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Found 151 papers, 19 papers with code
Explaining Why: How Instructions and User Interfaces Impact Annotator Rationales When Labeling Text Data
In the context of data labeling, NLP researchers are increasingly interested in having humans select rationales, a subset of input tokens relevant to the chosen label.
Low-dimensional adaptation of diffusion models: Convergence in total variation
This paper investigates how diffusion generative models leverage (unknown) low-dimensional structure to accelerate sampling.
Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment
As a drop-in enhancement to the existing RLHF workflow, our causal reward modeling provides a practical way to improve the trustworthiness and fairness of LLM finetuning.
FDPP: Fine-tune Diffusion Policy with Human Preference
This reward is then used to fine-tune the pre-trained policy with reinforcement learning (RL), resulting in alignment of pre-trained policy with new human preferences while still solving the original task.
Testing Human-Hand Segmentation on In-Distribution and Out-of-Distribution Data in Human-Robot Interactions Using a Deep Ensemble Model
This approach allowed us to account for multiple camera perspectives while also evaluating the performance of models trained on existing egocentric datasets as well as static-camera datasets.
Anytime Acceleration of Gradient Descent
This work investigates stepsize-based acceleration of gradient descent with {\em anytime} convergence guarantees.
DOGE: Towards Versatile Visual Document Grounding and Referring
In recent years, Multimodal Large Language Models (MLLMs) have increasingly emphasized grounding and referring capabilities to achieve detailed understanding and flexible user interaction.
mR$^2$AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA
Thus, multimodal Retrieval-Augmented Generation (mRAG) is naturally introduced to provide MLLMs with comprehensive and up-to-date knowledge, effectively expanding the knowledge scope.
InterFormer: Towards Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
A mutually beneficial integration of heterogeneous information is the cornerstone towards the success of CTR prediction.
Taming Rectified Flow for Inversion and Editing
To address this issue, we propose RF-Solver, a novel training-free sampler that effectively enhances inversion precision by mitigating the errors in the ODE-solving process of rectified flow.
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation
Multi-objective Bayesian optimization has been widely adopted in scientific experiment design, including drug discovery and hyperparameter optimization.
SleepNetZero: Zero-Burden Zero-Shot Reliable Sleep Staging With Neural Networks Based on Ballistocardiograms
However, reliable BCG-based sleep staging is challenging due to the limited sleep monitoring data available for BCG.
A-MFST: Adaptive Multi-Flow Sparse Tracker for Real-Time Tissue Tracking Under Occlusion
Methods: We use the Segment Anything Model2 (SAM2) to detect and mask occlusions by surgical tools, and we develop and integrate into SENDD an Adaptive Multi-Flow Sparse Tracker (A-MFST) with forward-backward consistency metrics, to enhance occlusion and uncertainty estimation.
Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality
The denoising diffusion probabilistic model (DDPM) has emerged as a mainstream generative model in generative AI.
Preference Optimization with Multi-Sample Comparisons
However, current post-training methods such as reinforcement learning from human feedback (RLHF) and direct alignment from preference methods (DAP) primarily utilize single-sample comparisons.
Stochastic Runge-Kutta Methods: Provable Acceleration of Diffusion Models
Diffusion models play a pivotal role in contemporary generative modeling, claiming state-of-the-art performance across various domains.
GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks
The scarcity of high-quality and multi-task singing datasets significantly hinders the development of diverse controllable and personalized singing tasks, as existing singing datasets suffer from low quality, limited diversity of languages and singers, absence of multi-technique information and realistic music scores, and poor task suitability.
A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models
Diffusion models, which convert noise into new data instances by learning to reverse a diffusion process, have become a cornerstone in contemporary generative modeling.
EA-VTR: Event-Aware Video-Text Retrieval
EA-VTR can efficiently encode frame-level and video-level visual representations simultaneously, enabling detailed event content and complex event temporal cross-modal alignment, ultimately enhancing the comprehensive understanding of video events.
How to Make Cross Encoder a Good Teacher for Efficient Image-Text Retrieval?
Dominant dual-encoder models enable efficient image-text retrieval but suffer from limited accuracy while the cross-encoder models offer higher accuracy at the expense of efficiency.
