Search Results for author:
Found 35 papers, 16 papers with code
Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering
In this paper, we identify a critical problem, "lost-in-retrieval", in retrieval-augmented multi-hop question answering (QA): the key entities are missed in LLMs' sub-question decomposition.
Controllable Protein Sequence Generation with LLM Preference Optimization
Designing proteins with specific attributes offers an important solution to address biomedical challenges.
Personalized Language Model Learning on Text Data Without User Identifiers
In many practical natural language applications, user data are highly sensitive, requiring anonymous uploads of text data from mobile devices to the cloud without user identifiers.
Provable Partially Observable Reinforcement Learning with Privileged Information
Partial observability of the underlying states generally presents significant challenges for reinforcement learning (RL).
Partially Observable Reinforcement Learning
reinforcement-learning
+2
MVBoost: Boost 3D Reconstruction with Multi-View Refinement
The key of MVBoost is combining the advantages of the high accuracy of the multi-view generation model and the consistency of the 3D reconstruction model to create a reliable data source.
AdaFlow: Opportunistic Inference on Asynchronous Mobile Data with Generalized Affinity Control
In response, we present a shift to \textit{opportunistic} inference for asynchronous distributed multi-modal data, enabling inference as soon as partial data arrives.
fairBERTs: Erasing Sensitive Information Through Semantic and Fairness-aware Perturbations
Pre-trained language models (PLMs) have revolutionized both the natural language processing research and applications.
Is poisoning a real threat to LLM alignment? Maybe more so than you think
Recent advancements in Reinforcement Learning with Human Feedback (RLHF) have significantly impacted the alignment of Large Language Models (LLMs).
Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation
We alleviate the issue of imbalanced attribute correlations during training using counterfactual feature vectors in the attribute latent space by disentanglement.
Do LLM Agents Have Regret? A Case Study in Online Learning and Games
To better understand the limits of LLM agents in these interactive environments, we propose to study their interactions in benchmark decision-making settings in online learning and game theory, through the performance metric of \emph{regret}.
Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies
In light of the burgeoning success of reinforcement learning (RL) in diverse real-world applications, considerable focus has been directed towards ensuring RL policies are robust to adversarial attacks during test time.
Knowledge Graph Error Detection with Contrastive Confidence Adaption
Knowledge graphs (KGs) often contain various errors.
Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment
Optimal decision-making presents a significant challenge for autonomous systems operating in uncertain, stochastic and time-varying environments.
Partially Observable Multi-Agent Reinforcement Learning with Information Sharing
We establish concrete computational and sample complexities under several common structural assumptions of the model.}
Computational Efficiency
Multi-agent Reinforcement Learning
+3
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
To tackle this challenge, we propose GRAD, a novel game-theoretic approach that treats the temporally-coupled robust RL problem as a partially observable two-player zero-sum game.
Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL
To address these limitations, we introduce a generalized attack framework that has the flexibility to model to what extent the adversary is able to control the agent, and allows the attacker to regulate the state distribution shift and produce stealthier adversarial policies.
FairRec: Fairness Testing for Deep Recommender Systems
Given the testing report, by adopting a simple re-ranking mitigation strategy on these identified disadvantaged groups, we show that the fairness of DRSs can be significantly improved.
Deep Reinforcement Learning for Localizability-Enhanced Navigation in Dynamic Human Environments
The planner learns to assign different importance to the geometric features and encourages the robot to navigate through areas that are helpful for laser localization.
A Survey on Knowledge-Enhanced Pre-trained Language Models
Natural Language Processing (NLP) has been revolutionized by the use of Pre-trained Language Models (PLMs) such as BERT.
RoChBert: Towards Robust BERT Fine-tuning for Chinese
Despite of the superb performance on a wide range of tasks, pre-trained language models (e. g., BERT) have been proved vulnerable to adversarial texts.
SemAttack: Natural Textual Attacks via Different Semantic Spaces
In particular, SemAttack optimizes the generated perturbations constrained on generic semantic spaces, including typo space, knowledge space (e. g., WordNet), contextualized semantic space (e. g., the embedding space of BERT clusterings), or the combination of these spaces.
Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
With this unified diversity measure, we design the corresponding diversity-promoting objective and population effectivity when seeking the best responses in open-ended learning.
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders
Meanwhile, the existing of unobserved confounders which affect the input features and labels simultaneously cause spurious correlation and hinder the learning of the invariant relationship contained in the conditional distribution.
Counterfactual Adversarial Learning with Representation Interpolation
Deep learning models exhibit a preference for statistical fitting over logical reasoning.
Incorporating External POS Tagger for Punctuation Restoration
Punctuation restoration is an important post-processing step in automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Third, to deploy MAAB in the large-scale advertising system with millions of advertisers, we propose a mean-field approach.
Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
With this unified diversity measure, we design the corresponding diversity-promoting objective and population effectivity when seeking the best responses in open-ended learning.
Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
In e-commerce advertising, it is crucial to jointly consider various performance metrics, e. g., user experience, advertiser utility, and platform revenue.
Enhancing Model Robustness By Incorporating Adversarial Knowledge Into Semantic Representation
Extensive experiments on two real-world tasks show that AdvGraph exhibits better performance compared with previous work: (i) effective - it significantly strengthens the model robustness even under the adaptive attacks setting without negative impact on model performance over legitimate input; (ii) generic - its key component, i. e., the representation of connotative adversarial knowledge is task-agnostic, which can be reused in any Chinese-based NLP models without retraining; and (iii) efficient - it is a light-weight defense with sub-linear computational complexity, which can guarantee the efficiency required in practical scenarios.
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.
Revisit Systematic Generalization via Meaningful Learning
Humans can systematically generalize to novel compositions of existing concepts.
PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening
This paper addresses the problem of remote sensing image pan-sharpening from the perspective of generative adversarial learning.
Invisible Mask: Practical Attacks on Face Recognition with Infrared
We propose a kind of brand new attack against face recognition systems, which is realized by illuminating the subject using infrared according to the adversarial examples worked out by our algorithm, thus face recognition systems can be bypassed or misled while simultaneously the infrared perturbations cannot be observed by raw eyes.
Cryptography and Security
Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild
In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild.
Cryptography and Security
Remote Sensing Image Fusion Based on Two-stream Fusion Network
Unlike previous CNN based methods that consider pan-sharpening as a super resolution problem and perform pan-sharpening in pixel level, the proposed TFNet aims to fuse PAN and MS images in feature level and reconstruct the pan-sharpened image from the fused features.
