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Found 38 papers, 5 papers with code
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games
We then design an uncoupled no-regret algorithm that finds an $\varepsilon$-approximate $K$-EFCE within $\widetilde{\mathcal{O}}(\max_{i}X_iA_i^{K}/\varepsilon^2)$ iterations in the full feedback setting, where $X_i$ and $A_i$ are the number of information sets and actions for the $i$-th player.
PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization
Few-shot abstractive summarization has become a challenging task in natural language generation.
Non-autoregressive Translation with Dependency-Aware Decoder
In this paper, we propose a novel and general approach to enhance the target dependency within the NAT decoder from two perspectives: decoder input and decoder self-attention.
Efficient and Differentiable Conformal Prediction with General Function Classes
Experiments show that our algorithm is able to learn valid prediction sets and improve the efficiency significantly over existing approaches in several applications such as prediction intervals with improved length, minimum-volume prediction sets for multi-output regression, and label prediction sets for image classification.
Privacy protection based on mask template
Powerful recognition algorithms are widely used in the Internet or important medical systems, which poses a serious threat to personal privacy.
Near-Optimal Learning of Extensive-Form Games with Imperfect Information
This improves upon the best known sample complexity of $\widetilde{\mathcal{O}}((X^2A+Y^2B)/\varepsilon^2)$ by a factor of $\widetilde{\mathcal{O}}(\max\{X, Y\})$, and matches the information-theoretic lower bound up to logarithmic factors.
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning
We validate the learned solutions are $\epsilon$-meta-equilibria through best-response analyses, show that they align with economic intuitions, and show our approach can learn a spectrum of qualitatively distinct $\epsilon$-meta-equilibria in open RBC models.
Unifying Cross-lingual Summarization and Machine Translation with Compression Rate
Through introducing compression rate, the information ratio between the source and the target text, we regard the MT task as a special CLS task with a compression rate of 100%.
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?
First, we design algorithms for learning an $\epsilon$-Coarse Correlated Equilibrium (CCE) in $\widetilde{\mathcal{O}}(H^5S\max_{i\le m} A_i / \epsilon^2)$ episodes, and an $\epsilon$-Correlated Equilibrium (CE) in $\widetilde{\mathcal{O}}(H^6S\max_{i\le m} A_i^2 / \epsilon^2)$ episodes.
Local Calibration: Metrics and Recalibration
In this work, we propose the local calibration error (LCE) to span the gap between average and individual reliability.
Cross-Lingual Language Model Meta-Pretraining
The success of pretrained cross-lingual language models relies on two essential abilities, i. e., generalization ability for learning downstream tasks in a source language, and cross-lingual transferability for transferring the task knowledge to other languages.
Understanding the Under-Coverage Bias in Uncertainty Estimation
Estimating the data uncertainty in regression tasks is often done by learning a quantile function or a prediction interval of the true label conditioned on the input.
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.
Cross-Lingual Abstractive Summarization with Limited Parallel Resources
Employing one unified decoder to generate the sequential concatenation of monolingual and cross-lingual summaries, MCLAS makes the monolingual summarization task a prerequisite of the cross-lingual summarization (CLS) task.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
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Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network
Predicting future trajectories of surrounding obstacles is a crucial task for autonomous driving cars to achieve a high degree of road safety.
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models
We show that, in the setting where the classical uniform convergence bound is vacuous (diverges to $\infty$), uniform convergence over the interpolators still gives a non-trivial bound of the test error of interpolating solutions.
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games
Real world applications such as economics and policy making often involve solving multi-agent games with two unique features: (1) The agents are inherently asymmetric and partitioned into leaders and followers; (2) The agents have different reward functions, thus the game is general-sum.
Localized Calibration: Metrics and Recalibration
Probabilistic classifiers output confidence scores along with their predictions, and these confidence scores must be well-calibrated (i. e. reflect the true probability of an event) to be meaningful and useful for downstream tasks.
Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
Modern machine learning models with high accuracy are often miscalibrated -- the predicted top probability does not reflect the actual accuracy, and tends to be over-confident.
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction
Our main result shows that OPDVR provably identifies an $\epsilon$-optimal policy with $\widetilde{O}(H^2/d_m\epsilon^2)$ episodes of offline data in the finite-horizon stationary transition setting, where $H$ is the horizon length and $d_m$ is the minimal marginal state-action distribution induced by the behavior policy.
Improved Uncertainty Post-Calibration via Rank Preserving Transforms
In this paper, we propose Neural Rank Preserving Transforms (NRPT), a new post-calibration method that adjusts the output probabilities of a trained classifier using a calibrator of higher capacity, while maintaining its prediction accuracy.
How Important is the Train-Validation Split in Meta-Learning?
A common practice in meta-learning is to perform a train-validation split (\emph{train-val method}) where the prior adapts to the task on one split of the data, and the resulting predictor is evaluated on another split.
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
However, for multi-agent reinforcement learning in Markov games, the current best known sample complexity for model-based algorithms is rather suboptimal and compares unfavorably against recent model-free approaches.
Model-based Reinforcement Learning
Multi-agent Reinforcement Learning
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Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning
The problem of Offline Policy Evaluation (OPE) in Reinforcement Learning (RL) is a critical step towards applying RL in real-life applications.
Towards Understanding Hierarchical Learning: Benefits of Neural Representations
When the trainable network is the quadratic Taylor model of a wide two-layer network, we show that neural representation can achieve improved sample complexities compared with the raw input: For learning a low-rank degree-$p$ polynomial ($p \geq 4$) in $d$ dimension, neural representation requires only $\tilde{O}(d^{\lceil p/2 \rceil})$ samples, while the best-known sample complexity upper bound for the raw input is $\tilde{O}(d^{p-1})$.
Near-Optimal Reinforcement Learning with Self-Play
This paper considers the problem of designing optimal algorithms for reinforcement learning in two-player zero-sum games.
Provable Self-Play Algorithms for Competitive Reinforcement Learning
We introduce a self-play algorithm---Value Iteration with Upper/Lower Confidence Bound (VI-ULCB)---and show that it achieves regret $\tilde{\mathcal{O}}(\sqrt{T})$ after playing $T$ steps of the game, where the regret is measured by the agent's performance against a \emph{fully adversarial} opponent who can exploit the agent's strategy at \emph{any} step.
Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width
We propose \emph{Taylorized training} as an initiative towards better understanding neural network training at finite width.
Directed-Weighting Group Lasso for Eltwise Blocked CNN Pruning
Eltwise layer is a commonly used structure in the multi-branch deep learning network.
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks
Recent theoretical work has established connections between over-parametrized neural networks and linearized models governed by he Neural Tangent Kernels (NTKs).
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.
Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs
We study a family of (potentially non-convex) constrained optimization problems with convex composite structure.
Subgradient Descent Learns Orthogonal Dictionaries
This paper concerns dictionary learning, i. e., sparse coding, a fundamental representation learning problem.
ProxQuant: Quantized Neural Networks via Proximal Operators
To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights.
Approximability of Discriminators Implies Diversity in GANs
Our preliminary experiments show that on synthetic datasets the test IPM is well correlated with KL divergence or the Wasserstein distance, indicating that the lack of diversity in GANs may be caused by the sub-optimality in optimization instead of statistical inefficiency.
CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices
As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performance while maintaining accuracy.
TAPAS: Two-pass Approximate Adaptive Sampling for Softmax
TAPAS is a novel adaptive sampling method for the softmax model.
The Landscape of Empirical Risk for Non-convex Losses
We establish uniform convergence of the gradient and Hessian of the empirical risk to their population counterparts, as soon as the number of samples becomes larger than the number of unknown parameters (modulo logarithmic factors).
