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Found 175 papers, 54 papers with code
Efficient Human-AI Coordination via Preparatory Language-based Convention
Developing intelligent agents capable of seamless coordination with humans is a critical step towards achieving artificial general intelligence.
ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models
This is due to the computational overhead of the value model, which does not exist in ReMax.
AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
Specifically, we adopt a multiple pairwise ranking loss which trains the user model to capture the similarity orders between the implicitly augmented view, the explicitly augmented view, and views from other users.
Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning
MOREC learns a generalizable dynamics reward function from offline data, which is subsequently employed as a transition filter in any offline MBRL method: when generating transitions, the dynamics model generates a batch of transitions and selects the one with the highest dynamics reward value.
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments
In this work, we focus on imitator learning based on only one expert demonstration.
Improve the efficiency of deep reinforcement learning through semantic exploration guided by natural language
We assume that the interaction can be modeled as a sequence of templated questions and answers, and that there is a large corpus of previous interactions available.
ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning
Third, we train an Advantage-Conditioned Transformer (ACT) to generate actions conditioned on the estimated advantages.
Hide and Seek (HaS): A Lightweight Framework for Prompt Privacy Protection
Numerous companies have started offering services based on large language models (LLM), such as ChatGPT, which inevitably raises privacy concerns as users' prompts are exposed to the model provider.
Sensiverse: A dataset for ISAC study
In order to address the lack of applicable channel models for ISAC research and evaluation, we release Sensiverse, a dataset that can be used for ISAC research.
Synergistic Signal Denoising for Multimodal Time Series of Structure Vibration
Structural Health Monitoring (SHM) plays an indispensable role in ensuring the longevity and safety of infrastructure.
Transformer-Based Denoising of Mechanical Vibration Signals
Mechanical vibration signal denoising is a pivotal task in various industrial applications, including system health monitoring and failure prediction.
Disentangling Multi-view Representations Beyond Inductive Bias
In this paper, we propose a novel multi-view representation disentangling method that aims to go beyond inductive biases, ensuring both interpretability and generalizability of the resulting representations.
Car-Studio: Learning Car Radiance Fields from Single-View and Endless In-the-wild Images
However, previous studies learned within a sequence of autonomous driving datasets, resulting in unsatisfactory blurring when rotating the car in the simulator.
Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning
MOBILE conducts uncertainty quantification through the inconsistency of Bellman estimations under an ensemble of learned dynamics models, which can be a better approximator to the true Bellman error, and penalizes the Bellman estimation based on this uncertainty.
A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges
Reaction and retrosynthesis prediction are fundamental tasks in computational chemistry that have recently garnered attention from both the machine learning and drug discovery communities.
SplatFlow: Learning Multi-frame Optical Flow via Splatting
Occlusion problem remains a key challenge in Optical Flow Estimation (OFE) despite the recent significant progress brought by deep learning in the field.
NPVForensics: Jointing Non-critical Phonemes and Visemes for Deepfake Detection
Next, we design a phoneme-viseme awareness module for cross-modal feature fusion and representation alignment, so that the modality gap can be reduced and the intrinsic complementarity of the two modalities can be better explored.
Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
A common taxonomy of existing offline RL works is policy regularization, which typically constrains the learned policy by distribution or support of the behavior policy.
Provably Efficient Adversarial Imitation Learning with Unknown Transitions
Adversarial imitation learning (AIL), a subset of IL methods, is particularly promising, but its theoretical foundation in the presence of unknown transitions has yet to be fully developed.
Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction
However, the current non-autoregressive decoder does not satisfy two essential rules of electron redistribution modeling simultaneously: the electron-counting rule and the symmetry rule.
Language Model Self-improvement by Reinforcement Learning Contemplation
We demonstrate that SIRLC can be applied to various NLP tasks, such as reasoning problems, text generation, and machine translation.
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers
Concretely, to avoid the ego-system overfitting to a specific attacker, we maintain a set of attackers, which is optimized to guarantee the attackers high attacking quality and behavior diversity.
Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation
In specific, we introduce a novel message-attacking approach that models the learning of the auxiliary attacker as a cooperative problem under a shared goal to minimize the coordination ability of the ego system, with which every information channel may suffer from distinct message attacks.