Language Representations Can be What Recommenders Need: Findings and Potentials
Contrary to prevailing understanding that LMs and traditional recommenders learn two distinct representation spaces due to the huge gap in language and behavior modeling objectives, this work re-examines such understanding and explores extracting a recommendation space directly from the language representation space.
Leveraging Data Mining, Active Learning, and Domain Adaptation in a Multi-Stage, Machine Learning-Driven Approach for the Efficient Discovery of Advanced Acidic Oxygen Evolution Electrocatalysts
The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst.
Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models
In this setting, a CL learner is required to incrementally learn from multiple domains and classify test images from both seen and unseen domains without any domain-identity hint.
MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention
Instead of inferring the complete human behavior characteristics, MEReQ infers a residual reward function that captures the discrepancy between the human expert's and the prior policy's underlying reward functions.
On Softmax Direct Preference Optimization for Recommendation
Specifically, we incorporate multiple negatives in user preference data and devise an alternative version of DPO loss tailored for LM-based recommenders, which is extended from the traditional full-ranking Plackett-Luce (PL) model to partial rankings and connected to softmax sampling strategies.
No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processes
This paper investigates the challenge of learning in black-box games, where the underlying utility function is unknown to any of the agents.
Horizon-Free Regret for Linear Markov Decision Processes
A recent line of works showed regret bounds in reinforcement learning (RL) can be (nearly) independent of planning horizon, a. k. a.~the horizon-free bounds.
Accelerating Convergence of Score-Based Diffusion Models, Provably
Score-based diffusion models, while achieving remarkable empirical performance, often suffer from low sampling speed, due to extensive function evaluations needed during the sampling phase.
Wukong: Towards a Scaling Law for Large-Scale Recommendation
Scaling laws play an instrumental role in the sustainable improvement in model quality.
Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large-Scale Recommendation
We study a mismatch between the deep learning recommendation models' flat architecture, common distributed training paradigm and hierarchical data center topology.
Direct Acquisition Optimization for Low-Budget Active Learning
Active Learning (AL) has gained prominence in integrating data-intensive machine learning (ML) models into domains with limited labeled data.
Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context
Many neural network architectures are known to be Turing Complete, and can thus, in principle implement arbitrary algorithms.
Optimal Multi-Distribution Learning
Focusing on a hypothesis class of Vapnik-Chervonenkis (VC) dimension d, we propose a novel algorithm that yields an varepsilon-optimal randomized hypothesis with a sample complexity on the order of (d+k)/varepsilon^2 (modulo some logarithmic factor), matching the best-known lower bound.
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle.
AutoML for Large Capacity Modeling of Meta's Ranking Systems
The large scale of models and tight production schedule requires AutoML to outperform human baselines by only using a small number of model evaluation trials (around 100).
Enhancing Instance-Level Image Classification with Set-Level Labels
Our experimental results demonstrate the effectiveness of our approach, showcasing improved classification performance compared to traditional single-instance label-based methods.
Heteroskedastic Tensor Clustering
Tensor clustering, which seeks to extract underlying cluster structures from noisy tensor observations, has gained increasing attention.
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning
Federated reinforcement learning (RL) enables collaborative decision making of multiple distributed agents without sharing local data trajectories.
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization
To address these limitations, we propose a novel approach for active learning, which aims to select batches of unlabeled instances through a learned surrogate model for data acquisition.
On Generative Agents in Recommendation
Recommender systems are the cornerstone of today's information dissemination, yet a disconnect between offline metrics and online performance greatly hinders their development.
Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints
Optimizing objectives under constraints, where both the objectives and constraints are black box functions, is a common scenario in real-world applications such as scientific experimental design, design of medical therapies, and industrial process optimization.
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization
We observe that, generally, a more diverse set of co-play agents during training enhances the generalization performance of the ego agent; however, this improvement varies across distinct scenarios and environments.
Blending Imitation and Reinforcement Learning for Robust Policy Improvement
Integral to RPI are Robust Active Policy Selection (RAPS) and Robust Policy Gradient (RPG), both of which reason over whether to perform state-wise imitation from the oracles or learn from its own value function when the learner's performance surpasses that of the oracles in a specific state.
Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints
The increasing capabilities of large language models (LLMs) raise opportunities for artificial general intelligence but concurrently amplify safety concerns, such as potential misuse of AI systems, necessitating effective AI alignment.
