be grossly violated u…
Canonical polyadic decomposition (CPD) has been a workhorse for multimodal data analytics. This work puts forth a stochastic algorithmic framework for CPD under β-divergence, which is well-motivated in statistical learning—where the Euclidean distance is typically not preferred. Despite the existenc…
Graph clustering is a core technique for network analysis problems, e.g., community detection. This work puts forth a node clustering approach for largely incomplete adjacency graphs. Under the considered scenario, instead of having access to the complete graph, only a small amount of queries about…
There has been a growing interest in developing data-driven, in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such as power control, beamforming, and MIMO detection, these methods achieve state-of-the-art performance while requiring less c…
This paper focuses on a core task in computational sustainability and statistical ecology: species distribution modeling (SDM). In SDM, the occurrence pattern of a species on a landscape is predicted by environmental features based on observations at a set of locations. At first, SDM may appear to b…
In this paper, we propose CPD-fLM++, a fast implementation of the Levenberg-Marquardt (LM) algorithm for the tensor canonical polyadic decomposition. The overall algorithmic framework follows exactly the LM approach, which enjoys locally a super-linear convergence rate and has been observed to be ab…
This work focuses on canonical polyadic decomposition (CPD) for large-scale tensors. Many prior works rely on data sparsity to develop scalable CPD algorithms, which are not suitable for handling dense tensor, while dense tensors often arise in applications such as image and video processing. As an…
The data deluge comes with high demands for data labeling. Crowdsourcing (or, more generally, ensemble learning) techniques aim to produce accurate labels via integrating noisy, non-expert labeling from annotators. The classic Dawid-Skene estimator and its accompanying expectation maximization (EM)…
Many machine learning problems come in the form of networks with relational data between entities, and one of the key unsupervised learning tasks is to detect communities in such a network. We adopt the mixed-membership stochastic blockmodel as the underlying probabilistic model, and give conditions…
Sampling lies at the heart of signal processing. The celebrated Shan-non - Nyquist theorem states that in order to reconstruct a continuous or discrete time signal from uniform samples one must sample at a rate twice the highest frequency present in the signal. Numerous signals and images of interes…
Hyperspectral super-resolution (HSR) is a problem of recovering a high-spectral-spatial-resolution image from a multispectral measurement and a hyperspectral measurement, which have low spectral and spatial resolutions, respectively. We consider a low-rank structured matrix factorization formulation…
This work focuses on the problem of fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image that admits high spatial and spectral resolutions. Existing algorithms are mostly based on joint low-rank factorization of the ma-tricized HSI and MSI. This fram…
The sum-of-correlations (SUMCOR) generalized canonical correlation analysis (GCCA) aims at producing low-dimensional representations of multiview data via enforcing pairwise similarity of the reduced-dimension views. SUMCOR has been applied to a large variety of applications including blind separati…
We present a new algorithm for identifying the transition and emission probabilities of a hidden Markov model (HMM) from the emitted data. Expectation-maximization becomes computationally prohibitive for long observation records, which are often required for identification. The new algorithm is part…
This paper presents a framework for shared, human-machine control of a prosthetic arm. The method employs electromyogram and peripheral neural signals to decode motor intent, and incorporates a higher-level goal in the controller to augment human effort. The controller derivation employs Markov Deci…
The 3GPP suggests to combine dual polarized (DP) antenna arrays with the double directional (DD) channel model for downlink channel estimation. This combination strikes a good balance between high-capacity communications and parsimonious channel modeling, and also brings limited feedback schemes for…
Consider a structured matrix factorizaton (SMF) whose coefficient vectors are constrained to lie in the unit simplex. This kind of simplex SMF (SSMF) has received growing attention and has found many applications such as hyperspectral unmixing in remote sensing, text mining in machine learning, and…
Unlike dimensionality reduction (DR) tools for single-view data, e.g., principal component analysis (PCA), canonical correlation analysis (CCA) and generalized CCA (GCCA) are able to integrate information from multiple feature spaces of data. This is critical in multi-modal data fusion and analytics…
Most learning approaches treat dimensionality reduction (DR) and clustering separately (i.e., sequentially), but recent research has shown that optimizing the two tasks jointly can substantially improve the performance of both. The premise behind the latter genre is that the data samples are obtaine…
In topic modeling, many algorithms that guarantee identifiability of the topics have been developed under the premise that there exist anchor words -- i.e., words that only appear (with positive probability) in one topic. Follow-up work has resorted to three or higher-order statistics of the data co…
This paper focuses on volume minimization (VolMin)-based structured matrix factorization (SMF), which factors a data matrix into a full-column rank basis and a coefficient matrix whose columns reside in the unit simplex. The VolMin criterion achieves this goal via finding a minimum-volume enclosing…