Multi-agent Continual Coordination via Progressive Task Contextualization
Towards tackling the mentioned issue, this paper proposes an approach Multi-Agent Continual Coordination via Progressive Task Contextualization, dubbed MACPro.
Robust Multi-agent Communication via Multi-view Message Certification
Many multi-agent scenarios require message sharing among agents to promote coordination, hastening the robustness of multi-agent communication when policies are deployed in a message perturbation environment.
Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems
However, building a user simulator with no reality-gap, i. e., can predict user's feedback exactly, is unrealistic because the users' reaction patterns are complex and historical logs for each user are limited, which might mislead the simulator-based recommendation policy.
Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning
Open-set semi-supervised learning (Open-set SSL) considers a more practical scenario, where unlabeled data and test data contain new categories (outliers) not observed in labeled data (inliers).
Beware of Instantaneous Dependence in Reinforcement Learning
Playing an important role in Model-Based Reinforcement Learning (MBRL), environment models aim to predict future states based on the past.
Model-based Reinforcement Learning
reinforcement-learning
+1
How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement
In this paper, we propose a novel active imitation learning framework based on a teacher-student interaction model, in which the teacher's goal is to identify the best teaching behavior and actively affect the student's learning process.
Uncertainty Estimation by Fisher Information-based Evidential Deep Learning
To address this problem, we propose a novel method, Fisher Information-based Evidential Deep Learning ($\mathcal{I}$-EDL).
Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning
We also release the built offline benchmarks in this paper as a testbed for communication ability validation to facilitate further future research.
Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation
To ease the learning burden of the policy, we investigate an inside-out scheme for natural language-conditioned RL by developing a task language (TL) that is task-related and unique.
Theoretical Analysis of Offline Imitation With Supplementary Dataset
This paper considers a situation where, besides the small amount of expert data, a supplementary dataset is available, which can be collected cheaply from sub-optimal policies.
A Clustering-guided Contrastive Fusion for Multi-view Representation Learning
To this end, we utilize a deep fusion network to fuse view-specific representations into the view-common representation, extracting high-level semantics for obtaining robust representation.
Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense
Experiments on two public datasets show that ClusterAttack can effectively degrade the performance of FedRec systems while circumventing many defense methods, and UNION can improve the resistance of the system against various untargeted attacks, including our ClusterAttack.
Momentum Calibration for Text Generation
The input and output of most text generation tasks can be transformed to two sequences of tokens and they can be modeled using sequence-to-sequence learning modeling tools such as Transformers.
Ranked #2 on
Text Summarization
on SAMSum Corpus
Learning Physically Realizable Skills for Online Packing of General 3D Shapes
We study the problem of learning online packing skills for irregular 3D shapes, which is arguably the most challenging setting of bin packing problems.
Real-time Blind Deblurring Based on Lightweight Deep-Wiener-Network
In this paper, we address the problem of blind deblurring with high efficiency.
Does Debiasing Inevitably Degrade the Model Performance
We use our theoretical framework to explain why the current debiasing methods cause performance degradation.
Knowledge is Power: Understanding Causality Makes Legal judgment Prediction Models More Generalizable and Robust
To validate our theoretical analysis, we further propose another method using our proposed Causality-Aware Self-Attention Mechanism (CASAM) to guide the model to learn the underlying causality knowledge in legal texts.
Semantic Structure Enhanced Contrastive Adversarial Hash Network for Cross-media Representation Learning
Secondly, for further improving learning ability of implicit cross-media semantic associations, a semantic label association graph is constructed, and the graph convolutional network is utilized to mine the implicit semantic structures, thus guiding learning of discriminative features of different modalities.
Domain generalization Person Re-identification on Attention-aware multi-operation strategery
Domain generalization person re-identification (DG Re-ID) aims to directly deploy a model trained on the source domain to the unseen target domain with good generalization, which is a challenging problem and has practical value in a real-world deployment.
Multi-agent Dynamic Algorithm Configuration
MA-DAC formulates the dynamic configuration of a complex algorithm with multiple types of hyperparameters as a contextual multi-agent Markov decision process and solves it by a cooperative multi-agent RL (MARL) algorithm.
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems
In this paper, we present a general framework for benchmarking the degree of manipulations of recommendation algorithms, in both slate recommendation and sequential recommendation scenarios.
