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Found 37 papers, 15 papers with code
Automated Contrastive Learning Strategy Search for Time Series
In recent years, Contrastive Learning (CL) has become a predominant representation learning paradigm for time series.
CASPER: Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation
Based on the results of the frontdoor adjustment, we introduce a novel Causality-Aware SPatiotEmpoRal graph neural network (CASPER), which contains a novel Spatiotemporal Causal Attention (SCA) and a Prompt Based Decoder (PBD).
Benchmarking Data Science Agents
In this paper, we introduce DSEval -- a novel evaluation paradigm, as well as a series of innovative benchmarks tailored for assessing the performance of these agents throughout the entire data science lifecycle.
ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
A wide range of experiments on both synthetic and real-world datasets have illustrated the superior modeling capacities and prediction performance of ContiFormer on irregular time series data.
EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model
Self-supervised learning has emerged as a highly effective approach in the fields of natural language processing and computer vision.
TaskBench: Benchmarking Large Language Models for Task Automation
To this end, we introduce TaskBench to evaluate the capability of LLMs in task automation.
Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals
Seeing is believing, however, the underlying mechanism of how human visual perceptions are intertwined with our cognitions is still a mystery.
Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance
Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets.
Is Risk-Sensitive Reinforcement Learning Properly Resolved?
Due to the nature of risk management in learning applicable policies, risk-sensitive reinforcement learning (RSRL) has been realized as an important direction.
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling
A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU).
SIMPLE: Specialized Model-Sample Matching for Domain Generalization
Moreover, our method is highly efficient and achieves more than 1000 times training speedup compared to the conventional DG methods with fine-tuning a pretrained model.
Ranked #1 on
Domain Generalization
on PACS
MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
In contrast, though human engineers have the incredible ability to understand tasks and reason about solutions, their experience and knowledge are often sparse and difficult to utilize by quantitative approaches.
AutoTaskFormer: Searching Vision Transformers for Multi-task Learning
Vision Transformers have shown great performance in single tasks such as classification and segmentation.
Towards Inference Efficient Deep Ensemble Learning
Ensemble methods can deliver surprising performance gains but also bring significantly higher computational costs, e. g., can be up to 2048X in large-scale ensemble tasks.
Reinforcement Learning with Automated Auxiliary Loss Search
A good state representation is crucial to solving complicated reinforcement learning (RL) challenges.
Bootstrapped Transformer for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims at learning policies from previously collected static trajectory data without interacting with the real environment.
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble
Considering the great performance of ensemble methods on both accuracy and generalization in supervised learning (SL), we design a robust and applicable method named Ensemble Proximal Policy Optimization (EPPO), which learns ensemble policies in an end-to-end manner.
Domain Generalization using Pretrained Models without Fine-tuning
Fine-tuning pretrained models is a common practice in domain generalization (DG) tasks.
Ranked #8 on
Domain Generalization
on TerraIncognita
Towards Generating Real-World Time Series Data
In this paper, we propose a novel generative framework for RTS data - RTSGAN to tackle the aforementioned challenges.
AARL: Automated Auxiliary Loss for Reinforcement Learning
A good state representation is crucial to reinforcement learning (RL) while an ideal representation is hard to learn only with signals from the RL objective.
Deep Ensemble Policy Learning
Ensemble learning, which can consistently improve the prediction performance in supervised learning, has drawn increasing attentions in reinforcement learning (RL).
SANE: Specialization-Aware Neural Network Ensemble
Real-world data is often generated by some complex distribution, which can be approximated by a composition of multiple simpler distributions.
Universal Trading for Order Execution with Oracle Policy Distillation
As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument.
Infrared target tracking based on proximal robust principal component analysis method
Infrared target tracking plays an important role in both civil and military fields.
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences.
A Deep Recurrent Survival Model for Unbiased Ranking
Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data.
Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling
Various sequential recommendation methods are proposed to model the dynamic user behaviors.
Deep Landscape Forecasting for Real-time Bidding Advertising
The problem is formulated as to forecast the probability distribution of market price for each ad auction.
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user.
Guiding the One-to-one Mapping in CycleGAN via Optimal Transport
CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples, achieving the task of unsupervised data translation.
Deep Recurrent Survival Analysis
By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i. e., the probability of the non-occurrence of the event, for the censored data.
Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising
To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation.
Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising
From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes.
Activation Maximization Generative Adversarial Nets
Our proposed model also outperforms the baseline methods in the new metric.
Real-Time Bidding by Reinforcement Learning in Display Advertising
In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set.
Product-based Neural Networks for User Response Prediction
Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.
Ranked #1 on
Click-Through Rate Prediction
on iPinYou
A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia
We present a question answering system over DBpedia, filling the gap between user information needs expressed in natural language and a structured query interface expressed in SPARQL over the underlying knowledge base (KB).
