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Found 139 papers, 32 papers with code
Deep Reinforcement Learning with Smooth Policy
In contrast to policy parameterized by linear/reproducing kernel functions, where simple regularization techniques suffice to control smoothness, for neural network based reinforcement learning algorithms, there is no readily available solution to learn a smooth policy.
NoWag: A Unified Framework for Shape Preserving Compression of Large Language Models
Large language models (LLMs) exhibit remarkable performance across various natural language processing tasks but suffer from immense computational and memory demands, limiting their deployment in resource-constrained environments.
IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining
We study the enlarge-and-prune pipeline as an integrated system to address two critical questions: whether it is worth pretraining an enlarged model even when the model is never deployed, and how to optimize the entire pipeline for better pruned models.
A Minimalist Example of Edge-of-Stability and Progressive Sharpening
Through this model, we rigorously prove the existence of progressive sharpening and self-stabilization under large learning rates, and establish non-asymptotic analysis of the training dynamics and sharpness along the entire GD trajectory.
COSMOS: A Hybrid Adaptive Optimizer for Memory-Efficient Training of LLMs
In this paper, we propose COSMOS, a novel hybrid optimizer that leverages the varying importance of eigensubspaces in the gradient matrix to achieve memory efficiency without compromising optimization performance.
RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization
Large language models (LLMs) have achieved impressive performance but face high computational costs and latency, limiting their deployment in resource-constrained settings.
Provable Acceleration of Nesterov's Accelerated Gradient for Rectangular Matrix Factorization and Linear Neural Networks
We study the convergence rate of first-order methods for rectangular matrix factorization, which is a canonical nonconvex optimization problem.
Model Tells Itself Where to Attend: Faithfulness Meets Automatic Attention Steering
However, they often struggle to fully comprehend and effectively utilize their input contexts, resulting in responses that are unfaithful or hallucinated.
RNR: Teaching Large Language Models to Follow Roles and Rules
However, existing models trained on open-source IFT datasets only have the ability to follow instructions from users, and often fail to follow complex role and rules specified by developers, a. k. a.
Robust Reinforcement Learning from Corrupted Human Feedback
To tackle this challenge, we propose a robust RLHF approach -- $R^3M$, which models the potentially corrupted preference label as sparse outliers.
RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning
By adding a sparsity constraint on the product of low-rank matrices and converting it to row and column-wise sparsity, we ensure efficient and precise model updates.
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback
By incorporating an adaptive scaling parameter into the loss for each pair, our method increases the flexibility of the reward function.
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO
In this paper, we present a principled framework for learning a small yet generalizable temperature prediction network (TempNet) to improve LFMs.
Stochastic Constrained Decentralized Optimization for Machine Learning with Fewer Data Oracles: a Gradient Sliding Approach
In modern decentralized applications, ensuring communication efficiency and privacy for the users are the key challenges.
GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM
Key-value (KV) caching has become the de-facto to accelerate generation speed for large language models (LLMs) inference.
BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering
Retrieval-augmented Large Language Models (LLMs) offer substantial benefits in enhancing performance across knowledge-intensive scenarios.
Data Diversity Matters for Robust Instruction Tuning
QDIT provides a simple method to simultaneously control dataset diversity and quality, allowing us to conduct an in-depth study on the effect of diversity and quality on instruction tuning performance.
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
In human-written articles, we often leverage the subtleties of text style, such as bold and italics, to guide the attention of readers.
Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult
This regularity, together with gradient descent using a large learning rate that favors flatter regions, results in these nontrivial dynamical behaviors.
Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification
Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of marked STPPs through score-matching and offers uncertainty quantification for the predicted event time, location and mark by computing confidence regions over the generated samples.
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process
We conduct extensive experiments in both event type prediction and uncertainty quantification of arrival time.
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer
These models leverage the attention mechanism to capture long- and short-range dependencies in the sequence.
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Quantization is an indispensable technique for serving Large Language Models (LLMs) and has recently found its way into LoRA fine-tuning.
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds
As a result, by properly choosing the network size and hyperparameters, NPMD can find an $\epsilon$-optimal policy with $\widetilde{O}(\epsilon^{-\frac{d}{\alpha}-2})$ samples in expectation, where $\alpha\in(0, 1]$ indicates the smoothness of environment.
Pivotal Estimation of Linear Discriminant Analysis in High Dimensions
We consider the linear discriminant analysis problem in the high-dimensional settings.
