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Title: A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning Abstract: We present a rotated hyperbolic wrapped normal distribution (RoWN), a simple yet effective alteration of a hyperbolic wrapped normal distribution (HWN). The HWN expands the domain of probabilistic modeling from Eu... |
Title: BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning Abstract: Deep neural networks are vulnerable to Trojan attacks. Existing attacks use visible patterns (e.g., a patch or image transformations) as triggers, which are vulnerab... |
Title: Looking for Out-of-Distribution Environments in Critical Care: A case study with the eICU Database Abstract: Generalizing to new populations and domains in machine learning is still an open problem which has seen increased interest recently. In particular, clinical models show a significant performance drop when... |
Title: Your Transformer May Not be as Powerful as You Expect Abstract: Relative Positional Encoding (RPE), which encodes the relative distance between any pair of tokens, is one of the most successful modifications to the original Transformer. As far as we know, theoretical understanding of the RPE-based Transformers i... |
Title: A Fair Federated Learning Framework With Reinforcement Learning Abstract: Federated learning (FL) is a paradigm where many clients collaboratively train a model under the coordination of a central server, while keeping the training data locally stored. However, heterogeneous data distributions over different cli... |
Title: Avoiding Barren Plateaus with Classical Deep Neural Networks Abstract: Variational quantum algorithms (VQAs) are among the most promising algorithms in the era of Noisy Intermediate Scale Quantum Devices. The VQAs are applied to a variety of tasks, such as in chemistry simulations, optimization problems, and qua... |
Title: Machine Learning Models Are Not Necessarily Biased When Constructed Properly: Evidence from Neuroimaging Studies Abstract: Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distrib... |
Title: Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms Abstract: E-commerce is the fastest-growing segment of the economy. Online reviews play a crucial role in helping consumers evaluate and compare products and services. As a result, fake reviews (opinion spam) are becoming ... |
Title: The Neuro-Symbolic Brain Abstract: Neural networks promote a distributed representation with no clear place for symbols. Despite this, we propose that symbols are manufactured simply by training a sparse random noise as a self-sustaining attractor in a feedback spiking neural network. This way, we can generate m... |
Title: Principled Knowledge Extrapolation with GANs Abstract: Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions. To imitate this ability via generative models, previous works have extensively studied explicitly encoding Structural Causal Models (SCMs... |
Title: Mutual Information Divergence: A Unified Metric for Multimodal Generative Models Abstract: Text-to-image generation and image captioning are recently emerged as a new experimental paradigm to assess machine intelligence. They predict continuous quantity accompanied by their sampling techniques in the generation,... |
Title: Variance-Aware Sparse Linear Bandits Abstract: It is well-known that the worst-case minimax regret for sparse linear bandits is $\widetilde{\Theta}\left(\sqrt{dT}\right)$ where $d$ is the ambient dimension and $T$ is the number of time steps (ignoring the dependency on sparsity). On the other hand, in the benign... |
Title: Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback Abstract: We consider regret minimization for Adversarial Markov Decision Processes (AMDPs), where the loss functions are changing over time and adversarially chosen, and the learner only observes the losses for the visite... |
Title: Continual evaluation for lifelong learning: Identifying the stability gap Abstract: Introducing a time dependency on the data generating distribution has proven to be difficult for gradient-based training of neural networks, as the greedy updates result in catastrophic forgetting of previous timesteps. Continual... |
Title: AutoTSG: Learning and Synthesis for Incident Troubleshooting Abstract: Incident management is a key aspect of operating large-scale cloud services. To aid with faster and efficient resolution of incidents, engineering teams document frequent troubleshooting steps in the form of Troubleshooting Guides (TSGs), to ... |
Title: SigMaNet: One Laplacian to Rule Them All Abstract: This paper introduces SigMaNet, a generalized Graph Convolutional Network (GCN) capable of handling both undirected and directed graphs with weights not restricted in sign and magnitude. The cornerstone of SigMaNet is the introduction of a generalized Laplacian ... |
Title: FedAug: Reducing the Local Learning Bias Improves Federated Learning on Heterogeneous Data Abstract: Federated Learning (FL) is a machine learning paradigm that learns from data kept locally to safeguard the privacy of clients, whereas local SGD is typically employed on the clients' devices to improve communicat... |
