Search Results for author:
Found 40 papers, 28 papers with code
Graph Diffusion Policy Optimization
Recent research has made significant progress in optimizing diffusion models for specific downstream objectives, which is an important pursuit in fields such as graph generation for drug design.
Purifying Large Language Models by Ensembling a Small Language Model
The emerging success of large language models (LLMs) heavily relies on collecting abundant training data from external (untrusted) sources.
Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One
Building on this insight and observation, we develop FairThinking, a pipeline designed to automatically generate roles that enable LLMs to articulate diverse perspectives for fair expressions.
Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast
A multimodal large language model (MLLM) agent can receive instructions, capture images, retrieve histories from memory, and decide which tools to use.
Test-Time Backdoor Attacks on Multimodal Large Language Models
Backdoor attacks are commonly executed by contaminating training data, such that a trigger can activate predetermined harmful effects during the test phase.
Weak-to-Strong Jailbreaking on Large Language Models
In this paper, we propose the weak-to-strong jailbreaking attack, an efficient method to attack aligned LLMs to produce harmful text.
Locality Sensitive Sparse Encoding for Learning World Models Online
Unfortunately, NN-based models need re-training on all accumulated data at every interaction step to achieve FTL, which is computationally expensive for lifelong agents.
Benchmarking Large Multimodal Models against Common Corruptions
This technical report aims to fill a deficiency in the assessment of large multimodal models (LMMs) by specifically examining the self-consistency of their outputs when subjected to common corruptions.
Contrastive Learning with Negative Sampling Correction
To correct the negative sampling bias, we propose a novel contrastive learning method named Positive-Unlabeled Contrastive Learning (PUCL).
TaskWeaver: A Code-First Agent Framework
TaskWeaver provides support for rich data structures, flexible plugin usage, and dynamic plugin selection, and leverages LLM coding capabilities for complex logic.
Finetuning Text-to-Image Diffusion Models for Fairness
The rapid adoption of text-to-image diffusion models in society underscores an urgent need to address their biases.
Intriguing Properties of Data Attribution on Diffusion Models
Data attribution seeks to trace model outputs back to training data.
On Memorization in Diffusion Models
Looking into this, we first observe that memorization behaviors tend to occur on smaller-sized datasets, which motivates our definition of effective model memorization (EMM), a metric measuring the maximum size of training data at which a learned diffusion model approximates its theoretical optimum.
Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction
To mitigate the impact of label uncertainty and improve the robustness of learning with positive and unlabeled data, we propose a new robust PU learning method with a training strategy motivated by the nature of human learning: easy cases should be learned first.
LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition
This paper investigates LoRA composability for cross-task generalization and introduces LoraHub, a simple framework devised for the purposive assembly of LoRA modules trained on diverse given tasks, with the objective of achieving adaptable performance on unseen tasks.
AdAM: Few-Shot Image Generation via Adaptation-Aware Kernel Modulation
However, a major limitation of existing methods is that their knowledge preserving criteria consider only source domain/task and fail to consider target domain/adaptation in selecting source knowledge, casting doubt on their suitability for setups of different proximity between source and target domain.
Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning
In this regard, we explore the problem of constrained bidding in adversarial bidding environments, which assumes no knowledge about the adversarial factors.
Exploring Model Dynamics for Accumulative Poisoning Discovery
In this paper, we dive into the perspective of model dynamics and propose a novel information measure, namely, Memorization Discrepancy, to explore the defense via the model-level information.
Efficient Diffusion Policies for Offline Reinforcement Learning
2) It is incompatible with maximum likelihood-based RL algorithms (e. g., policy gradient methods) as the likelihood of diffusion models is intractable.
On Evaluating Adversarial Robustness of Large Vision-Language Models
Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented performance in response generation, especially with visual inputs, enabling more creative and adaptable interaction than large language models such as ChatGPT.
Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows
Sliced-Wasserstein Flow (SWF) is a promising approach to nonparametric generative modeling but has not been widely adopted due to its suboptimal generative quality and lack of conditional modeling capabilities.
Exploring Incompatible Knowledge Transfer in Few-shot Image Generation
To this end, we propose knowledge truncation to mitigate this issue in FSIG, which is a complementary operation to knowledge preservation and is implemented by a lightweight pruning-based method.
CoSDA: Continual Source-Free Domain Adaptation
Recently, SFDA has gained popularity due to the need to protect the data privacy of the source domain, but it suffers from catastrophic forgetting on the source domain due to the lack of data.
A Recipe for Watermarking Diffusion Models
Diffusion models (DMs) have demonstrated advantageous potential on generative tasks.
Bag of Tricks for Training Data Extraction from Language Models
With the advance of language models, privacy protection is receiving more attention.
Better Diffusion Models Further Improve Adversarial Training
Under the $\ell_\infty$-norm threat model with $\epsilon=8/255$, our models achieve $70. 69\%$ and $42. 67\%$ robust accuracy on CIFAR-10 and CIFAR-100, respectively, i. e. improving upon previous state-of-the-art models by $+4. 58\%$ and $+8. 03\%$.
Does Federated Learning Really Need Backpropagation?
BAFFLE is 1) memory-efficient and easily fits uploading bandwidth; 2) compatible with inference-only hardware optimization and model quantization or pruning; and 3) well-suited to trusted execution environments, because the clients in BAFFLE only execute forward propagation and return a set of scalars to the server.
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning
Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems.
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks.
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e. g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models.
Improving Adversarial Robustness via Promoting Ensemble Diversity
Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial attacks.
To Relieve Your Headache of Training an MRF, Take AdVIL
We propose a black-box algorithm called {\it Adversarial Variational Inference and Learning} (AdVIL) to perform inference and learning on a general Markov random field (MRF).
Learning Implicit Generative Models by Teaching Explicit Ones
Implicit generative models are difficult to train as no explicit density functions are defined.
Max-Mahalanobis Linear Discriminant Analysis Networks
In this paper, we show that a properly designed classifier can improve robustness to adversarial attacks and lead to better prediction results.
Towards Robust Detection of Adversarial Examples
Although the recent progress is substantial, deep learning methods can be vulnerable to the maliciously generated adversarial examples.
Collaborative Filtering with User-Item Co-Autoregressive Models
Deep neural networks have shown promise in collaborative filtering (CF).
Field-Programmable Crossbar Array (FPCA) for Reconfigurable Computing
The proposed computing architecture is based on a uniform, physical, resistive, memory-centric fabric that can be optimally reconfigured and utilized to perform different computing and data storage tasks in a massively parallel approach.
Interactive Visual Hull Refinement for Specular and Transparent Object Surface Reconstruction
We discovered that these internal contours, which are results of convex parts on an object's surface, can lead to a tighter fit than the original visual hull.
Learning Deep Generative Models with Doubly Stochastic MCMC
We present doubly stochastic gradient MCMC, a simple and generic method for (approximate) Bayesian inference of deep generative models (DGMs) in a collapsed continuous parameter space.
Inner Product Similarity Search using Compositional Codes
This paper addresses the nearest neighbor search problem under inner product similarity and introduces a compact code-based approach.