Deep Reinforcement Learning from Hierarchical Preference Design
Researchers typically utilize feedback signals from the environment to handcraft a reward function, but this process is not always effective due to the varying scale and intricate dependencies of the feedback signals.
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems
A popular approach is to utilize human feedback to learn a reward function for training.
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks
Convolutional residual neural networks (ConvResNets), though overparameterized, can achieve remarkable prediction performance in practice, which cannot be well explained by conventional wisdom.
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories
Existing theories on deep nonparametric regression have shown that when the input data lie on a low-dimensional manifold, deep neural networks can adapt to the intrinsic data structures.
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation
Pruning enhances the diversity of low-rank approximations, and low-rank approximation prevents pruning from losing too many expressive neurons.
Machine Learning Force Fields with Data Cost Aware Training
To address this issue, we propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data.
AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
Therefore, many fine-tuning methods are proposed to learn incremental updates of pre-trained weights in a parameter efficient way, e. g., low-rank increments.
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds
Many existing experiments have demonstrated that generative networks can generate high-dimensional complex data from a low-dimensional easy-to-sample distribution.
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers
Since the teacher model has a significantly larger capacity and stronger representation power than the student model, it is very difficult for the student to produce predictions that match the teacher's over a massive amount of open-domain training data.
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data
Furthermore, the generated distribution based on the estimated score function captures the data geometric structures and converges to a close vicinity of the data distribution.
Efficient Long Sequence Modeling via State Space Augmented Transformer
Specifically, we augment a SSM into the bottom layer of SPADE, and we employ efficient local attention methods for the other layers.
High Dimensional Binary Classification under Label Shift: Phase Transition and Regularization
Label Shift has been widely believed to be harmful to the generalization performance of machine learning models.
Less is More: Task-aware Layer-wise Distillation for Language Model Compression
As such, TED reduces the knowledge gap between the two models and helps the student to fit better on the target task.
First-order Policy Optimization for Robust Markov Decision Process
We consider the problem of solving robust Markov decision process (MDP), which involves a set of discounted, finite state, finite action space MDPs with uncertain transition kernels.
Differentially Private Estimation of Hawkes Process
Point process models are of great importance in real world applications.
Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites
The model subsequently calculates session representations by combining the contextual information with the instant search query using an aggregation network.
DiP-GNN: Discriminative Pre-Training of Graph Neural Networks
Specifically, we train a generator to recover identities of the masked edges, and simultaneously, we train a discriminator to distinguish the generated edges from the original graph's edges.
PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance
Large Transformer-based models have exhibited superior performance in various natural language processing and computer vision tasks.
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint
Overparameterized neural networks enjoy great representation power on complex data, and more importantly yield sufficiently smooth output, which is crucial to their generalization and robustness.
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks
We consider the off-policy evaluation problem of reinforcement learning using deep convolutional neural networks.
A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks
Based on the approximation theory of neural networks, we show that the neural network IPM test has the type-II risk in the order of $n^{-(s+\beta)/d}$, which is in the same order of the type-II risk as the H\"older IPM test.
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation
We propose MoEBERT, which uses a Mixture-of-Experts structure to increase model capacity and inference speed.
CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing
To retain ensemble benefits while maintaining a low memory cost, we propose a consistency-regularized ensemble learning approach based on perturbed models, named CAMERO.
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data
User sessions empower many search and recommendation tasks on a daily basis.
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably
We investigate the role of noise in optimization algorithms for learning over-parameterized models.
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models
Analysis shows that the proposed schedule indeed reduces the redundancy and improves generalization performance.
Homotopic Policy Mirror Descent: Policy Convergence, Implicit Regularization, and Improved Sample Complexity
We first establish the global linear convergence of HPMD instantiated with Kullback-Leibler divergence, for both the optimality gap, and a weighted distance to the set of optimal policies.
Block Policy Mirror Descent
Despite the nonconvex nature of the problem and a partial update rule, we provide a unified analysis for several sampling schemes, and show that BPMD achieves fast linear convergence to the global optimality.
Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network
This paper presents a deep learning assisted synthesis approach for direct end-to-end generation of RF/mm-wave passive matching network with 3D EM structures.
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces
Learning operators between infinitely dimensional spaces is an important learning task arising in wide applications in machine learning, imaging science, mathematical modeling and simulations, etc.
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL
Theoretically, under a weak coverage assumption that the experience dataset contains enough information about the optimal policy, we prove that for an episodic mean-field MDP with a horizon $H$ and $N$ training trajectories, SAFARI attains a sub-optimality gap of $\mathcal{O}(H^2d_{\rm eff} /\sqrt{N})$, where $d_{\rm eff}$ is the effective dimension of the function class for parameterizing the value function, but independent on the number of agents.
Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits
We show that incorporating frequency information of tokens in the embedding learning problems leads to provably efficient algorithms, and demonstrate that common adaptive algorithms implicitly exploit the frequency information to a large extent.
Taming Sparsely Activated Transformer with Stochastic Experts
While most on-going research focuses on improving SAMs models by exploring methods of routing inputs to experts, our analysis reveals that such research might not lead to the solution we expect, i. e., the commonly-used routing methods based on gating mechanisms do not work better than randomly routing inputs to experts.
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
Moreover, we rigorously establish an implicit bias of GD induced by such a large learning rate, termed 'balancing', meaning that magnitudes of $X$ and $Y$ at the limit of GD iterations will be close even if their initialization is significantly unbalanced.
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning
To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation- invariant actor-critic neural architecture.
Adversarially Regularized Policy Learning Guided by Trajectory Optimization
Recent advancement in combining trajectory optimization with function approximation (especially neural networks) shows promise in learning complex control policies for diverse tasks in robot systems.
ARCH: Efficient Adversarial Regularized Training with Caching
Adversarial regularization can improve model generalization in many natural language processing tasks.
Self-Training with Differentiable Teacher
In self-training, the student contributes to the prediction performance, and the teacher controls the training process by generating pseudo-labels.
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks
Most of existing statistical theories on deep neural networks have sample complexities cursed by the data dimension and therefore cannot well explain the empirical success of deep learning on high-dimensional data.
QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction
We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms.
Implicit Regularization of Bregman Proximal Point Algorithm and Mirror Descent on Separable Data
For any BPPA instantiated with a fixed Bregman divergence, we provide a lower bound of the margin obtained by BPPA with respect to an arbitrarily chosen norm.
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data
Unfortunately, we observe that weakly labeled data does not necessarily improve, or even deteriorate the model performance (due to the extensive noise in the weak labels) when we train deep NER models over a simple or weighted combination of the strongly labeled and weakly labeled data.
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization
The Lottery Ticket Hypothesis suggests that an over-parametrized network consists of ``lottery tickets'', and training a certain collection of them (i. e., a subnetwork) can match the performance of the full model.
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural architecture.
COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction
The global spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has cast a significant threat to mankind.
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach
Adversarial regularization has been shown to improve the generalization performance of deep learning models in various natural language processing tasks.
Token-wise Curriculum Learning for Neural Machine Translation
Existing curriculum learning approaches to Neural Machine Translation (NMT) require sampling sufficient amounts of "easy" samples from training data at the early training stage.
Reinforcement Learning for Adaptive Mesh Refinement
Large-scale finite element simulations of complex physical systems governed by partial differential equations (PDE) crucially depend on adaptive mesh refinement (AMR) to allocate computational budget to regions where higher resolution is required.
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization
Numerous empirical evidences have corroborated the importance of noise in nonconvex optimization problems.
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach
An ideal environment for evaluating dialog systems, also known as the Turing test, needs to involve human interaction, which is usually not affordable for large-scale experiments.
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective
We then compare the kernel of deep ResNets with that of deep FFNets and discover that the class of functions induced by the kernel of FFNets is asymptotically not learnable, as the depth goes to infinity.
Differentiable Top-k with Optimal Transport
Finding the k largest or smallest elements from a collection of scores, i. e., top-k operation, is an important model component widely used in information retrieval, machine learning, and data mining.
A Hypergradient Approach to Robust Regression without Correspondence
Due to the combinatorial nature of the problem, most existing methods are only applicable when the sample size is small, and limited to linear regression models.
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks
Our theory shows that deep neural networks are adaptive to the low-dimensional geometric structures of the covariates, and partially explains the success of deep learning for causal inference.
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
Fine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization.
Differentiable Top-$k$ with Optimal Transport
The top-$k$ operation, i. e., finding the $k$ largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining.
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
To address this problem, we develop a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision.
Ranked #1 on
Word Sense Disambiguation
on Words in Context
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.
Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers
We propose using machine learning models for the direct synthesis of on-chip electromagnetic (EM) passive structures to enable rapid or even automated designs and optimizations of RF/mm-Wave circuits.
BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision
We study the open-domain named entity recognition (NER) problem under distant supervision.
The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R
This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, $\ell_q$ Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME).
