Paper introduces EnCounteR for estimating causal effects using encouragement data.
problem Challenges in estimating causal effects due to incomplete randomization and limited encouragement data.
method Introduces a generalized IV estimator, EnCounteR, leveraging both observational and encouragement data.
result Demonstrates superior performance of EnCounteR over existing methods.
The paper develops classifiers that encourage positive adaptation in machine learning settings.
problem Strategic behavior by decision subjects leads to performance loss in machine learning models.
method Formulates a two-stage game to characterize optimal strategies for model designers and decision subjects.
result Trained classifiers maintain accuracy while inducing higher improvement and less manipulation.
Boosts neural network performance by improving weight separability.
problem Improving the separability of weight vectors in neural networks.
method Proposes a new evaluation metric and feed-backward reconstruction loss to encourage weight separability.
result Improves visual recognition performance across various tasks.
Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment. The benchmark is designed to encourage the development of learning algorithms that are sa…
MIM learns joint distributions with mutual information and low divergence.
problem Learning joint distributions over observations and latent variables.
method Probabilistic auto-encoder with three design principles: low divergence, high mutual information, and low marginal entropy.
result MIM learns representations with high mutual information, consistent encoding and decoding distributions, effective latent clustering, and comparable data log likelihood to VAE.
Automates translating natural language to Verilog for digital design.
problem Manual translation of natural language specifications to Verilog is time-consuming and error-prone.
method Fine-tuned GPT-2 to derive Verilog from English, using a dataset of design tasks.
result GPT-2 achieved 94.8% correct translation across simple and abstract design tasks.
The paper encourages Kleinian group thinking for higher rank Lie groups.
problem No specific problem stated; encouraging new thinking.
method Discussion of Kleinian group ideas applied to higher rank Lie groups.
result Encouragement to think about higher rank Lie groups using Kleinian group theory.
In designing personalized ranking algorithms, it is desirable to encourage a high precision at the top of the ranked list. Existing methods either seek a smooth convex surrogate for a non-smooth ranking metric or directly modify updating procedures to encourage top accuracy. In this work we point out that these methods…
A new model improves clustering by reducing redundancy in mixture of local EPCAs.
problem Redundancy in traditional mixture models causes ambiguity in clustering.
method Introduced a repulsiveness-encouraging prior and a DPP for diversity in DEPCAM model.
result The method reduces model redundancy and improves clustering performance.
SMGD trains low-bit neural networks with memory constraints.
problem Training large neural networks with limited memory.
method Stochastic Markov Gradient Descent (SMGD).
result Encouraging numerical results and theoretical guarantees.
Autonomous driving is getting a lot of attention in the last decade and will be the hot topic at least until the first successful certification of a car with Level 5 autonomy. There are many public datasets in the academic community. However, they are far away from what a robust industrial production system needs. Ther…
Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant? To help answer this question, we design and investigate different image generation models associated with different loss functions to boost creativity in fashion generation. The dimensions of our explorations include…
Model predicts stable molecules with AI and physics constraints.
problem Designing stable molecules with limited data.
method Graph Scattering Variational Autoencoder with physical constraints.
result Model generates stable molecules with desired properties.
Simplified non-contrastive learning avoids representation collapse.
problem Training failure modes in self-supervised learning.
method Hyperdimensional computing and inductive bias.
result The approach avoids representation collapses.
Study optimal and equitable encouragement policies for treatment adherence.
problem Optimal treatment adherence policies in the presence of human non-adherence.
method Covariate-conditional no-direct-effect model of encouragement; tractable policy characterizations under constraints.
result Induced treatment take-up is the fairness target, not recommendation rates.
We introduce Generative Neural Machine Translation (GNMT), a latent variable architecture which is designed to model the semantics of the source and target sentences. We modify an encoder-decoder translation model by adding a latent variable as a language agnostic representation which is encouraged to learn the meaning…
This work proposes a model to disentangle image factors effectively and control their manipulation.
problem Controlling disentanglement during image editing while preserving object identity.
method Encoder-decoder architecture with decorrelation regularization and soft target representations.
result The model successfully disentangles image factors and manipulates them effectively.