BitCoin: Bidirectional Tagging and Supervised Contrastive Learning based Joint Relational Triple Extraction Framework
To address these issues, we propose BitCoin, an innovative Bidirectional tagging and supervised Contrastive learning based joint relational triple extraction framework.
Settling the Sample Complexity of Online Reinforcement Learning
While a number of recent works achieved asymptotically minimal regret in online RL, the optimality of these results is only guaranteed in a ``large-sample'' regime, imposing enormous burn-in cost in order for their algorithms to operate optimally.
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
Our approach is easy to tune, and is able to focus on local region of the optimization space that can be tackled by existing BO methods.
Active Policy Improvement from Multiple Black-box Oracles
We introduce MAPS and MAPS-SE, a class of policy improvement algorithms that perform imitation learning from multiple suboptimal oracles.
Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models
Diffusion models, which convert noise into new data instances by learning to reverse a Markov diffusion process, have become a cornerstone in contemporary generative modeling.
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
Assuming access to a generative model that draws samples based on the nominal MDP, we characterize the sample complexity of RMDPs when the uncertainty set is specified via either the total variation (TV) distance or $\chi^2$ divergence.
Efficient Online Decision Tree Learning with Active Feature Acquisition
Specifically, we employ a surrogate information acquisition function based on adaptive submodularity to actively query feature values with a minimal cost, while using a posterior sampling scheme to maintain a low regret for online prediction.
Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning
This paper studies reward-agnostic exploration in reinforcement learning (RL) -- a scenario where the learner is unware of the reward functions during the exploration stage -- and designs an algorithm that improves over the state of the art.
A force-sensing surgical drill for real-time force feedback in robotic mastoidectomy
Results: The force measurements on the tip of the surgical drill are validated with raw-egg drilling experiments, where a force sensor mounted below the egg serves as ground truth.
Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA
This paper is concerned with estimating the column subspace of a low-rank matrix $\boldsymbol{X}^\star \in \mathbb{R}^{n_1\times n_2}$ from contaminated data.
Learning Human-Compatible Representations for Case-Based Decision Support
Despite the promising performance of supervised learning, representations learned by supervised models may not align well with human intuitions: what models consider as similar examples can be perceived as distinct by humans.
Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods
Efficient computation of the optimal transport distance between two distributions serves as an algorithm subroutine that empowers various applications.
Order-Prompted Tag Sequence Generation for Video Tagging
This paper proposes a novel generative model, Order-Prompted Tag Sequence Generation (OP-TSG), according to the above characteristics.
ViLEM: Visual-Language Error Modeling for Image-Text Retrieval
ViLEM then enforces the model to discriminate the correctness of each word in the plausible negative texts and further correct the wrong words via resorting to image information.
Ranked #45 on
Visual Reasoning
on Winoground
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model
This paper studies multi-agent reinforcement learning in Markov games, with the goal of learning Nash equilibria or coarse correlated equilibria (CCE) sample-optimally.
Cost-Effective Online Contextual Model Selection
In particular, we focus on the task of selecting pre-trained classifiers, and propose a contextual active model selection algorithm (CAMS), which relies on a novel uncertainty sampling query criterion defined on a given policy class for adaptive model selection.
PIC 4th Challenge: Semantic-Assisted Multi-Feature Encoding and Multi-Head Decoding for Dense Video Captioning
The task of Dense Video Captioning (DVC) aims to generate captions with timestamps for multiple events in one video.
On the Permanence of Backdoors in Evolving Models
Existing research on training-time attacks for deep neural networks (DNNs), such as backdoors, largely assume that models are static once trained, and hidden backdoors trained into models remain active indefinitely.
Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games
This paper makes progress towards learning Nash equilibria in two-player zero-sum Markov games from offline data.
Model-based Reinforcement Learning
reinforcement-learning
+1
Settling the Sample Complexity of Model-Based Offline Reinforcement Learning
We demonstrate that the model-based (or "plug-in") approach achieves minimax-optimal sample complexity without burn-in cost for tabular Markov decision processes (MDPs).
CREATE: A Benchmark for Chinese Short Video Retrieval and Title Generation
In this paper, we propose to CREATE, the first large-scale Chinese shoRt vidEo retrievAl and Title gEneration benchmark, to facilitate research and application in video titling and video retrieval in Chinese.
Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation
Open-vocabulary object detection aims to detect novel object categories beyond the training set.
Ranked #30 on
Open Vocabulary Object Detection
on MSCOCO
Learning Representation for Bayesian Optimization with Collision-free Regularization
We show that even with proper network design, such learned representation often leads to collision in the latent space: two points with significantly different observations collide in the learned latent space, leading to degraded optimization performance.
The Efficacy of Pessimism in Asynchronous Q-Learning
This paper is concerned with the asynchronous form of Q-learning, which applies a stochastic approximation scheme to Markovian data samples.
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity
Offline or batch reinforcement learning seeks to learn a near-optimal policy using history data without active exploration of the environment.
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
Overall, we hope our work serves as a starting place for researchers and engineers to design interactive explainability systems.
LRSVRG-IMC: An SVRG-Based Algorithm for LowRank Inductive Matrix Completion
Low-rank inductive matrix completion (IMC) is currently widely used in IoT data completion, recommendation systems, and so on, as the side information in IMC has demonstrated great potential in reducing sample point remains a major obstacle for the convergence of the nonconvex solutions to IMC.
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing
To scale up the computation of queries to large batches, we further propose an efficient batch-mode acquisition procedure, which aims to maximize a novel information measure defined through the acquisition function.
Teaching via Best-Case Counterexamples in the Learning-with-Equivalence-Queries Paradigm
This learning paradigm has been extensively studied when the learner receives worst-case or random counterexamples; in this paper, we consider the optimal teacher who picks best-case counterexamples to teach the target hypothesis within a hypothesis class.
Online Learning of Energy Consumption for Navigation of Electric Vehicles
In order to learn the model parameters, we develop an online learning framework and investigate several exploration strategies such as Thompson Sampling and Upper Confidence Bound.
Class-wise Thresholding for Robust Out-of-Distribution Detection
We consider the problem of detecting OoD(Out-of-Distribution) input data when using deep neural networks, and we propose a simple yet effective way to improve the robustness of several popular OoD detection methods against label shift.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Teaching an Active Learner with Contrastive Examples
Our focus is to design a teaching algorithm that can provide an informative sequence of contrastive examples to the learner to speed up the learning process.
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning
Achieving sample efficiency in online episodic reinforcement learning (RL) requires optimally balancing exploration and exploitation.
A Contract Theory based Incentive Mechanism for Federated Learning
It is challenging to design proper incentives for the FL clients due to the fact that the task is privately trained by the clients.
Inference for Heteroskedastic PCA with Missing Data
This paper studies how to construct confidence regions for principal component analysis (PCA) in high dimension, a problem that has been vastly under-explored.
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition
Graph convolutional networks (GCNs) have been widely used and achieved remarkable results in skeleton-based action recognition.
Ranked #11 on
Skeleton Based Action Recognition
on N-UCLA
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
These can often be accounted for via regularized RL, which augments the target value function with a structure-promoting regularizer.
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting
The current paper pertains to a scenario with value-based linear representation, which postulates the linear realizability of the optimal Q-function (also called the "linear $Q^{\star}$ problem").
Understanding the Effect of Bias in Deep Anomaly Detection
We also provide an extensive empirical study on how a biased training anomaly set affects the anomaly score function and therefore the detection performance on different anomaly classes.
Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities
Data-intensive science is increasingly reliant on real-time processing capabilities and machine learning workflows, in order to filter and analyze the extreme volumes of data being collected.
Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps
Eigenvector perturbation analysis plays a vital role in various data science applications.
Softmax Policy Gradient Methods Can Take Exponential Time to Converge
The softmax policy gradient (PG) method, which performs gradient ascent under softmax policy parameterization, is arguably one of the de facto implementations of policy optimization in modern reinforcement learning.
Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis
This paper addresses these questions for the synchronous setting: (1) when $|\mathcal{A}|=1$ (so that Q-learning reduces to TD learning), we prove that the sample complexity of TD learning is minimax optimal and scales as $\frac{|\mathcal{S}|}{(1-\gamma)^3\varepsilon^2}$ (up to log factor); (2) when $|\mathcal{A}|\geq 2$, we settle the sample complexity of Q-learning to be on the order of $\frac{|\mathcal{S}||\mathcal{A}|}{(1-\gamma)^4\varepsilon^2}$ (up to log factor).