MARS: A Motif-based Autoregressive Model for Retrosynthesis Prediction
In this paper, we propose a novel end-to-end graph generation model for retrosynthesis prediction, which sequentially identifies the reaction center, generates the synthons, and adds motifs to the synthons to generate reactants.
On Efficient Reinforcement Learning for Full-length Game of StarCraft II
In this work, we investigate a set of RL techniques for the full-length game of StarCraft II.
WeLM: A Well-Read Pre-trained Language Model for Chinese
Large Language Models pre-trained with self-supervised learning have demonstrated impressive zero-shot generalization capabilities on a wide spectrum of tasks.
ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery
The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD).
Model-based Reinforcement Learning with Multi-step Plan Value Estimation
The new model-based reinforcement learning algorithm MPPVE (Model-based Planning Policy Learning with Multi-step Plan Value Estimation) shows a better utilization of the learned model and achieves a better sample efficiency than state-of-the-art model-based RL approaches.
Model-based Reinforcement Learning
reinforcement-learning
+1
Deep Anomaly Detection and Search via Reinforcement Learning
Semi-supervised Anomaly Detection (AD) is a kind of data mining task which aims at learning features from partially-labeled datasets to help detect outliers.
MORI-RAN: Multi-view Robust Representation Learning via Hybrid Contrastive Fusion
To this end, in this paper, we proposed a hybrid contrastive fusion algorithm to extract robust view-common representation from unlabeled data.
Convolutional Neural Networks with A Topographic Representation Module for EEG-Based Brain-Computer Interfaces
Significance: We improve the classification performance of 3 CNNs on 2 datasets by the use of TRM, indicating that it has the capability to mine the EEG spatial topological information.
Unified Policy Optimization for Continuous-action Reinforcement Learning in Non-stationary Tasks and Games
This paper addresses policy learning in non-stationary environments and games with continuous actions.
Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution
Experimental results on a collaborative cooking task show the necessity of considering the heterogeneous setting and illustrate that our proposed method is a promising solution for heterogeneous cooperative MARL.
Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis
Imitation learning learns a policy from expert trajectories.
Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations
Concretely, for trusty representation learning, we propose to incorporate pseudo labels to instruct the pair selection, obtaining more reliable representation pairs for pixel contrast.
A Survey on Model-based Reinforcement Learning
In this survey, we take a review of MBRL with a focus on the recent progress in deep RL.
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning
Value function estimation is an indispensable subroutine in reinforcement learning, which becomes more challenging in the offline setting.
Offline Reinforcement Learning with Causal Structured World Models
Model-based methods have recently shown promising for offline reinforcement learning (RL), aiming to learn good policies from historical data without interacting with the environment.
Transferable Reward Learning by Dynamics-Agnostic Discriminator Ensemble
Empirical results compared with the state-of-the-art AIL methods show that DARL can learn a reward that is more consistent with the true reward, thus obtaining higher environment returns.
Model Generation with Provable Coverability for Offline Reinforcement Learning
Model-based offline optimization with dynamics-aware policy provides a new perspective for policy learning and out-of-distribution generalization, where the learned policy could adapt to different dynamics enumerated at the training stage.
Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation
Automatic surgical scene segmentation is fundamental for facilitating cognitive intelligence in the modern operating theatre.
Enhancing Neural Mathematical Reasoning by Abductive Combination with Symbolic Library
On a mathematical reasoning dataset, we adopt the recently proposed abductive learning framework, and propose the ABL-Sym algorithm that combines the Transformer neural models with a symbolic mathematics library.
A Note on Target Q-learning For Solving Finite MDPs with A Generative Oracle
In particular, we demonstrate that the sample complexity of the target Q-learning algorithm in [Lee and He, 2020] is $\widetilde{\mathcal O}(|\mathcal S|^2|\mathcal A|^2 (1-\gamma)^{-5}\varepsilon^{-2})$.
Multi-Agent Policy Transfer via Task Relationship Modeling
We demonstrate that the task representation can capture the relationship among tasks, and can generalize to unseen tasks.
Attentive Temporal Pooling for Conformer-based Streaming Language Identification in Long-form Speech
In this paper, we introduce a novel language identification system based on conformer layers.
Rethinking ValueDice: Does It Really Improve Performance?
First, we show that ValueDice could reduce to BC under the offline setting.