Title: Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency Abstract: Reinforcement learning in partially observed Markov decision processes (POMDPs) faces two challenges. (i) It often takes the full history to predict the future, which induces a sample complexity that sc... |
Title: Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations Abstract: Modeling multivariate time series as temporal signals over a (possibly dynamic) graph is an effective representational framework that allows for developing models for time series analysis. In fact, discrete sequenc... |
Title: DeepJoint: Robust Survival Modelling Under Clinical Presence Shift Abstract: Observational data in medicine arise as a result of the complex interaction between patients and the healthcare system. The sampling process is often highly irregular and itself constitutes an informative process. When using such data t... |
Title: SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation Abstract: Conventional point cloud semantic segmentation methods usually employ an encoder-decoder architecture, where mid-level features are locally aggregated to extract geometric information. However, the over-reliance on these class-agno... |
Title: Sparse Graph Learning for Spatiotemporal Time Series Abstract: Outstanding achievements of graph neural networks for spatiotemporal time series prediction show that relational constraints introduce a positive inductive bias into neural forecasting architectures. Often, however, the relational information charact... |
Title: Mesoscopic modeling of hidden spiking neurons Abstract: Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved? Modeling the unobserved neurons with large pools of hidden spiking neurons leads to severely underconstra... |
Title: Censored Quantile Regression Neural Networks Abstract: This paper considers doing quantile regression on censored data using neural networks (NNs). This adds to the survival analysis toolkit by allowing direct prediction of the target variable, along with a distribution-free characterisation of uncertainty, usin... |
Title: An Analytic Framework for Robust Training of Artificial Neural Networks Abstract: The reliability of a learning model is key to the successful deployment of machine learning in various industries. Creating a robust model, particularly one unaffected by adversarial attacks, requires a comprehensive understanding ... |
Title: Are Transformers Effective for Time Series Forecasting? Abstract: Recently, there has been a surge of Transformer-based solutions for the time series forecasting (TSF) task, especially for the challenging long-term TSF problem. Transformer architecture relies on self-attention mechanisms to effectively extract t... |
Title: A framework for overparameterized learning Abstract: An explanation for the success of deep neural networks is a central question in theoretical machine learning. According to classical statistical learning, the overparameterized nature of such models should imply a failure to generalize. Many argue that good em... |
Title: Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling Abstract: Domain Adaptation (DA) has received widespread attention from deep learning researchers in recent years because of its potential to improve test accuracy with out-of-distribution labeled data. Most state-of-the-art DA algo... |
Title: Green Hierarchical Vision Transformer for Masked Image Modeling Abstract: We present an efficient approach for Masked Image Modeling (MIM) with hierarchical Vision Transformers (ViTs), e.g., Swin Transformer, allowing the hierarchical ViTs to discard masked patches and operate only on the visible ones. Our appro... |
Title: Transfer learning driven design optimization for inertial confinement fusion Abstract: Transfer learning is a promising approach to creating predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of sim... |
Title: Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality Abstract: Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations. In reinforcement learning, a set of diverse policies can be useful for expl... |
Title: Kernel Ridgeless Regression is Inconsistent in Low Dimensions Abstract: We show that kernel interpolation for a large class of shift-invariant kernels is inconsistent in fixed dimension, even with bandwidth adaptive to the training set. |
Title: Subspace clustering in high-dimensions: Phase transitions \& Statistical-to-Computational gap Abstract: A simple model to study subspace clustering is the high-dimensional $k$-Gaussian mixture model where the cluster means are sparse vectors. Here we provide an exact asymptotic characterization of the statistica... |
Title: TempoRL: Temporal Priors for Exploration in Off-Policy Reinforcement Learning Abstract: Efficient exploration is a crucial challenge in deep reinforcement learning. Several methods, such as behavioral priors, are able to leverage offline data in order to efficiently accelerate reinforcement learning on complex t... |