Understanding Bias in Anomaly Detection: A Semi-Supervised View with PAC Guarantees
Given two different anomaly score functions, we formally define their difference in performance as the relative scoring bias of the anomaly detectors.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
+1
Learning to Make Decisions via Submodular Regularization
In particular, we focus on a class of combinatorial problems that can be solved via submodular maximization (either directly on the objective function or via submodular surrogates).
Learning Collision-free Latent Space for Bayesian Optimization
To address this issue, we propose Collision-Free Latent Space Optimization (CoFLO), which employs a novel regularizer to reduce the collision in the learned latent space and encourage the mapping from the latent space to objective value to be Lipschitz continuous.
Spectral Methods for Data Science: A Statistical Perspective
While the studies of spectral methods can be traced back to classical matrix perturbation theory and methods of moments, the past decade has witnessed tremendous theoretical advances in demystifying their efficacy through the lens of statistical modeling, with the aid of non-asymptotic random matrix theory.
The Teaching Dimension of Kernel Perceptron
As a warm-up, we show that the teaching complexity is $\Theta(d)$ for the exact teaching of linear perceptrons in $\mathbb{R}^d$, and $\Theta(d^k)$ for kernel perceptron with a polynomial kernel of order $k$.
Preference-Based Batch and Sequential Teaching
We analyze several properties of the teaching complexity parameter $TD(\sigma)$ associated with different families of the preference functions, e. g., comparison to the VC dimension of the hypothesis class and additivity/sub-additivity of $TD(\sigma)$ over disjoint domains.
Learning Mixtures of Low-Rank Models
We study the problem of learning mixtures of low-rank models, i. e. reconstructing multiple low-rank matrices from unlabelled linear measurements of each.
Using Ensemble Classifiers to Detect Incipient Anomalies
Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions.
Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution under Random Designs
We investigate the effectiveness of convex relaxation and nonconvex optimization in solving bilinear systems of equations under two different designs (i. e.$~$a sort of random Fourier design and Gaussian design).
Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization
This class of methods is often applied in conjunction with entropy regularization -- an algorithmic scheme that encourages exploration -- and is closely related to soft policy iteration and trust region policy optimization.
Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI
Ensemble learning is widely applied in Machine Learning (ML) to improve model performance and to mitigate decision risks.
Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?
Intermediate-Severity (IS) faults present milder symptoms compared to severe faults, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions.
Average-case Complexity of Teaching Convex Polytopes via Halfspace Queries
We investigate the average teaching complexity of the task, i. e., the minimal number of samples (halfspace queries) required by a teacher to help a version-space learner in locating a randomly selected target.
Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality
Furthermore, our findings unveil the statistical optimality of nonconvex tensor completion: it attains un-improvable $\ell_{2}$ accuracy -- including both the rates and the pre-constants -- when estimating both the unknown tensor and the underlying tensor factors.
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction
Focusing on a $\gamma$-discounted MDP with state space $\mathcal{S}$ and action space $\mathcal{A}$, we demonstrate that the $\ell_{\infty}$-based sample complexity of classical asynchronous Q-learning --- namely, the number of samples needed to yield an entrywise $\varepsilon$-accurate estimate of the Q-function --- is at most on the order of $\frac{1}{\mu_{\min}(1-\gamma)^5\varepsilon^2}+ \frac{t_{mix}}{\mu_{\min}(1-\gamma)}$ up to some logarithmic factor, provided that a proper constant learning rate is adopted.
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
This paper is concerned with the sample efficiency of reinforcement learning, assuming access to a generative model (or simulator).
Understanding the Power and Limitations of Teaching with Imperfect Knowledge
Machine teaching studies the interaction between a teacher and a student/learner where the teacher selects training examples for the learner to learn a specific task.
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles
In order to learn the model parameters, we develop an online learning framework and investigate several exploration strategies such as Thompson Sampling and Upper Confidence Bound.
A Financial Service Chatbot based on Deep Bidirectional Transformers
Our main novel contribution is the discussion about uncertainty measure for BERT, where three different approaches are systematically compared on real problems.