Online Allocation Problem with Two-sided Resource Constraints
As a result, only limited violations of constraints or pessimistic competitive bounds could be guaranteed.
RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction
To overcome this limitation, we propose an innovative retrosynthesis prediction framework that can compose novel templates beyond training templates.
Ranked #8 on
Single-step retrosynthesis
on USPTO-50k
Progressive Multi-stage Interactive Training in Mobile Network for Fine-grained Recognition
Finally, using the progressive training (P), the features extracted by the model in different stages can be fully utilized and fused with each other.
Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation
We further propose a two-stage knowledge distillation method to improve the efficiency of the large PLM-based news recommendation model while maintaining its performance.
Adaptive Online Packing-guided Search for POMDPs
A belief is a distribution of states representing state uncertainty.
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning
Experiments on MuJoCo and Hand Manipulation Suite tasks show that the agents deployed with our method achieve similar performance as it has in the source domain, while those deployed with previous methods designed for same-modal domain adaptation suffer a larger performance gap.
Offline Model-based Adaptable Policy Learning
Current offline reinforcement learning methods commonly learn in the policy space constrained to in-support regions by the offline dataset, in order to ensure the robustness of the outcome policies.
Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach
In order to solve this model, this research combines Jaya algorithm and interior point method (IPM) to develop a hybrid analysis-heuristic solution method called Jaya-IPM, where the lower- and upper- levels are respectively addressed by the IPM and the Jaya, and the scheduling scheme is obtained via iterations between the two levels.
Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods
Experimental results in an exemplary environment show that our MARL approach is able to demonstrate the effectiveness and necessity of restrictions on individual liberty for collaborative supply of public goods.
Learning Efficient Online 3D Bin Packing on Packing Configuration Trees
PCT is a full-fledged description of the state and action space of bin packing which can support packing policy learning based on deep reinforcement learning (DRL).
LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates
During long-time observations, agents can build \textit{awareness} for teammates to alleviate the problem of partial observability.
UserBERT: Contrastive User Model Pre-training
Two self-supervision tasks are incorporated in UserBERT for user model pre-training on unlabeled user behavior data to empower user modeling.
Neural-to-Tree Policy Distillation with Policy Improvement Criterion
The empirical results show that the proposed method can preserve a higher cumulative reward than behavior cloning and learn a more consistent policy to the original one.
PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management
PatrickStar uses the CPU-GPU heterogeneous memory space to store the model data.
Imitate TheWorld: A Search Engine Simulation Platform
Recent E-commerce applications benefit from the growth of deep learning techniques.
On Generalization of Adversarial Imitation Learning and Beyond
For some MDPs, we show that vanilla AIL has a worse sample complexity than BC.
HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation
Instead of a single user embedding, in our method each user is represented in a hierarchical interest tree to better capture their diverse and multi-grained interest in news.
Context-Aware Sparse Deep Coordination Graphs
Learning sparse coordination graphs adaptive to the coordination dynamics among agents is a long-standing problem in cooperative multi-agent learning.
Active Hierarchical Exploration with Stable Subgoal Representation Learning
Although GCHRL possesses superior exploration ability by decomposing tasks via subgoals, existing GCHRL methods struggle in temporally extended tasks with sparse external rewards, since the high-level policy learning relies on external rewards.
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization
Reinforcement learning (RL) has made remarkable progress in many decision-making tasks, such as Go, game playing, and robotics control.
Regret Minimization Experience Replay in Off-Policy Reinforcement Learning
In reinforcement learning, experience replay stores past samples for further reuse.
An Introduction of mini-AlphaStar
StarCraft II (SC2) is a real-time strategy game in which players produce and control multiple units to fight against opponent's units.
Distributed Bootstrap for Simultaneous Inference Under High Dimensionality
We propose a distributed bootstrap method for simultaneous inference on high-dimensional massive data that are stored and processed with many machines.
Derivative-Free Reinforcement Learning: A Review
In this article, we summarize methods of derivative-free reinforcement learning to date, and organize the methods in aspects including parameter updating, model selection, exploration, and parallel/distributed methods.
Self-supervised learning for fast and scalable time series hyper-parameter tuning
Hyper-parameters of time series models play an important role in time series analysis.