Title: Semantic Parsing of Interpage Relations Abstract: Page-level analysis of documents has been a topic of interest in digitization efforts, and multimodal approaches have been applied to both classification and page stream segmentation. In this work, we focus on capturing finer semantic relations between pages of a... |
Title: Training ReLU networks to high uniform accuracy is intractable Abstract: Statistical learning theory provides bounds on the necessary number of training samples needed to reach a prescribed accuracy in a learning problem formulated over a given target class. This accuracy is typically measured in terms of a gene... |
Title: Selective Classification Via Neural Network Training Dynamics Abstract: Selective classification is the task of rejecting inputs a model would predict incorrectly on through a trade-off between input space coverage and model accuracy. Current methods for selective classification impose constraints on either the ... |
Title: Verifying Learning-Based Robotic Navigation Systems Abstract: Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for various tasks in which complex policies are learned within reactive systems. In parallel, there has recently been significant research on verifying deep neural networks... |
Title: Mitigating barren plateaus of variational quantum eigensolvers Abstract: Variational quantum algorithms (VQAs) are expected to establish valuable applications on near-term quantum computers. However, recent works have pointed out that the performance of VQAs greatly relies on the capability of the ansatzes and i... |
Title: Revealing the Dark Secrets of Masked Image Modeling Abstract: Masked image modeling (MIM) as pre-training is shown to be effective for numerous vision downstream tasks, but how and where MIM works remain unclear. In this paper, we compare MIM with the long-dominant supervised pre-trained models from two perspect... |
Title: Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data Abstract: We propose a machine learning framework for the data-driven discovery of macroscopic chemotactic Partial Differential Equations (PDEs) -- and the closures that lead to them -- from high-fidelity, individ... |
Title: Training and Inference on Any-Order Autoregressive Models the Right Way Abstract: Conditional inference on arbitrary subsets of variables is a core problem in probabilistic inference with important applications such as masked language modeling and image inpainting. In recent years, the family of Any-Order Autore... |
Title: Unequal Covariance Awareness for Fisher Discriminant Analysis and Its Variants in Classification Abstract: Fisher Discriminant Analysis (FDA) is one of the essential tools for feature extraction and classification. In addition, it motivates the development of many improved techniques based on the FDA to adapt to... |
Title: Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment Abstract: Multi-armed bandit (MAB) is a classic model for understanding the exploration-exploitation trade-off. The traditional MAB model for recommendation systems assumes the user stays in the system for the entire learning horiz... |
Title: Learning Dialogue Representations from Consecutive Utterances Abstract: Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data scarcity. In this paper, we introduce Dialogue Sentence Embeddi... |
Title: Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations Abstract: Neural networks have achieved tremendous success in a large variety of applications. However, their memory footprint and computational demand can render them impractical in application settings with l... |
Title: Efficient Approximation of Gromov-Wasserstein Distance using Importance Sparsification Abstract: As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown the potential for the matching problems of structured data like point clouds and graphs. However, its application in practice is ... |
Title: Pruning has a disparate impact on model accuracy Abstract: Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy. This paper shows that pruning may create or exacerbate disparate impacts. The paper sheds light on the... |
Title: Predictor-corrector algorithms for stochastic optimization under gradual distribution shift Abstract: Time-varying stochastic optimization problems frequently arise in machine learning practice (e.g. gradual domain shift, object tracking, strategic classification). Although most problems are solved in discrete t... |
Title: Understanding new tasks through the lens of training data via exponential tilting Abstract: Deploying machine learning models to new tasks is a major challenge despite the large size of the modern training datasets. However, it is conceivable that the training data can be reweighted to be more representative of ... |
Title: Dynamic Network Reconfiguration for Entropy Maximization using Deep Reinforcement Learning Abstract: A key problem in network theory is how to reconfigure a graph in order to optimize a quantifiable objective. Given the ubiquity of networked systems, such work has broad practical applications in a variety of sit... |
