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Found 60 papers, 15 papers with code
Adaptable Semantic Compression and Resource Allocation for Task-Oriented Communications
To solve the problem, both compression ratio and resource allocation are optimized for the task-oriented communication system to maximize the success probability of tasks.
Offline Reinforcement Learning Under Value and Density-Ratio Realizability: the Power of Gaps
We consider a challenging theoretical problem in offline reinforcement learning (RL): obtaining sample-efficiency guarantees with a dataset lacking sufficient coverage, under only realizability-type assumptions for the function approximators.
ActFormer: A GAN Transformer Framework towards General Action-Conditioned 3D Human Motion Generation
Such a design combines the strong spatio-temporal representation capacity of Transformer, superiority in generative modeling of GAN, and inherent temporal correlations from latent prior.
VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie Recommendation
Fortunately, context-aware recommender systems can alleviate the sparsity problem by making use of some auxiliary information, such as the information of both the users and items.
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality
Deployment efficiency is an important criterion for many real-world applications of reinforcement learning (RL).
Offline Reinforcement Learning with Realizability and Single-policy Concentrability
Sample-efficiency guarantees for offline reinforcement learning (RL) often rely on strong assumptions on both the function classes (e. g., Bellman-completeness) and the data coverage (e. g., all-policy concentrability).
Adversarially Trained Actor Critic for Offline Reinforcement Learning
We propose Adversarially Trained Actor Critic (ATAC), a new model-free algorithm for offline reinforcement learning under insufficient data coverage, based on a two-player Stackelberg game framing of offline RL: A policy actor competes against an adversarially trained value critic, who finds data-consistent scenarios where the actor is inferior to the data-collection behavior policy.
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes
In this work, we first propose novel identification methods for OPE in POMDPs with latent confounders, by introducing bridge functions that link the target policy's value and the observed data distribution.
Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning
How to select between policies and value functions produced by different training algorithms in offline reinforcement learning (RL) -- which is crucial for hyperpa-rameter tuning -- is an important open question.
A Fast Randomized Algorithm for Massive Text Normalization
In addition, FLAN does not require any annotated data or supervised learning.
A Spectral Approach to Off-Policy Evaluation for POMDPs
We consider off-policy evaluation (OPE) in Partially Observable Markov Decision Processes, where the evaluation policy depends only on observable variables but the behavior policy depends on latent states (Tennenholtz et al. (2020a)).
Bellman-consistent Pessimism for Offline Reinforcement Learning
The use of pessimism, when reasoning about datasets lacking exhaustive exploration has recently gained prominence in offline reinforcement learning.
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning
This offline result is the first that matches the sample complexity lower bound in this setting, and resolves a recent open question in offline RL.
On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction
In this paper, we study the convergence properties of off-policy policy improvement algorithms with state-action density ratio correction under function approximation setting, where the objective function is formulated as a max-max-min optimization problem.
Minimax Model Learning
We present a novel off-policy loss function for learning a transition model in model-based reinforcement learning.
CURE: Code-Aware Neural Machine Translation for Automatic Program Repair
Finally, CURE uses a subword tokenization technique to generate a smaller search space that contains more correct fixes.
Model-free Representation Learning and Exploration in Low-rank MDPs
In this work, we present the first model-free representation learning algorithms for low rank MDPs.
SM+: Refined Scale Match for Tiny Person Detection
Detecting tiny objects ( e. g., less than 20 x 20 pixels) in large-scale images is an important yet open problem.
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency
We offer a theoretical characterization of off-policy evaluation (OPE) in reinforcement learning using function approximation for marginal importance weights and $q$-functions when these are estimated using recent minimax methods.
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function
We consider local planning in fixed-horizon MDPs with a generative model under the assumption that the optimal value function lies close to the span of a feature map.
Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking
The releasing of such a large-scale dataset could be a useful initial step in research of tracking UAVs.
Experimental demonstration of memory-enhanced scaling for entanglement connection of quantum repeater segments
The experimental realization of entanglement connection of two quantum repeater segments with an efficient memory-enhanced scaling demonstrates a key advantage of the quantum repeater protocol, which makes a cornerstone towards future large-scale quantum networks.
Quantum Physics
Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networks
We apply the method to 11, 790 urban road networks across 30 cities worldwide to measure the spatial homogeneity of road networks within each city and across different cities.
When Counterpoint Meets Chinese Folk Melodies
An interaction reward model is trained on the duets formed from outer parts of Bach chorales to model counterpoint interaction, while a style reward model is trained on monophonic melodies of Chinese folk songs to model melodic patterns.
Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach
Generating natural language under complex constraints is a principled formulation towards controllable text generation.
A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting
Recently, Wang et al. (2020) showed a highly intriguing hardness result for batch reinforcement learning (RL) with linearly realizable value function and good feature coverage in the finite-horizon case.
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration
This paper studies regret minimization with randomized value functions in reinforcement learning.
The 1st Tiny Object Detection Challenge:Methods and Results
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.
Analysis of Random Access in NB-IoT Networks with Three Coverage Enhancement Groups: A Stochastic Geometry Approach
NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks.
Batch Value-function Approximation with Only Realizability
We make progress in a long-standing problem of batch reinforcement learning (RL): learning $Q^\star$ from an exploratory and polynomial-sized dataset, using a realizable and otherwise arbitrary function class.