Adaptive Teaching of Temporal Logic Formulas to Learners with Preferences
In the context of teaching temporal logic formulas, an exhaustive search even for a myopic solution takes exponential time (with respect to the time span of the task).
Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data
This paper delivers improved theoretical guarantees for the convex programming approach in low-rank matrix estimation, in the presence of (1) random noise, (2) gross sparse outliers, and (3) missing data.
Tackling small eigen-gaps: Fine-grained eigenvector estimation and inference under heteroscedastic noise
This paper aims to address two fundamental challenges arising in eigenvector estimation and inference for a low-rank matrix from noisy observations: (1) how to estimate an unknown eigenvector when the eigen-gap (i. e. the spacing between the associated eigenvalue and the rest of the spectrum) is particularly small; (2) how to perform estimation and inference on linear functionals of an eigenvector -- a sort of "fine-grained" statistical reasoning that goes far beyond the usual $\ell_2$ analysis.
Nonconvex Low-Rank Tensor Completion from Noisy Data
We study a noisy tensor completion problem of broad practical interest, namely, the reconstruction of a low-rank tensor from highly incomplete and randomly corrupted observations of its entries.
Landmark Ordinal Embedding
In this paper, we aim to learn a low-dimensional Euclidean representation from a set of constraints of the form "item j is closer to item i than item k".
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models
In our framework, each function $\sigma \in \Sigma$ induces a teacher-learner pair with teaching complexity as $\TD(\sigma)$.
Subspace Estimation from Unbalanced and Incomplete Data Matrices: $\ell_{2,\infty}$ Statistical Guarantees
This paper is concerned with estimating the column space of an unknown low-rank matrix $\boldsymbol{A}^{\star}\in\mathbb{R}^{d_{1}\times d_{2}}$, given noisy and partial observations of its entries.
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction
There is growing interest in large-scale machine learning and optimization over decentralized networks, e. g. in the context of multi-agent learning and federated learning.
Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults
The Monte Carlo dropout method has proved to be a scalable and easy-to-use approach for estimating the uncertainty of deep neural network predictions.
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
We show that the encoder-decoder model is able to identify the injected anomalies in a modern manufacturing process in an unsupervised fashion.
Inference and Uncertainty Quantification for Noisy Matrix Completion
As a byproduct, we obtain a sharp characterization of the estimation accuracy of our de-biased estimators, which, to the best of our knowledge, are the first tractable algorithms that provably achieve full statistical efficiency (including the preconstant).
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
Importantly, we show that our objective function can be efficiently decomposed as a difference of submodular functions (DS), which allows us to employ DS optimization tools to greedily identify sets of constraints that increase the likelihood of finding items with high utility.
AED-Net: An Abnormal Event Detection Network
It is challenging to detect the anomaly in crowded scenes for quite a long time.
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
This paper studies noisy low-rank matrix completion: given partial and noisy entries of a large low-rank matrix, the goal is to estimate the underlying matrix faithfully and efficiently.
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status.
Trip Prediction by Leveraging Trip Histories from Neighboring Users
We propose a novel approach for trip prediction by analyzing user's trip histories.
Asymmetry Helps: Eigenvalue and Eigenvector Analyses of Asymmetrically Perturbed Low-Rank Matrices
The aim is to estimate the leading eigenvalue and eigenvector of $\mathbf{M}^{\star}$.
Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach.
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes
How can we efficiently gather information to optimize an unknown function, when presented with multiple, mutually dependent information sources with different costs?
Pixel Level Data Augmentation for Semantic Image Segmentation using Generative Adversarial Networks
Semantic segmentation is one of the basic topics in computer vision, it aims to assign semantic labels to every pixel of an image.
Et Tu Alexa? When Commodity WiFi Devices Turn into Adversarial Motion Sensors
Our work demonstrates a new set of silent reconnaissance attacks, which leverages the presence of commodity WiFi devices to track users inside private homes and offices, without compromising any WiFi network, data packets, or devices.
Cryptography and Security
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview
Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix factorization via nonconvex optimization.
Addressing Training Bias via Automated Image Annotation
Build accurate DNN models requires training on large labeled, context specific datasets, especially those matching the target scenario.
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion
Focusing on two statistical estimation problems, i. e. solving random quadratic systems of equations and low-rank matrix completion, we establish that gradient descent achieves near-optimal statistical and computational guarantees without explicit regularization.