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python
We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e. g., sparse linear regression, sparse logistic regression, sparse Poisson regression and scaled sparse linear regression) combined with efficient active set selection strategies.
The huge Package for High-dimensional Undirected Graph Estimation in R
We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data.
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 $\tilde{O}(d^{p-1})$.
Implicit Bias of Gradient Descent based Adversarial Training on Separable Data
Specifically, we show that for any fixed iteration $T$, when the adversarial perturbation during training has proper bounded L2 norm, the classifier learned by gradient descent based adversarial training converges in direction to the maximum L2 norm margin classifier at the rate of $O(1/\sqrt{T})$, significantly faster than the rate $O(1/\log T}$ of training with clean data.
Deep Reinforcement Learning with Robust and Smooth Policy
Deep reinforcement learning (RL) has achieved great empirical successes in various domains.
Transformer Hawkes Process
Modern data acquisition routinely produce massive amounts of event sequence data in various domains, such as social media, healthcare, and financial markets.
Differentiable Top-k Operator with Optimal Transport
The top-k operation, i. e., finding the k largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining.
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -- A Neural Tangent Kernel Perspective
We then compare the kernel of deep ResNets with that of deep FFNets and discover that the class of functions induced by the kernel of FFNets is asymptotically not learnable, as the depth goes to infinity.
Distribution Approximation and Statistical Estimation Guarantees of Generative Adversarial Networks
Generative Adversarial Networks (GANs) have achieved a great success in unsupervised learning.
On Computation and Generalization of Generative Adversarial Imitation Learning
Generative Adversarial Imitation Learning (GAIL) is a powerful and practical approach for learning sequential decision-making policies.
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds
The network size scales exponentially in the approximation error, with an exponent depending on the intrinsic dimension of the data and the smoothness of the function.
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
However, due to limited data resources from downstream tasks and the extremely large capacity of pre-trained models, aggressive fine-tuning often causes the adapted model to overfit the data of downstream tasks and forget the knowledge of the pre-trained model.
Ranked #1 on
Natural Language Inference
on QNLI
Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing
To overcome this limitation, we propose a novel multi-domain NMT model using individual modules for each domain, on which we apply word-level, adaptive and layer-wise domain mixing.
On Generalization Bounds of a Family of Recurrent Neural Networks
We remark: (1) Our generalization bound for vanilla RNNs is significantly tighter than the best of existing results; (2) We are not aware of any other generalization bounds for MGU, LSTM, and Conv RNNs in the exiting literature; (3) We demonstrate the advantages of these variants in generalization.
Towards Understanding the Importance of Shortcut Connections in Residual Networks
We show, however, that gradient descent combined with proper normalization, avoids being trapped by the spurious local optimum, and converges to a global optimum in polynomial time, when the weight of the first layer is initialized at 0, and that of the second layer is initialized arbitrarily in a ball.
Towards Understanding the Importance of Noise in Training Neural Networks
Numerous empirical evidence has corroborated that the noise plays a crucial rule in effective and efficient training of neural networks.
Meta Learning with Relational Information for Short Sequences
This paper proposes a new meta-learning method -- named HARMLESS (HAwkes Relational Meta LEarning method for Short Sequences) for learning heterogeneous point process models from short event sequence data along with a relational network.
Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks : Function Approximation and Statistical Recovery
It therefore demonstrates the adaptivity of deep ReLU networks to low-dimensional geometric structures of data, and partially explains the power of deep ReLU networks in tackling high-dimensional data with low-dimensional geometric structures.
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data
Specifically, we show that when the adversarial perturbation during training has bounded $\ell_2$-norm, the classifier learned by gradient descent based adversarial training converges in direction to the maximum $\ell_2$-norm margin classifier at the rate of $\tilde{\mathcal{O}}(1/\sqrt{T})$, significantly faster than the rate $\mathcal{O}(1/\log T)$ of training with clean data.
On Scalable and Efficient Computation of Large Scale Optimal Transport
Optimal Transport (OT) naturally arises in many machine learning applications, yet the heavy computational burden limits its wide-spread uses.
On Computation and Generalization of Generative Adversarial Networks under Spectrum Control
Generative Adversarial Networks (GANs), though powerful, is hard to train.
On Tighter Generalization Bounds for Deep Neural Networks: CNNs, ResNets, and Beyond
We propose a generalization error bound for a general family of deep neural networks based on the depth and width of the networks, as well as the spectral norm of weight matrices.