Title: Learning in Feedback-driven Recurrent Spiking Neural Networks using full-FORCE Training Abstract: Feedback-driven recurrent spiking neural networks (RSNNs) are powerful computational models that can mimic dynamical systems. However, the presence of a feedback loop from the readout to the recurrent layer de-stabi... |
Title: Evolution of beliefs in social networks Abstract: Evolution of beliefs of a society are a product of interactions between people (horizontal transmission) in the society over generations (vertical transmission). Researchers have studied both horizontal and vertical transmission separately. Extending prior work, ... |
Title: Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes Abstract: We study offline reinforcement learning (RL) in partially observable Markov decision processes. In particular, we aim to learn an optimal policy from a dataset colle... |
Title: DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning Abstract: Predicted inter-chain residue-residue contacts can be used to build the quaternary structure of protein complexes from scratch. However, only a small number of methods have been developed to reconstruct protei... |
Title: VectorAdam for Rotation Equivariant Geometry Optimization Abstract: The rise of geometric problems in machine learning has necessitated the development of equivariant methods, which preserve their output under the action of rotation or some other transformation. At the same time, the Adam optimization algorithm ... |
Title: MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control Abstract: Embodied agents in continuous control domains have had limited exposure to tasks allowing to explore musculoskeletal properties that enable agile and nimble behaviors in biological beings. The sophistication behind neuro-musc... |
Title: Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods Abstract: Addressing the challenge of scaling-up epidemiological inference to complex and heterogeneous models, we introduce Poisson Approximate Likelihood (PAL) methods. In contrast to the popular ODE approa... |
Title: Tensor Program Optimization with Probabilistic Programs Abstract: Automatic optimization for tensor programs becomes increasingly important as we deploy deep learning in various environments, and efficient optimization relies on a rich search space and effective search. Most existing efforts adopt a search space... |
Title: Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets Abstract: Detailed mobile sensing data from phones, watches, and fitness trackers offer an unparalleled opportunity to quantify and act upon previously unmeasurable behavioral changes in order t... |
Title: Circumventing Backdoor Defenses That Are Based on Latent Separability Abstract: Deep learning models are vulnerable to backdoor poisoning attacks. In particular, adversaries can embed hidden backdoors into a model by only modifying a very small portion of its training data. On the other hand, it has also been co... |
Title: Emergent organization of receptive fields in networks of excitatory and inhibitory neurons Abstract: Local patterns of excitation and inhibition that can generate neural waves are studied as a computational mechanism underlying the organization of neuronal tunings. Sparse coding algorithms based on networks of e... |
Title: Fight Poison with Poison: Detecting Backdoor Poison Samples via Decoupling Benign Correlations Abstract: In this work, we study poison samples detection for defending against backdoor poisoning attacks on deep neural networks (DNNs). A principled idea underlying prior arts on this problem is to utilize the backd... |
Title: Approximate Q-learning and SARSA(0) under the $\epsilon$-greedy Policy: a Differential Inclusion Analysis Abstract: Q-learning and SARSA(0) with linear function approximation, under $\epsilon$-greedy exploration, are leading methods to estimate the optimal policy in Reinforcement Learning (RL). It has been empir... |
Title: Denial-of-Service Attack on Object Detection Model Using Universal Adversarial Perturbation Abstract: Adversarial attacks against deep learning-based object detectors have been studied extensively in the past few years. The proposed attacks aimed solely at compromising the models' integrity (i.e., trustworthines... |
Title: Fairness in Recommendation: A Survey Abstract: As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommen... |
Title: Differentially Private Decoding in Large Language Models Abstract: Recent large-scale natural language processing (NLP) systems use a pre-trained Large Language Model (LLM) on massive and diverse corpora as a headstart. In practice, the pre-trained model is adapted to a wide array of tasks via fine-tuning on tas... |
Title: A Hybrid Neural Autoencoder for Sensory Neuroprostheses and Its Applications in Bionic Vision Abstract: Sensory neuroprostheses are emerging as a promising technology to restore lost sensory function or augment human capacities. However, sensations elicited by current devices often appear artificial and distorte... |