Adapts Bayesian optimization for mixed constraints in aircraft design.
problem Optimizing expensive black box functions with mixed constraints.
method Super efficient global optimization with upper trust bound for constraints, Gaussian process uncertainty, refinement procedure.
result Superior performance on aircraft design problem compared to state-of-the-art solvers.
New benchmarks for RNA 3D structure-function modeling.
problem Lack of standardized benchmarks for RNA deep learning.
method Developed seven benchmark datasets, provided tools for data handling, and offered a user-friendly environment for model comparison.
result Demonstrated utility with baseline results using a relational graph neural network.
The paper tackles auction market design flaws by randomizing closing times and optimizing transaction fees.
problem Strategic traders exploit accumulated information to delay their orders, distorting auction efficiency.
method Randomizing auction closing times and designing optimal transaction fees policies.
result Policies encourage strategic traders to send orders earlier, improving auction market efficiency.
This paper tackles disentanglement in image editing and reconstruction.
problem Learning disentangled image representations and balancing disentanglement strength and reconstruction quality.
method Distance covariance based decorrelation regularization for disentanglement, soft target representation for reconstruction, and collapsing AE decoder and GAN generator.
result The proposed model improves the disentanglement strength and perceptual quality of generated images.
BSVGD improves sampling for multimodal distributions using branching.
problem Sampling from multimodal distributions.
method Random branching in Stein Variational Gradient Descent (SVGD).
result Theoretical convergence guarantee and empirical validation.
Proposes MIVI for efficient posterior estimation and design of MCMC transitions.
problem Efficiently estimating posterior distributions in constrained time.
method Combines variational inference and MCMC with a variational distribution and optimized Markov chain.
result Optimized Markov chain improves variational distribution and vice versa, leading to more accurate posteriors.
NGAT predicts long-term stock trends using graph attention networks.
problem Lack of effective corporate relationship graph comparison methods and model complexity in stock prediction.
method Developed a Node-level Graph Attention Network (NGAT) for corporate relationship graphs.
result Demonstrated the effectiveness of NGAT across two datasets.
The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling. When it comes to learning interpretable (disentangled) representations, VAE and its variants show unparalleled performance. However, the reasons for this are unclear, since a very particular alignmen…
We consider a class of a nested optimization problems involving inner and outer objectives. We observe that by taking into explicit account the optimization dynamics for the inner objective it is possible to derive a general framework that unifies gradient-based hyperparameter optimization and meta-learning (or learnin…
Dropout is one of the key techniques to prevent the learning from overfitting. It is explained that dropout works as a kind of modified L2 regularization. Here, we shed light on the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the dropout rate, which is beneficial for learning of wei…
Paper proposes incentive mechanism to encourage participation in federated learning.
problem Users are reluctant to participate in federated learning due to privacy concerns.
method Formulated as a two-stage Stackelberg game, designed an incentive mechanism to select and compensate users.
result Demonstrated effectiveness of the proposed incentive mechanism through simulations.
Efficient optimisation of black-box problems that comprise both continuous and categorical inputs is important, yet poses significant challenges. We propose a new approach, Continuous and Categorical Bayesian Optimisation (CoCaBO), which combines the strengths of multi-armed bandits and Bayesian optimisation to select …
Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test. To succeed at this challenge, models must cope with various gener…
Entropy regularization is commonly used to improve policy optimization in reinforcement learning. It is believed to help with \emph{exploration} by encouraging the selection of more stochastic policies. In this work, we analyze this claim using new visualizations of the optimization landscape based on randomly perturbi…
Managing unemployment is one of the key issues in social policies. Unemployment insurance schemes are designed to cushion the financial and morale blow of loss of job but also to encourage the unemployed to seek new jobs more pro-actively due to the continuous reduction of benefit payments. In the present paper, a simp…
Study copyright's impact on creative industries using AI-generated fonts.
problem Estimating supply and demand in creative industries with AI-generated content.
method Neural network embeddings, spatial regression, event-study analyses, structural model of supply and demand.
result Copyright can raise consumer welfare by encouraging product relocation.