Learning to Defense by Learning to Attack
From the perspective of generative learning, our proposed method can be viewed as learning a deep generative model for generating adversarial samples, which is adaptive to the robust classification.
On Computation and Generalization of GANs with Spectrum Control
Specifically, we propose a new reparameterization approach for the weight matrices of the discriminator in GANs, which allows us to directly manipulate the spectra of the weight matrices through various regularizers and constraints, without intensively computing singular value decompositions.
Learning to Defend by Learning to Attack
Adversarial training provides a principled approach for training robust neural networks.
Provable Gaussian Embedding with One Observation
In this paper, we study the Gaussian embedding model and develop the first theoretical results for exponential family embedding models.
On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization
However, due to the lack of convexity, their landscape is not well understood and how to find the stable equilibria of the Lagrangian function is still unknown.
On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond
We establish a margin based data dependent generalization error bound for a general family of deep neural networks in terms of the depth and width, as well as the Jacobian of the networks.
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization
Asynchronous momentum stochastic gradient descent algorithms (Async-MSGD) is one of the most popular algorithms in distributed machine learning.
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models
We propose a nonparametric method for detecting nonlinear causal relationship within a set of multidimensional discrete time series, by using sparse additive models (SpAMs).
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization
Specifically, our goal is to estimate the principle component of time series data with respect to the covariance matrix of the stationary distribution.
A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization
Our theoretical discovery partially corroborates the empirical success of MSGD in training deep neural networks.
Misspecified Nonconvex Statistical Optimization for Phase Retrieval
Existing nonconvex statistical optimization theory and methods crucially rely on the correct specification of the underlying "true" statistical models.
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning
We propose a DC proximal Newton algorithm for solving nonconvex regularized sparse learning problems in high dimensions.
Parametric Simplex Method for Sparse Learning
High dimensional sparse learning has imposed a great computational challenge to large scale data analysis.
Deep Hyperspherical Learning
In light of such challenges, we propose hyperspherical convolution (SphereConv), a novel learning framework that gives angular representations on hyperspheres.
Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability
Multiview representation learning is popular for latent factor analysis.
On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Sparse Learning in High Dimensions
We propose a DC proximal Newton algorithm for solving nonconvex regularized sparse learning problems in high dimensions.
Online Factorization and Partition of Complex Networks From Random Walks
We formulate this into a nonconvex stochastic factorization problem and propose an efficient and scalable stochastic generalized Hebbian algorithm.
Homotopy Parametric Simplex Method for Sparse Learning
High dimensional sparse learning has imposed a great computational challenge to large scale data analysis.
Dropping Convexity for More Efficient and Scalable Online Multiview Learning
Multiview representation learning is very popular for latent factor analysis.
Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization
We propose a general theory for studying the \xl{landscape} of nonconvex \xl{optimization} with underlying symmetric structures \tz{for a class of machine learning problems (e. g., low-rank matrix factorization, phase retrieval, and deep linear neural networks)}.
The Physical Systems Behind Optimization Algorithms
We use differential equations based approaches to provide some {\it \textbf{physics}} insights into analyzing the dynamics of popular optimization algorithms in machine learning.
On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization
In particular, we first show that for a family of quadratic minimization problems, the iteration complexity $\mathcal{O}(\log^2(p)\cdot\log(1/\epsilon))$ of the CBCD-type methods matches that of the GD methods in term of dependency on $p$, up to a $\log^2 p$ factor.
NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization
We study a stochastic and distributed algorithm for nonconvex problems whose objective consists of a sum of $N$ nonconvex $L_i/N$-smooth functions, plus a nonsmooth regularizer.
On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About Its Nonsmooth Loss Function
Many machine learning techniques sacrifice convenient computational structures to gain estimation robustness and modeling flexibility.
Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction
We propose a stochastic variance reduced optimization algorithm for solving sparse learning problems with cardinality constraints.
A Nonconvex Optimization Framework for Low Rank Matrix Estimation
We study the estimation of low rank matrices via nonconvex optimization.
Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory
This is the first result on the computational and statistical guarantees of the pathwise coordinate optimization framework in high dimensions.
Accelerated Mini-batch Randomized Block Coordinate Descent Method
When the regularization function is block separable, we can solve the minimization problems in a randomized block coordinate descent (RBCD) manner.
Multivariate Regression with Calibration
We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models.
Sparse Inverse Covariance Estimation with Calibration
We propose a semiparametric procedure for estimating high dimensional sparse inverse covariance matrix.
Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery
We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models.