Title: Faster Optimization on Sparse Graphs via Neural Reparametrization Abstract: In mathematical optimization, second-order Newton's methods generally converge faster than first-order methods, but they require the inverse of the Hessian, hence are computationally expensive. However, we discover that on sparse graphs,... |
Title: BagFlip: A Certified Defense against Data Poisoning Abstract: Machine learning models are vulnerable to data-poisoning attacks, in which an attacker maliciously modifies the training set to change the prediction of a learned model. In a trigger-less attack, the attacker can modify the training set but not the te... |
Title: RIGID: Robust Linear Regression with Missing Data Abstract: We present a robust framework to perform linear regression with missing entries in the features. By considering an elliptical data distribution, and specifically a multivariate normal model, we are able to conditionally formulate a distribution for the ... |
Title: Quark: Controllable Text Generation with Reinforced Unlearning Abstract: Large-scale language models often learn behaviors that are misaligned with user expectations. Generated text may contain offensive or toxic language, contain significant repetition, or be of a different sentiment than desired by the user. W... |
Title: A Model Predictive Control Functional Continuous Time Bayesian Network for Self-Management of Multiple Chronic Conditions Abstract: Multiple chronic conditions (MCC) are one of the biggest challenges of modern times. The evolution of MCC follows a complex stochastic process that is influenced by a variety of ris... |
Title: Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures Abstract: This paper considers the Pointer Value Retrieval (PVR) benchmark introduced in [ZRKB21], where a 'reasoning' function acts on a string of digits to produce the label. More generally, the paper considers the learni... |
Title: A Unified Analysis of Federated Learning with Arbitrary Client Participation Abstract: Federated learning (FL) faces challenges of intermittent client availability and computation/communication efficiency. As a result, only a small subset of clients can participate in FL at a given time. It is important to under... |
Title: Mixed Federated Learning: Joint Decentralized and Centralized Learning Abstract: Federated learning (FL) enables learning from decentralized privacy-sensitive data, with computations on raw data confined to take place at edge clients. This paper introduces mixed FL, which incorporates an additional loss term cal... |
Title: Contextual Adapters for Personalized Speech Recognition in Neural Transducers Abstract: Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR) models is a challenge due to the lack of training data. A standard way to address this issue is with shallow fusion methods at inference time... |
Title: Explaining Preferences with Shapley Values Abstract: While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored. In this paper, we propose \textsc{Pref-SHAP}, a Shapley value-based model explanation framework for pair... |
Title: Transformer for Partial Differential Equations' Operator Learning Abstract: Data-driven learning of partial differential equations' solution operators has recently emerged as a promising paradigm for approximating the underlying solutions. The solution operators are usually parameterized by deep learning models ... |
Title: Global Normalization for Streaming Speech Recognition in a Modular Framework Abstract: We introduce the Globally Normalized Autoregressive Transducer (GNAT) for addressing the label bias problem in streaming speech recognition. Our solution admits a tractable exact computation of the denominator for the sequence... |
Title: Reinforcement Learning Approach for Mapping Applications to Dataflow-Based Coarse-Grained Reconfigurable Array Abstract: The Streaming Engine (SE) is a Coarse-Grained Reconfigurable Array which provides programming flexibility and high-performance with energy efficiency. An application program to be executed on ... |
Title: Fast variable selection makes scalable Gaussian process BSS-ANOVA a speedy and accurate choice for tabular and time series regression Abstract: Gaussian processes (GPs) are non-parametric regression engines with a long history. They are often overlooked in modern machine learning contexts because of scalability ... |
Title: SeedGNN: Graph Neural Networks for Supervised Seeded Graph Matching Abstract: Recently, there have been significant interests in designing Graph Neural Networks (GNNs) for seeded graph matching, which aims to match two (unlabeled) graphs using only topological information and a small set of seeds. However, most ... |
Title: Membership Inference Attack Using Self Influence Functions Abstract: Member inference (MI) attacks aim to determine if a specific data sample was used to train a machine learning model. Thus, MI is a major privacy threat to models trained on private sensitive data, such as medical records. In MI attacks one may ... |
