Search Results for author:
Found 45 papers, 9 papers with code
HoloVIC: Large-scale Dataset and Benchmark for Multi-Sensor Holographic Intersection and Vehicle-Infrastructure Cooperative
Based on HoloVIC, we formulated four tasks to facilitate the development of related research.
Batched Nonparametric Contextual Bandits
We study nonparametric contextual bandits under batch constraints, where the expected reward for each action is modeled as a smooth function of covariates, and the policy updates are made at the end of each batch of observations.
Top-$K$ ranking with a monotone adversary
On the analytical front, we provide a refined $\ell_\infty$ error analysis of the weighted MLE that is more explicit and tighter than existing analyses.
On the design-dependent suboptimality of the Lasso
This paper investigates the effect of the design matrix on the ability (or inability) to estimate a sparse parameter in linear regression.
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift
This paper addresses this fundamental question by proving that, surprisingly, classical Maximum Likelihood Estimation (MLE) purely using source data (without any modification) achieves the minimax optimality for covariate shift under the well-specified setting.
Provably Accelerating Ill-Conditioned Low-rank Estimation via Scaled Gradient Descent, Even with Overparameterization
Many problems encountered in science and engineering can be formulated as estimating a low-rank object (e. g., matrices and tensors) from incomplete, and possibly corrupted, linear measurements.
Unraveling Projection Heads in Contrastive Learning: Insights from Expansion and Shrinkage
Firstly, through empirical and theoretical analysis, we identify two crucial effects -- expansion and shrinkage -- induced by the contrastive loss on the projectors.
Sharp high-probability sample complexities for policy evaluation with linear function approximation
This paper is concerned with the problem of policy evaluation with linear function approximation in discounted infinite horizon Markov decision processes.
Multi-Teacher Knowledge Distillation For Text Image Machine Translation
Text image machine translation (TIMT) has been widely used in various real-world applications, which translates source language texts in images into another target language sentence.
E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation
Furthermore, the ablation studies verify the generalization of our method, where the proposed modal adapter is effective to bridge various OCR and MT models.
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
We propose $\textsf{ScaledGD($\lambda$)}$, a preconditioned gradient descent method to tackle the low-rank matrix sensing problem when the true rank is unknown, and when the matrix is possibly ill-conditioned.
Improving End-to-End Text Image Translation From the Auxiliary Text Translation Task
End-to-end text image translation (TIT), which aims at translating the source language embedded in images to the target language, has attracted intensive attention in recent research.
$O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games
We prove that optimistic-follow-the-regularized-leader (OFTRL), together with smooth value updates, finds an $O(T^{-1})$-approximate Nash equilibrium in $T$ iterations for two-player zero-sum Markov games with full information.
Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent
An increasing number of data science and machine learning problems rely on computation with tensors, which better capture the multi-way relationships and interactions of data than matrices.
Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets
We present a family $\{\hat{\pi}\}_{p\ge 1}$ of pessimistic learning rules for offline learning of linear contextual bandits, relying on confidence sets with respect to different $\ell_p$ norms, where $\hat{\pi}_2$ corresponds to Bellman-consistent pessimism (BCP), while $\hat{\pi}_\infty$ is a novel generalization of lower confidence bound (LCB) to the linear setting.
Optimally tackling covariate shift in RKHS-based nonparametric regression
We study the covariate shift problem in the context of nonparametric regression over a reproducing kernel Hilbert space (RKHS).
Jump-Start Reinforcement Learning
In addition, we provide an upper bound on the sample complexity of JSRL and show that with the help of a guide-policy, one can improve the sample complexity for non-optimism exploration methods from exponential in horizon to polynomial.
A new similarity measure for covariate shift with applications to nonparametric regression
We study covariate shift in the context of nonparametric regression.
BBA-net: A bi-branch attention network for crowd counting
Our method can integrate the pedestrian's head and body information to enhance the feature expression ability of the density map.
TANet++: Triple Attention Network with Filtered Pointcloud on 3D Detection
TANet is one of state-of-the-art 3D object detection method on KITTI and JRDB benchmark, the network contains a Triple Attention module and Coarse-to-Fine Regression module to improve the robustness and accuracy of 3D Detection.
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements
Tensors, which provide a powerful and flexible model for representing multi-attribute data and multi-way interactions, play an indispensable role in modern data science across various fields in science and engineering.
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
Based on the composition of the offline dataset, two main categories of methods are used: imitation learning which is suitable for expert datasets and vanilla offline RL which often requires uniform coverage datasets.
Minimax Off-Policy Evaluation for Multi-Armed Bandits
Second, when the behavior policy is unknown, we analyze performance in terms of the competitive ratio, thereby revealing a fundamental gap between the settings of known and unknown behavior policies.
Beyond Procrustes: Balancing-Free Gradient Descent for Asymmetric Low-Rank Matrix Sensing
Low-rank matrix estimation plays a central role in various applications across science and engineering.
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.
Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number
Many problems in data science can be treated as estimating a low-rank matrix from highly incomplete, sometimes even corrupted, observations.
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.
CASIA's System for IWSLT 2020 Open Domain Translation
This paper describes the CASIA{'}s system for the IWSLT 2020 open domain translation task.
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent
Low-rank matrix estimation is a canonical problem that finds numerous applications in signal processing, machine learning and imaging science.
Microwave Photonic Imaging Radar with a Millimeter-level Resolution
Microwave photonic radars enable fast or even real-time high-resolution imaging thanks to its broad bandwidth.
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.
Multi-wavelength properties of radio and machine-learning identified counterparts to submillimeter sources in S2COSMOS
We identify multi-wavelength counterparts to 1, 147 submillimeter sources from the S2COSMOS SCUBA-2 survey of the COSMOS field by employing a recently developed radio$+$machine-learning method trained on a large sample of ALMA-identified submillimeter galaxies (SMGs), including 260 SMGs identified in the AS2COSMOS pilot survey.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
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).
A Selective Overview of Deep Learning
Deep learning has arguably achieved tremendous success in recent years.
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.
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.
Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking
In this paper, we propose a novel tracklet processing method to cleave and re-connect tracklets on crowd or long-term occlusion by Siamese Bi-Gated Recurrent Unit (GRU).
Ranked #20 on
Multi-Object Tracking
on MOT16
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 $y_{i}=(\mathbf{a}_{i}^{\top}\mathbf{x}^{\natural})^{2}$, $1\leq i\leq m$.
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.
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.
Inter-Subject Analysis: Inferring Sparse Interactions with Dense Intra-Graphs
Our framework is based on the Gaussian graphical models, under which ISA can be converted to the problem of estimation and inference of the inter-subject precision matrix.
Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video
In this work, we propose an extractive Multi-modal Summarization (MMS) method which can automatically generate a textual summary given a set of documents, images, audios and videos related to a specific topic.
Automatic Speech Recognition (ASR)
Document Summarization
+1
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.
Application of Bayesian graphs to SN Ia data analysis and compression
Overall, the results of our analysis emphasize the need for a fully consistent Bayesian statistical approach in the analysis of future large SN Ia data sets.
Cosmology and Nongalactic Astrophysics
Panther: Fast Top-k Similarity Search in Large Networks
The algorithm is based on a novel idea of random path, and an extended method is also presented, to enhance the structural similarity when two vertices are completely disconnected.
Social and Information Networks