This work introduces reward teaching for federated multi-armed bandits to guide clients towards global optimality.
problem Existing federated multi-armed bandits designs assume clients will follow the server's protocol, but this is not always feasible.
method Introduces reward teaching where the server adjusts clients' local rewards to encourage global optimality, using phased Teaching-After-Learning (TAL) and Teaching-While-Learning (TWL) algorithms.
result Demonstrates that TAL achieves logarithmic regrets with only logarithmic adjustment costs, and TWL outperforms TAL for UCB1 clients.
The paper proposes a method to train NNs with a small Lipschitz constant to improve robustness.
problem Neural networks' susceptibility to adversarial perturbations in safety-critical applications.
method The paper introduces a framework to train multi-layer NNs by minimizing their Lipschitz constant, using an optimization scheme based on the Alternating Direction Method of Multipliers.
result The proposed training procedure successfully increases the robustness of neural networks.
Progress in machine learning is measured by careful evaluation on problems of outstanding common interest. However, the proliferation of benchmark suites and environments, adversarial attacks, and other complications has diluted the basic evaluation model by overwhelming researchers with choices. Deliberate or accident…
Challenge encourages reproducible deep learning methods.
problem Inconsistent training behavior in deep learning competitions.
method Evaluate methods based on their training procedures, retrain in controlled settings.
result Guaranteed reproducibility and generalization of methods.
A new model improves the performance of knowledge distillation in GNNs.
problem Inconsistent performance of existing knowledge distillation techniques in GNNs.
method Proposes a new model, Routing-by-Memory (RbM), a form of Mixture-of-Experts (MoE), to address performance concerns.
result Demonstrates experimentally that RbM achieves considerably more consistent performance across multiple datasets.
We describe a novel binary classification technique called Banded SVM (B-SVM). In the standard C-SVM formulation of Cortes et al. (1995), the decision rule is encouraged to lie in the interval [1, \infty]. The new B-SVM objective function contains a penalty term that encourages the decision rule to lie in a user specif…
New method learns fair representations by separating out protected attributes.
problem Learning fair representations invariant to protected attributes.
method FD-VAE: disentangles latent space into target, protected, and mutual attributes.
result FD-VAE outperforms previous methods in fairness metrics.
MIM learns useful representations with high mutual information.
problem Learning useful representations for downstream tasks.
method Symmetric Jensen-Shannon divergence and mutual information regularizer in an encoder/decoder framework.
result MIM learns high mutual information representations without posterior collapse.
Powerful generative models, particularly in Natural Language Modelling, are commonly trained by maximizing a variational lower bound on the data log likelihood. These models often suffer from poor use of their latent variable, with ad-hoc annealing factors used to encourage retention of information in the latent variab…
Meta-learning curiosity algorithms improves exploration across various tasks.
problem Generating curious behavior in reinforcement learning.
method Meta-learning approach to adapt reward signals dynamically.
result Two novel curiosity algorithms outperform human-designed ones.
UKM framework optimizes VQCs, showing QCL performance is bounded.
problem Designing and optimizing variational quantum classifiers (VQCs).
method Unitary Kernel Method (UKM) and Variational Circuit Realization (VCR).
result QCL performance is bounded by UKM.
Novel segmentation method for energy game-theoretic frameworks using graphical lasso.
problem Difficulty in computing utility functions for high-player energy game-theoretic frameworks.
method Graphical Lasso based approach to cluster features leading to energy usage behaviors.
result Characteristic clusters demonstrating different energy usage behaviors identified.
Topic modeling enables exploration and compact representation of a corpus. The CaringBridge (CB) dataset is a massive collection of journals written by patients and caregivers during a health crisis. Topic modeling on the CB dataset, however, is challenging due to the asynchronous nature of multiple authors writing abo…
We make two theoretical contributions to disentanglement learning by (a) defining precise semantics of disentangled representations, and (b) establishing robust metrics for evaluation. First, we characterize the concept "disentangled representations" used in supervised and unsupervised methods along three dimensions-in…
Paper connects DP and optimization for RL, suggesting new algorithms.
problem Optimizing scalar objectives in RL.
method Drawing connections between DP and optimization algorithms.
result Links between DP schemes and optimization algorithms.