A Question Type Driven and Copy Loss Enhanced Frameworkfor Answer-Agnostic Neural Question Generation
The answer-agnostic question generation is a significant and challenging task, which aims to automatically generate questions for a given sentence but without an answer.
Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison
We prove performance guarantees of two algorithms for approximating $Q^\star$ in batch reinforcement learning.
RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning
We cast this as a reinforcement learning problem, where the generation agent learns a policy to generate a musical note (action) based on previously generated context (state).
Minimax Value Interval for Off-Policy Evaluation and Policy Optimization
By slightly altering the derivation of previous methods (one from each style; Uehara et al., 2020), we unify them into a single value interval that comes with a special type of double robustness: when either the value-function or the importance-weight class is well specified, the interval is valid and its length quantifies the misspecification of the other class.
Scale Match for Tiny Person Detection
In this paper, we introduce a new benchmark, referred to as TinyPerson, opening up a promising directionfor tiny object detection in a long distance and with mas-sive backgrounds.
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
We offer an experimental benchmark and empirical study for off-policy policy evaluation (OPE) in reinforcement learning, which is a key problem in many safety critical applications.
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
We provide theoretical investigations into off-policy evaluation in reinforcement learning using function approximators for (marginalized) importance weights and value functions.
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles
As an extension, we also consider the more challenging problem of model selection, where the state features are unknown and can be chosen from a large candidate set.
From Importance Sampling to Doubly Robust Policy Gradient
We show that on-policy policy gradient (PG) and its variance reduction variants can be derived by taking finite difference of function evaluations supplied by estimators from the importance sampling (IS) family for off-policy evaluation (OPE).
Quantum Communication between Multiplexed Atomic Quantum Memories
The use of multiplexed atomic quantum memories (MAQM) can significantly enhance the efficiency to establish entanglement in a quantum network.
Quantum Physics
On Value Functions and the Agent-Environment Boundary
When function approximation is deployed in reinforcement learning (RL), the same problem may be formulated in different ways, often by treating a pre-processing step as a part of the environment or as part of the agent.
Provably Efficient Q-Learning with Low Switching Cost
We take initial steps in studying PAC-MDP algorithms with limited adaptivity, that is, algorithms that change its exploration policy as infrequently as possible during regret minimization.
Information-Theoretic Considerations in Batch Reinforcement Learning
Value-function approximation methods that operate in batch mode have foundational importance to reinforcement learning (RL).
Provably efficient RL with Rich Observations via Latent State Decoding
We study the exploration problem in episodic MDPs with rich observations generated from a small number of latent states.
Completing State Representations using Spectral Learning
A central problem in dynamical system modeling is state discovery—that is, finding a compact summary of the past that captures the information needed to predict the future.
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
We study the sample complexity of model-based reinforcement learning (henceforth RL) in general contextual decision processes that require strategic exploration to find a near-optimal policy.
Why Do Neural Response Generation Models Prefer Universal Replies?
Recent advances in sequence-to-sequence learning reveal a purely data-driven approach to the response generation task.
LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics
The experimental results have shown that our proposed corpus can be taken as a new benchmark dataset for the NRG task, and the presented metrics are promising to guide the optimization of NRG models by quantifying the diversity of the generated responses reasonably.
Image Classification Based on Quantum KNN Algorithm
However, the usually used classification method --- the K Nearest-Neighbor algorithm has high complexity, because its two main processes: similarity computing and searching are time-consuming.
Hierarchical Imitation and Reinforcement Learning
We study how to effectively leverage expert feedback to learn sequential decision-making policies.
On Oracle-Efficient PAC RL with Rich Observations
We study the computational tractability of PAC reinforcement learning with rich observations.
Markov Decision Processes with Continuous Side Information
Because our lower bound has an exponential dependence on the dimension, we consider a tractable linear setting where the context is used to create linear combinations of a finite set of MDPs.
Repeated Inverse Reinforcement Learning
We introduce a novel repeated Inverse Reinforcement Learning problem: the agent has to act on behalf of a human in a sequence of tasks and wishes to minimize the number of tasks that it surprises the human by acting suboptimally with respect to how the human would have acted.
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable
Our first contribution is a complexity measure, the Bellman rank, that we show enables tractable learning of near-optimal behavior in these processes and is naturally small for many well-studied reinforcement learning settings.
Neural Network Architecture Optimization through Submodularity and Supermodularity
Deep learning models' architectures, including depth and width, are key factors influencing models' performance, such as test accuracy and computation time.
Optimizing Recurrent Neural Networks Architectures under Time Constraints
A greedy algorithm with bounds is suggested to solve the transformed problem.
Hand Segmentation for Hand-Object Interaction from Depth map
Thus, we propose hand segmentation method for hand-object interaction using only a depth map.
Word Embedding based Correlation Model for Question/Answer Matching
With the development of community based question answering (Q&A) services, a large scale of Q&A archives have been accumulated and are an important information and knowledge resource on the web.
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
We study the problem of off-policy value evaluation in reinforcement learning (RL), where one aims to estimate the value of a new policy based on data collected by a different policy.
Unifying Spatial and Attribute Selection for Distracter-Resilient Tracking
This paper presents a novel method to jointly determine the best spatial location and the optimal metric.
