Search Results for author:
Found 22 papers, 6 papers with code
Gradient-based Sampling for Class Imbalanced Semi-supervised Object Detection
To tackle the confirmation bias from incorrect pseudo labels of minority classes, the class-rebalancing sampling module resamples unlabeled data following the guidance of the gradient-based reweighting module.
Navigating Complexity: Constrained Portfolio Analysis in High Dimensions with Tracking Error and Weight Constraints
This paper analyzes the statistical properties of constrained portfolio formation in a high dimensional portfolio with a large number of assets.
Wakening Past Concepts without Past Data: Class-Incremental Learning from Online Placebos
In experiments, we show that our method 1) is surprisingly effective even when there is no class overlap between placebos and original old class data, 2) does not require any additional supervision or memory budget, and 3) significantly outperforms a number of top-performing CIL methods, in particular when using lower memory budgets for old class exemplars, e. g., five exemplars per class.
Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysis
Further, this result is applied to robust adaptive model predictive control with uncertainty sets updated by set membership.
Non-asymptotic System Identification for Linear Systems with Nonlinear Policies
This paper considers a single-trajectory system identification problem for linear systems under general nonlinear and/or time-varying policies with i. i. d.
Online Hyperparameter Optimization for Class-Incremental Learning
Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase.
Mining the Factor Zoo: Estimation of Latent Factor Models with Sufficient Proxies
Latent factor model estimation typically relies on either using domain knowledge to manually pick several observed covariates as factor proxies, or purely conducting multivariate analysis such as principal component analysis.
Tele-Knowledge Pre-training for Fault Analysis
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task
On the other hand, the data captured from roadside cameras have strengths over frontal-view data, which is believed to facilitate a safer and more intelligent autonomous driving system.
Statistical Learning for Individualized Asset Allocation
We show that our proposed Discretization and Regression with generalized fOlded concaVe penalty on Effect discontinuity (DROVE) approach enjoys desirable theoretical properties and allows for statistical inference of the optimal value associated with optimal decision-making.
Rope3D: The Roadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task
On the other hand, the data captured from roadside cameras have strengths over frontal-view data, which is believed to facilitate a safer and more intelligent autonomous driving system.
Safe Adaptive Learning-based Control for Constrained Linear Quadratic Regulators with Regret Guarantees
We study the adaptive control of an unknown linear system with a quadratic cost function subject to safety constraints on both the states and actions.
Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video
In this paper, we introduce a novel task, referred to as Weakly-Supervised Spatio-Temporal Anomaly Detection (WSSTAD) in surveillance video.
DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning
Long-range and short-range temporal modeling are two complementary and crucial aspects of video recognition.
Ranked #6 on
Action Recognition
on ActivityNet
Good Practices and A Strong Baseline for Traffic Anomaly Detection
In this paper, we propose a straightforward and efficient framework that includes pre-processing, a dynamic track module, and post-processing.
Recent Advances of Generic Object Detection with Deep Learning: A Review
Object detection is an important and challenging problem in computer vision.
Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms
To address this question, we introduce a gradient-based online algorithm, Receding Horizon Inexact Gradient (RHIG), and analyze its performance by dynamic regrets in terms of the temporal variation of the environment and the prediction errors.
Coherent Loss: A Generic Framework for Stable Video Segmentation
Video segmentation approaches are of great importance for numerous vision tasks especially in video manipulation for entertainment.
Online Learning and Distributed Control for Residential Demand Response
This paper studies the automated control method for regulating air conditioner (AC) loads in incentive-based residential demand response (DR).
Online Optimal Control with Affine Constraints
We show that OGD-BZ can achieve a policy regret upper bound that is the square root of the horizon length multiplied by some logarithmic terms of the horizon length under proper algorithm parameters.
A Reliability-aware Multi-armed Bandit Approach to Learn and Select Users in Demand Response
One challenge in the optimization and control of societal systems is to handle the unknown and uncertain user behavior.