Teaching Multiple Concepts to a Forgetful Learner
Our framework is both generic, allowing the design of teaching schedules for different memory models, and also interactive, allowing the teacher to adapt the schedule to the underlying forgetting mechanisms of the learner.
Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval
This paper considers the problem of solving systems of quadratic equations, namely, recovering an object of interest $\mathbf{x}^{\natural}\in\mathbb{R}^{n}$ from $m$ quadratic equations/samples $y_{i}=(\mathbf{a}_{i}^{\top}\mathbf{x}^{\natural})^{2}$, $1\leq i\leq m$.
Teaching Categories to Human Learners with Visual Explanations
We study the problem of computer-assisted teaching with explanations.
Nonconvex Matrix Factorization from Rank-One Measurements
We consider the problem of recovering low-rank matrices from random rank-one measurements, which spans numerous applications including covariance sketching, phase retrieval, quantum state tomography, and learning shallow polynomial neural networks, among others.
Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
We highlight that adaptivity does not speed up the teaching process when considering existing models of version space learners, such as "worst-case" (the learner picks the next hypothesis randomly from the version space) and "preference-based" (the learner picks hypothesis according to some global preference).
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution
Recent years have seen a flurry of activities in designing provably efficient nonconvex procedures for solving statistical estimation problems.
Spectral Method and Regularized MLE Are Both Optimal for Top-$K$ Ranking
This paper is concerned with the problem of top-$K$ ranking from pairwise comparisons.
The Likelihood Ratio Test in High-Dimensional Logistic Regression Is Asymptotically a Rescaled Chi-Square
When used for the purpose of statistical inference, logistic models produce p-values for the regression coefficients by using an approximation to the distribution of the likelihood-ratio test.
Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting
We consider the optimal value of information (VoI) problem, where the goal is to sequentially select a set of tests with a minimal cost, so that one can efficiently make the best decision based on the observed outcomes.
The Projected Power Method: An Efficient Algorithm for Joint Alignment from Pairwise Differences
We prove that for a broad class of statistical models, the proposed projected power method makes no error---and hence converges to the maximum likelihood estimate---in a suitable regime.
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests
We consider the Bayesian active learning and experimental design problem, where the goal is to learn the value of some unknown target variable through a sequence of informative, noisy tests.
Community Recovery in Graphs with Locality
Motivated by applications in domains such as social networks and computational biology, we study the problem of community recovery in graphs with locality.
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems
We complement our theoretical study with numerical examples showing that solving random quadratic systems is both computationally and statistically not much harder than solving linear systems of the same size---hence the title of this paper.
Spectral MLE: Top-$K$ Rank Aggregation from Pairwise Comparisons
To approach this minimax limit, we propose a nearly linear-time ranking scheme, called \emph{Spectral MLE}, that returns the indices of the top-$K$ items in accordance to a careful score estimate.
Information Recovery from Pairwise Measurements
In particular, our results isolate a family of \emph{minimum} \emph{channel divergence measures} to characterize the degree of measurement corruption, which together with the size of the minimum cut of $\mathcal{G}$ dictates the feasibility of exact information recovery.
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation
Maximum a posteriori (MAP) inference over discrete Markov random fields is a fundamental task spanning a wide spectrum of real-world applications, which is known to be NP-hard for general graphs.
Near Optimal Bayesian Active Learning for Decision Making
Instead of minimizing uncertainty per se, we consider a set of overlapping decision regions of these hypotheses.
Near-Optimal Joint Object Matching via Convex Relaxation
Joint matching over a collection of objects aims at aggregating information from a large collection of similar instances (e. g. images, graphs, shapes) to improve maps between pairs of them.
Exact and Stable Covariance Estimation from Quadratic Sampling via Convex Programming
Our method admits universally accurate covariance estimation in the absence of noise, as soon as the number of measurements exceeds the information theoretic limits.
Robust Spectral Compressed Sensing via Structured Matrix Completion
The paper explores the problem of \emph{spectral compressed sensing}, which aims to recover a spectrally sparse signal from a small random subset of its $n$ time domain samples.
Spectral Compressed Sensing via Structured Matrix Completion
The paper studies the problem of recovering a spectrally sparse object from a small number of time domain samples.
