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Found 81 papers, 6 papers with code
A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization
Medical entity normalization, which links medical mentions in the text to entities in knowledge bases, is an important research topic in medical natural language processing.
Bias and Toxicity in Role-Play Reasoning
Role-play in the Large Language Model (LLM) is a crucial technique that enables models to adopt specific perspectives, enhancing their ability to generate contextually relevant and accurate responses.
A Re-solving Heuristic for Dynamic Assortment Optimization with Knapsack Constraints
In this paper, we consider a multi-stage dynamic assortment optimization problem with multi-nomial choice modeling (MNL) under resource knapsack constraints.
Research on Autonomous Robots Navigation based on Reinforcement Learning
We use the Deep Q Network (DQN) and Proximal Policy Optimization (PPO) models to optimize the path planning and decision-making process through the continuous interaction between the robot and the environment, and the reward signals with real-time feedback.
Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity
Optimization of convex functions under stochastic zeroth-order feedback has been a major and challenging question in online learning.
Federated Contrastive Learning for Personalized Semantic Communication
Simulation results verify the superiority of the proposed FedCL framework compared to other distributed learning benchmarks in terms of task performance and robustness under different numbers of clients and channel conditions, especially in low signal-to-noise ratio and highly heterogeneous data scenarios.
Navigate Beyond Shortcuts: Debiased Learning through the Lens of Neural Collapse
Recent studies have noted an intriguing phenomenon termed Neural Collapse, that is, when the neural networks establish the right correlation between feature spaces and the training targets, their last-layer features, together with the classifier weights, will collapse into a stable and symmetric structure.
On the Optimal Regret of Locally Private Linear Contextual Bandit
Contextual bandit with linear reward functions is among one of the most extensively studied models in bandit and online learning research.
Demand Balancing in Primal-Dual Optimization for Blind Network Revenue Management
This paper proposes a practically efficient algorithm with optimal theoretical regret which solves the classical network revenue management (NRM) problem with unknown, nonparametric demand.
Gender Bias in Large Language Models across Multiple Languages
We investigate the outputs of the GPT series of LLMs in various languages using our three measurement methods.
Cracking Factual Knowledge: A Comprehensive Analysis of Degenerate Knowledge Neurons in Large Language Models
Large language models (LLMs) store extensive factual knowledge, but the underlying mechanisms remain unclear.
Utility Fairness in Contextual Dynamic Pricing with Demand Learning
This paper introduces a novel contextual bandit algorithm for personalized pricing under utility fairness constraints in scenarios with uncertain demand, achieving an optimal regret upper bound.
Automatic Coronary Artery Plaque Quantification and CAD-RADS Prediction using Mesh Priors
This requires analysis of the coronary lumen and plaque.
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms
We consider a fundamental setting in which the objective function is quadratic, and provide the first tight characterization of the optimal Hessian-dependent sample complexity.
Contextual Dictionary Lookup for Knowledge Graph Completion
We extend several KGE models with the method, resulting in substantial performance improvements on widely-used benchmark datasets.
Leveraging Auxiliary Domain Parallel Data in Intermediate Task Fine-tuning for Low-resource Translation
In this paper, we show that intermediate-task fine-tuning (ITFT) of PMSS models is extremely beneficial for domain-specific NMT, especially when target domain data is limited/unavailable and the considered languages are missing or under-represented in the PMSS model.
Optimization of Image Transmission in a Cooperative Semantic Communication Networks
In this paper, a semantic communication framework for image transmission is developed.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
+1
xTrimoABFold: De novo Antibody Structure Prediction without MSA
Its improvement in both accuracy and efficiency makes it a valuable tool for de novo antibody design and could make further improvements in immuno-theory.
On Adaptivity in Non-stationary Stochastic Optimization With Bandit Feedback
In this paper we study the non-stationary stochastic optimization question with bandit feedback and dynamic regret measures.
Detecting Political Biases of Named Entities and Hashtags on Twitter
We examine a variety of applications and we thereby demonstrate the effectiveness of our PEM model.
Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach
Hence, the BS must select an appropriate resource block for each user as well as determine and transmit part of the semantic information to the users.
Network Revenue Management with Demand Learning and Fair Resource-Consumption Balancing
We introduce the regularized revenue, i. e., the total revenue with a balancing regularization, as our objective to incorporate fair resource-consumption balancing into the revenue maximization goal.
Cracking White-box DNN Watermarks via Invariant Neuron Transforms
On $9$ state-of-the-art white-box watermarking schemes and a broad set of industry-level DNN architectures, our attack for the first time reduces the embedded identity message in the protected models to be almost random.
Degree aware based adversarial graph convolutional networks for entity alignment in heterogeneous knowledge graph
This degree difference between equivalent entities poses a great challenge for entity alignment.
Active Learning for Contextual Search with Binary Feedbacks
In particular, for a sequence of arriving context vectors, with each context associated with an underlying value, the decision-maker either makes a query at a certain point or skips the context.