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application
However, existing language models are pre-trained and distilled on general corpus like Wikipedia, which has some gaps with the news domain and may be suboptimal for news intelligence.
The Flare and Warp of the Young Stellar Disk traced with LAMOST DR5 OB-type stars
We present analysis of the spatial density structure for the outer disk from 8$-$14 \, kpc with the LAMOST DR5 13534 OB-type stars and observe similar flaring on north and south sides of the disk implying that the flaring structure is symmetrical about the Galactic plane, for which the scale height at different Galactocentric distance is from 0. 14 to 0. 5 \, kpc.
Astrophysics of Galaxies
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
We evaluate existing offline RL algorithms on NeoRL and argue that the performance of a policy should also be compared with the deterministic version of the behavior policy, instead of the dataset reward.
ASBSO: An Improved Brain Storm Optimization With Flexible Search Length and Memory-Based Selection
This proposed method, adaptive step length based on memory selection BSO, namely ASBSO, applies multiple step lengths to modify the generation process of new solutions, thus supplying a flexible search according to corresponding problems and convergent periods.
Cross-Modal Domain Adaptation for Reinforcement Learning
Domain adaptation is a promising direction for deploying RL agents in real-world applications, where vision-based robotics tasks constitute an important part.
Offline Adaptive Policy Leaning in Real-World Sequential Recommendation Systems
Instead of increasing the fidelity of models for policy learning, we handle the distortion issue via learning to adapt to diverse simulators generated by the offline dataset.
Interactive Search Based on Deep Reinforcement Learning
With the continuous development of machine learning technology, major e-commerce platforms have launched recommendation systems based on it to serve a large number of customers with different needs more efficiently.
Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Embeddings and the Implications to Representation Learning
Human judgments of word similarity have been a popular method of evaluating the quality of word embedding.
Offline Imitation Learning with a Misspecified Simulator
In this work, we investigate policy learning in the condition of a few expert demonstrations and a simulator with misspecified dynamics.
OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs
We demonstrate the feasibility of this framework by proposing an approach underpinned by the framework for organizational model discovery, and also conduct experiments on real-life event logs to discover and evaluate organizational models.
Angular Embedding: A New Angular Robust Principal Component Analysis
As a widely used method in machine learning, principal component analysis (PCA) shows excellent properties for dimensionality reduction.
RetroXpert: Decompose Retrosynthesis Prediction like a Chemist
Retrosynthesis is the process of recursively decomposing target molecules into available building blocks.
Mining Generalized Features for Detecting AI-Manipulated Fake Faces
Recently, AI-manipulated face techniques have developed rapidly and constantly, which has raised new security issues in society.
Multiple-element joint detection for Aspect-Based Sentiment Analysis
In this paper, we propose a novel end-to-end multiple-element joint detection model (MEJD), which effectively extracts all (target, aspect, sentiment) triples from a sentence.
Error Bounds of Imitating Policies and Environments
In this paper, we firstly analyze the value gap between the expert policy and imitated policies by two imitation methods, behavioral cloning and generative adversarial imitation.
Difference-in-Differences: Bridging Normalization and Disentanglement in PG-GAN
We discover that pixel normalization causes object entanglement by in-painting the area occupied by ablated objects.
TurboTransformers: An Efficient GPU Serving System For Transformer Models
This paper designed a transformer serving system called TurboTransformers, which consists of a computing runtime and a serving framework to solve the above challenges.
Reinforced Epidemic Control: Saving Both Lives and Economy
Saving lives or economy is a dilemma for epidemic control in most cities while smart-tracing technology raises people's privacy concerns.
QPLEX: Duplex Dueling Multi-Agent Q-Learning
This paper presents a novel MARL approach, called duPLEX dueling multi-agent Q-learning (QPLEX), which takes a duplex dueling network architecture to factorize the joint value function.
Local Neighbor Propagation Embedding
Manifold Learning occupies a vital role in the field of nonlinear dimensionality reduction and its ideas also serve for other relevant methods.
Affect inTweets: A Transfer Learning Approach
As opposed to the traditional machine learning models which require considerable effort in designing task specific features, our model can be well adapted to the proposed tasks with a very limited amount of fine-tuning, which significantly reduces the manual effort in feature engineering.
The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation
Moreover, many countries have imposed tough lockdown measures to reduce the virus transmission (e. g., retail, catering) during the pandemic, which causes inconveniences for human daily life.