Title: Learning with Stochastic Orders Abstract: Learning high-dimensional distributions is often done with explicit likelihood modeling or implicit modeling via minimizing integral probability metrics (IPMs). In this paper, we expand this learning paradigm to stochastic orders, namely, the convex or Choquet order betw... |
Title: Asymptotic Convergence Rate and Statistical Inference for Stochastic Sequential Quadratic Programming Abstract: We apply a stochastic sequential quadratic programming (StoSQP) algorithm to solve constrained nonlinear optimization problems, where the objective is stochastic and the constraints are deterministic. ... |
Title: Safety Aware Changepoint Detection for Piecewise i.i.d. Bandits Abstract: In this paper, we consider the setting of piecewise i.i.d. bandits under a safety constraint. In this piecewise i.i.d. setting, there exists a finite number of changepoints where the mean of some or all arms change simultaneously. We intro... |
Title: FedAvg with Fine Tuning: Local Updates Lead to Representation Learning Abstract: The Federated Averaging (FedAvg) algorithm, which consists of alternating between a few local stochastic gradient updates at client nodes, followed by a model averaging update at the server, is perhaps the most commonly used method ... |
Title: FedFormer: Contextual Federation with Attention in Reinforcement Learning Abstract: A core issue in federated reinforcement learning is defining how to aggregate insights from multiple agents into one. This is commonly done by taking the average of each participating agent's model weights into one common model (... |
Title: Maximum Likelihood Training of Implicit Nonlinear Diffusion Models Abstract: Whereas diverse variations of diffusion models exist, expanding the linear diffusion into a nonlinear diffusion process is investigated only by a few works. The nonlinearity effect has been hardly understood, but intuitively, there woul... |
Title: ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting Abstract: Graph Neural Networks (GNNs) have achieved enormous success in tackling analytical problems on graph data. Most GNNs interpret nearly all the node connections as inductive bias with feature smoothness, and implicitly assume... |
Title: Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters Abstract: Motivated by the success of ensembles for uncertainty estimation in supervised learning, we take a renewed look at how ensembles of $Q$-functions can be leveraged as the primary source of p... |
Title: DP-PCA: Statistically Optimal and Differentially Private PCA Abstract: We study the canonical statistical task of computing the principal component from $n$ i.i.d.~data in $d$ dimensions under $(\varepsilon,\delta)$-differential privacy. Although extensively studied in literature, existing solutions fall short o... |
Title: Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss Abstract: A central issue in machine learning is how to train models on sensitive user data. Industry has widely adopted a simple algorithm: Stochastic Gradient Descent with noise (a.k.a. Stochastic Gradient Langevin Dynamics... |
Title: Incorporating the Barzilai-Borwein Adaptive Step Size into Sugradient Methods for Deep Network Training Abstract: In this paper, we incorporate the Barzilai-Borwein step size into gradient descent methods used to train deep networks. This allows us to adapt the learning rate using a two-point approximation to th... |
Title: Hazard Gradient Penalty for Survival Analysis Abstract: Survival analysis appears in various fields such as medicine, economics, engineering, and business. Recent studies showed that the Ordinary Differential Equation (ODE) modeling framework unifies many existing survival models while the framework is flexible ... |
Title: Off-Beat Multi-Agent Reinforcement Learning Abstract: We investigate model-free multi-agent reinforcement learning (MARL) in environments where off-beat actions are prevalent, i.e., all actions have pre-set execution durations. During execution durations, the environment changes are influenced by, but not synchr... |
Title: Effective Abstract Reasoning with Dual-Contrast Network Abstract: As a step towards improving the abstract reasoning capability of machines, we aim to solve Raven's Progressive Matrices (RPM) with neural networks, since solving RPM puzzles is highly correlated with human intelligence. Unlike previous methods tha... |
Title: Can Foundation Models Help Us Achieve Perfect Secrecy? Abstract: A key promise of machine learning is the ability to assist users with personal tasks. Because the personal context required to make accurate predictions is often sensitive, we require systems that protect privacy. A gold standard privacy-preserving... |
Title: On Consistency in Graph Neural Network Interpretation Abstract: Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies up... |
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