Differential Privacy in Personalized Pricing with Nonparametric Demand Models
In the recent decades, the advance of information technology and abundant personal data facilitate the application of algorithmic personalized pricing.
Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks
The problem is formulated as an optimization problem whose goal is to maximize the reliability of the VR network by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs.
Adversarial Combinatorial Bandits with General Non-linear Reward Functions
{The adversarial combinatorial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic bandit, or semi-bandit feedback.}
Smooth Bandit Optimization: Generalization to Hölder Space
We also study adaptation to unknown function smoothness over a continuous scale of H\"older spaces indexed by $\alpha$, with a bandit model selection approach applied with our proposed two-layer algorithms.
Privacy-Preserving Dynamic Personalized Pricing with Demand Learning
In this paper, we consider a dynamic pricing problem over $T$ time periods with an \emph{unknown} demand function of posted price and personalized information.
Constant Regret Re-solving Heuristics for Price-based Revenue Management
First, we prove that a natural re-solving heuristic attains $O(1)$ regret compared to the value of the optimal policy.
Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection
We consider the stochastic contextual bandit problem under the high dimensional linear model.
CASIA's System for IWSLT 2020 Open Domain Translation
This paper describes the CASIA{'}s system for the IWSLT 2020 open domain translation task.
Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing
In this paper, using the problem of demand function prediction in dynamic pricing as the motivating example, we study the problem of constructing accurate confidence intervals for the demand function.
Optimism in Reinforcement Learning with Generalized Linear Function Approximation
We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation.
Deep Learning for Optimal Deployment of UAVs with Visible Light Communications
Using GRUs and CNNs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution.
Synchronously Generating Two Languages with Interactive Decoding
In this paper, we introduce a novel interactive approach to translate a source language into two different languages simultaneously and interactively.
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers
We establish both upper and lower bounds on the regret, and show that our policy is optimal up to logarithmic factor in $T$ when the assortment capacity is constant.
Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVs
This problem is formulated as an optimization problem whose goal is to minimize the total transmit power while meeting the illumination and communication requirements of users.
$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank
Our learning algorithm, Adaptive Value-function Elimination (AVE), is inspired by the policy elimination algorithm proposed in (Jiang et al., 2017), known as OLIVE.
NCLS: Neural Cross-Lingual Summarization
Moreover, we propose to further improve NCLS by incorporating two related tasks, monolingual summarization and machine translation, into the training process of CLS under multi-task learning.
A Compact and Language-Sensitive Multilingual Translation Method
We verify our methods on various translation scenarios, including one-to-many, many-to-many and zero-shot.
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits
Linear contextual bandit is an important class of sequential decision making problems with a wide range of applications to recommender systems, online advertising, healthcare, and many other machine learning related tasks.
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits
We study the linear contextual bandit problem with finite action sets.
How Many Samples are Needed to Estimate a Convolutional Neural Network?
We show that for an $m$-dimensional convolutional filter with linear activation acting on a $d$-dimensional input, the sample complexity of achieving population prediction error of $\epsilon$ is $\widetilde{O(m/\epsilon^2)$, whereas the sample-complexity for its FNN counterpart is lower bounded by $\Omega(d/\epsilon^2)$ samples.
Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models
In this paper we consider the dynamic assortment selection problem under an uncapacitated multinomial-logit (MNL) model.
Dynamic Assortment Optimization with Changing Contextual Information
To this end, we develop an upper confidence bound (UCB) based policy and establish the regret bound on the order of $\widetilde O(d\sqrt{T})$, where $d$ is the dimension of the feature and $\widetilde O$ suppresses logarithmic dependence.
Efficient Load Sampling for Worst-Case Structural Analysis Under Force Location Uncertainty
Our approach uses a computationally tractable experimental design method to select number of sample force locations based on geometry only, without inspecting the stress response that requires computationally expensive finite-element analysis.
Three Strategies to Improve One-to-Many Multilingual Translation
However, previous studies show that one-to-many translation based on this framework cannot perform on par with the individually trained models.
Dynamic Assortment Selection under the Nested Logit Models
One key challenge is that utilities of products are unknown to the seller and need to be learned.
Robust Nonparametric Regression under Huber's $ε$-contamination Model
We first show that a simple local binning median step can effectively remove the adversary noise and this median estimator is minimax optimal up to absolute constants over the H\"{o}lder function class with smoothness parameters smaller than or equal to 1.
Phrase Table as Recommendation Memory for Neural Machine Translation
Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently.
How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network?
It is widely believed that the practical success of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) owes to the fact that CNNs and RNNs use a more compact parametric representation than their Fully-Connected Neural Network (FNN) counterparts, and consequently require fewer training examples to accurately estimate their parameters.
An Optimal Policy for Dynamic Assortment Planning Under Uncapacitated Multinomial Logit Models
We further establish the matching lower bound result to show the optimality of our policy.
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates
In this setup, an algorithm is allowed to adaptively query the underlying function at different locations and receives noisy evaluations of function values at the queried points (i. e. the algorithm has access to zeroth-order information).
Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels
Local polynomial regression (Fan and Gijbels 1996) is an important class of methods for nonparametric density estimation and regression problems.
Direct Learning to Rank and Rerank
Learning-to-rank techniques have proven to be extremely useful for prioritization problems, where we rank items in order of their estimated probabilities, and dedicate our limited resources to the top-ranked items.
Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach
The experimental design problem concerns the selection of k points from a potentially large design pool of p-dimensional vectors, so as to maximize the statistical efficiency regressed on the selected k design points.
Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT
Our experiments show that subword model performs best for Chinese-to-English translation with the vocabulary which is not so big while hybrid word-character model is most suitable for English-to-Chinese translation.
Towards Neural Machine Translation with Partially Aligned Corpora
While neural machine translation (NMT) has become the new paradigm, the parameter optimization requires large-scale parallel data which is scarce in many domains and language pairs.
Convergence Rates of Latent Topic Models Under Relaxed Identifiability Conditions
In this paper we study the frequentist convergence rate for the Latent Dirichlet Allocation (Blei et al., 2003) topic models.
Stochastic Zeroth-order Optimization in High Dimensions
We consider the problem of optimizing a high-dimensional convex function using stochastic zeroth-order queries.
A Note on a Tight Lower Bound for MNL-Bandit Assortment Selection Models
In this short note we consider a dynamic assortment planning problem under the capacitated multinomial logit (MNL) bandit model.
Non-stationary Stochastic Optimization under $L_{p,q}$-Variation Measures
We consider a non-stationary sequential stochastic optimization problem, in which the underlying cost functions change over time under a variation budget constraint.
Near-Optimal Design of Experiments via Regret Minimization
We consider computationally tractable methods for the experimental design problem, where k out of n design points of dimension p are selected so that certain optimality criteria are approximately satisfied.
Sequence Modeling via Segmentations
The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment is modeled using existing tools such as recurrent neural networks.
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems
This observation leads to many interesting results on general high-rank matrix estimation problems, which we briefly summarize below ($A$ is an $n\times n$ high-rank PSD matrix and $A_k$ is the best rank-$k$ approximation of $A$): (1) High-rank matrix completion: By observing $\Omega(\frac{n\max\{\epsilon^{-4}, k^2\}\mu_0^2\|A\|_F^2\log n}{\sigma_{k+1}(A)^2})$ elements of $A$ where $\sigma_{k+1}\left(A\right)$ is the $\left(k+1\right)$-th singular value of $A$ and $\mu_0$ is the incoherence, the truncated SVD on a zero-filled matrix satisfies $\|\widehat{A}_k-A\|_F \leq (1+O(\epsilon))\|A-A_k\|_F$ with high probability.
Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates
We consider the problems of estimation and of constructing component-wise confidence intervals in a sparse high-dimensional linear regression model when some covariates of the design matrix are missing completely at random.
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data
Subspace clustering is the problem of partitioning unlabeled data points into a number of clusters so that data points within one cluster lie approximately on a low-dimensional linear subspace.
Data Poisoning Attacks on Factorization-Based Collaborative Filtering
Recommendation and collaborative filtering systems are important in modern information and e-commerce applications.
Online and Differentially-Private Tensor Decomposition
In this paper, we resolve many of the key algorithmic questions regarding robustness, memory efficiency, and differential privacy of tensor decomposition.
An Improved Gap-Dependency Analysis of the Noisy Power Method
We consider the noisy power method algorithm, which has wide applications in machine learning and statistics, especially those related to principal component analysis (PCA) under resource (communication, memory or privacy) constraints.
Spectral Learning for Supervised Topic Models
Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet allocation (LDA), with provable guarantees.
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models
We derive computationally tractable methods to select a small subset of experiment settings from a large pool of given design points.
Differentially private subspace clustering
Subspace clustering is an unsupervised learning problem that aims at grouping data points into multiple ``clusters'' so that data points in a single cluster lie approximately on a low-dimensional linear subspace.
Fast and Guaranteed Tensor Decomposition via Sketching
Such tensor contractions are encountered in decomposition methods such as tensor power iterations and alternating least squares.
Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data
We consider the problem of matrix column subset selection, which selects a subset of columns from an input matrix such that the input can be well approximated by the span of the selected columns.
Graph Connectivity in Noisy Sparse Subspace Clustering
A line of recent work (4, 19, 24, 20) provided strong theoretical guarantee for sparse subspace clustering (4), the state-of-the-art algorithm for subspace clustering, on both noiseless and noisy data sets.
Spectral Methods for Supervised Topic Models
Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document.
Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition
We present a simple noise-robust margin-based active learning algorithm to find homogeneous (passing the origin) linear separators and analyze its error convergence when labels are corrupted by noise.
A Theoretical Analysis of NDCG Type Ranking Measures
We show that NDCG with logarithmic discount has consistent distinguishability although it converges to the same limit for all ranking functions.