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
The framework consists of an evaluator that generalizes to evaluate recommendations involving the context, and a generator that maximizes the evaluator score by reinforcement learning, and a discriminator that ensures the generalization of the evaluator.
Novelty-Prepared Few-Shot Classification
We disclose that a classically fully trained feature extractor can leave little embedding space for unseen classes, which keeps the model from well-fitting the new classes.
Simultaneous Inference for Massive Data: Distributed Bootstrap
In this paper, we propose a bootstrap method applied to massive data processed distributedly in a large number of machines.
Residual Bootstrap Exploration for Bandit Algorithms
In this paper, we propose a novel perturbation-based exploration method in bandit algorithms with bounded or unbounded rewards, called residual bootstrap exploration (\texttt{ReBoot}).
Temporal-adaptive Hierarchical Reinforcement Learning
Hierarchical reinforcement learning (HRL) helps address large-scale and sparse reward issues in reinforcement learning.
Robust Data-driven Profile-based Pricing Schemes
To enable an efficient electricity market, a good pricing scheme is of vital importance.
A Data-driven Storage Control Framework for Dynamic Pricing
Dynamic pricing is both an opportunity and a challenge to the demand side.
Bridging Machine Learning and Logical Reasoning by Abductive Learning
In the area of artificial intelligence (AI), the two abilities are usually realised by machine learning and logic programming, respectively.
Improving Fictitious Play Reinforcement Learning with Expanding Models
However, learning to beat a pool in stochastic games, i. e., a wide distribution over policy models, is either sample-consuming or insufficient to exploit all models with limited amount of samples.
Vulnerability Analysis for Data Driven Pricing Schemes
In this paper, from an adversarial machine learning point of view, we examine the vulnerability of data-driven electricity market design.
On Value Discrepancy of Imitation Learning
We also show that the framework leads to the value discrepancy of GAIL in an order of O((1-\gamma)^{-1}).
Optimal Storage Control for Dynamic Pricing
Renewable energy brings huge uncertainties to the power system, which challenges the traditional power system operation with limited flexible resources.
Systems and Control Systems and Control Optimization and Control
Conductor Galloping Prediction on Imbalanced Datasets: SVM with Smart Sampling
Conductor galloping is the high-amplitude, low-frequency oscillation of overhead power lines due to wind.
Signal Combination for Language Identification
We compare the performance of a lattice-based ensemble model and a deep neural network model to combine signals from recognizers with that of a baseline that only uses low-level acoustic signals.
Deep exploration by novelty-pursuit with maximum state entropy
Efficient exploration is essential to reinforcement learning in huge state space.
Hierarchic Neighbors Embedding
Manifold learning now plays a very important role in machine learning and many relevant applications.
Learning Action-Transferable Policy with Action Embedding
Transfer learning (TL) is a promising way to improve the sample efficiency of reinforcement learning.
Out-of-Domain Detection for Low-Resource Text Classification Tasks
Out-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task.
On the Robustness of Median Sampling in Noisy Evolutionary Optimization
Sampling is a popular strategy, which evaluates the objective a couple of times, and employs the mean of these evaluation results as an estimate of the objective value.
Towards AutoML in the presence of Drift: first results
We extendAuto-Sklearn with sound and intuitive mechanisms that allow it to cope with this sort ofproblems.
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation
DEMER also derives a recommendation policy with a significantly improved performance in the test phase of the real application.
Running Time Analysis of the (1+1)-EA for Robust Linear Optimization
Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties.
Key Ingredients of Self-Driving Cars
Over the past decade, many research articles have been published in the area of autonomous driving.
Knowledge-augmented Column Networks: Guiding Deep Learning with Advice
Recently, deep models have had considerable success in several tasks, especially with low-level representations.
Cascaded Algorithm-Selection and Hyper-Parameter Optimization with Extreme-Region Upper Confidence Bound Bandit
We show theoretically that the ER-UCB has a regret upper bound $O\left(K \ln n\right)$ with independent feedbacks, which is as efficient as the classical UCB bandit.
Reinforcement Learning Experience Reuse with Policy Residual Representation
Experience reuse is key to sample-efficient reinforcement learning.
Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images
Traditionally, the most indispensable diagnosis of cervix squamous carcinoma is histopathological assessment which is achieved under microscope by pathologist.
Automatic Calibration of Multiple 3D LiDARs in Urban Environments
Multiple LiDARs have progressively emerged on autonomous vehicles for rendering a wide field of view and dense measurements.
Human-Guided Column Networks: Augmenting Deep Learning with Advice
While extremely successful in several applications, especially with low-level representations; sparse, noisy samples and structured domains (with multiple objects and interactions) are some of the open challenges in most deep models.
A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces
Multiple lidars are prevalently used on mobile vehicles for rendering a broad view to enhance the performance of localization and perception systems.
Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice
Recently, deep models have been successfully applied in several applications, especially with low-level representations.
Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning
Injecting human knowledge is an effective way to accelerate reinforcement learning (RL).
PointIT: A Fast Tracking Framework Based on 3D Instance Segmentation
Finally, we extend the Sort algorithm with this instance framework to realize tracking in the 3D LiDAR point cloud data.
An end-to-end Generative Retrieval Method for Sponsored Search Engine --Decoding Efficiently into a Closed Target Domain
In this paper, we present a generative retrieval method for sponsored search engine, which uses neural machine translation (NMT) to generate keywords directly from query.
Tuplemax Loss for Language Identification
In many scenarios of a language identification task, the user will specify a small set of languages which he/she can speak instead of a large set of all possible languages.
Day-to-Day Dynamic Traffic Assignment with Imperfect Information, Bounded Rationality and Information Sharing
These two variants, serving as based models, are further extended with two features: bounded rationality (BR) and information sharing.
Physics and Society Optimization and Control
Taking Human out of Learning Applications: A Survey on Automated Machine Learning
We hope this survey can serve as not only an insightful guideline for AutoML beginners but also an inspiration for future research.
Analysis of Noisy Evolutionary Optimization When Sampling Fails
In noisy evolutionary optimization, sampling is a common strategy to deal with noise.
Exploration by Uncertainty in Reward Space
There are two policies in this approach, the exploration policy is used for exploratory sampling in the environment, then the benchmark policy try to update by the data proven by the exploration policy.
On Reinforcement Learning for Full-length Game of StarCraft
The reinforcement training algorithm for this architecture is also investigated.
Hierarchical Reinforcement Learning
reinforcement-learning
+4
Multi-Layered Gradient Boosting Decision Trees
Multi-layered representation is believed to be the key ingredient of deep neural networks especially in cognitive tasks like computer vision.
Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning
Applying reinforcement learning in physical-world tasks is extremely challenging.
WikiRank: Improving Keyphrase Extraction Based on Background Knowledge
Keyphrase is an efficient representation of the main idea of documents.
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
For better utilizing the correlation between different ranking steps, in this paper, we propose to use reinforcement learning (RL) to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session.
Knowledge Base Relation Detection via Multi-View Matching
Relation detection is a core component for Knowledge Base Question Answering (KBQA).
Tunneling Neural Perception and Logic Reasoning through Abductive Learning
Perception and reasoning are basic human abilities that are seamlessly connected as part of human intelligence.
ZOOpt: Toolbox for Derivative-Free Optimization
Recent advances in derivative-free optimization allow efficient approximation of the global-optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions.
Subset Selection under Noise
The problem of selecting the best $k$-element subset from a universe is involved in many applications.
Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms
To provide a general theoretical explanation of the behavior of EAs, it is desirable to study their performance on general classes of combinatorial optimization problems.
Open-Category Classification by Adversarial Sample Generation
ASG generates positive and negative samples of seen categories in the unsupervised manner via an adversarial learning strategy.
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given document to answer questions.
Ranked #49 on
Question Answering
on SQuAD1.1 dev
A Lower Bound Analysis of Population-based Evolutionary Algorithms for Pseudo-Boolean Functions
Our results imply that the increase of population size, while usually desired in practice, bears the risk of increasing the lower bound of the running time and thus should be carefully considered.
Subset Selection by Pareto Optimization
Selecting the optimal subset from a large set of variables is a fundamental problem in various learning tasks such as feature selection, sparse regression, dictionary learning, etc.
Empirical Study on Deep Learning Models for Question Answering
In this paper we explore deep learning models with memory component or attention mechanism for question answering task.