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Found 69 papers, 15 papers with code
Second Order Methods for Bandit Optimization and Control
We show that our algorithm achieves optimal (in terms of horizon) regret bounds for a large class of convex functions that we call $\kappa$-convex.
HiRE: High Recall Approximate Top-$k$ Estimation for Efficient LLM Inference
Autoregressive decoding with generative Large Language Models (LLMs) on accelerators (GPUs/TPUs) is often memory-bound where most of the time is spent on transferring model parameters from high bandwidth memory (HBM) to cache.
Tandem Transformers for Inference Efficient LLMs
On the PaLM2 pretraining dataset, a tandem of PaLM2-Bison and PaLM2-Gecko demonstrates a 3. 3% improvement in next-token prediction accuracy over a standalone PaLM2-Gecko, offering a 1. 16x speedup compared to a PaLM2-Otter model with comparable downstream performance.
The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks
In this paper, we introduce a key notion to predict and control feature learning: the angle $\theta_\ell$ between the feature updates and the backward pass (at layer index $\ell$).
Near Optimal Heteroscedastic Regression with Symbiotic Learning
Our work shows that we can estimate $\mathbf{w}^{*}$ in squared norm up to an error of $\tilde{O}\left(\|\mathbf{f}^{*}\|^2 \cdot \left(\frac{1}{n} + \left(\frac{d}{n}\right)^2\right)\right)$ and prove a matching lower bound (upto log factors).
Consistent Multiclass Algorithms for Complex Metrics and Constraints
We present consistent algorithms for multiclass learning with complex performance metrics and constraints, where the objective and constraints are defined by arbitrary functions of the confusion matrix.
Multi-User Reinforcement Learning with Low Rank Rewards
In this work, we consider the problem of collaborative multi-user reinforcement learning.
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks
To bridge the gap between these two lines of work, we first hypothesize and verify that while SB may not altogether preclude learning complex features, it amplifies simpler features over complex ones.
Optimistic MLE -- A Generic Model-based Algorithm for Partially Observable Sequential Decision Making
We prove that OMLE learns the near-optimal policies of an enormously rich class of sequential decision making problems in a polynomial number of samples.
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization
We consider the problem of OOD generalization, where the goal is to train a model that performs well on test distributions that are different from the training distribution.
All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP)
Compared to standard multilabel baselines, CHAMP provides improved AUPRC in both robustness (8. 87% mean percentage improvement ) and less data regimes.
MET: Masked Encoding for Tabular Data
Typical contrastive learning based SSL methods require instance-wise data augmentations which are difficult to design for unstructured tabular data.
Reproducibility in Optimization: Theoretical Framework and Limits
We initiate a formal study of reproducibility in optimization.
Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness
We study the complexity of optimizing highly smooth convex functions.
Statistically and Computationally Efficient Linear Meta-representation Learning
To cope with such data scarcity, meta-representation learning methods train across many related tasks to find a shared (lower-dimensional) representation of the data where all tasks can be solved accurately.
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs
The starting point of our work is the observation that in practice, Q-learning is used with two important modifications: (i) training with two networks, called online network and target network simultaneously (online target learning, or OTL) , and (ii) experience replay (ER) (Mnih et al., 2015).
Focus on the Common Good: Group Distributional Robustness Follows
We consider the problem of training a classification model with group annotated training data.
Minimax Optimization with Smooth Algorithmic Adversaries
This paper considers minimax optimization $\min_x \max_y f(x, y)$ in the challenging setting where $f$ can be both nonconvex in $x$ and nonconcave in $y$.
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems
In this work, we improve existing results for learning nonlinear systems in a number of ways: a) we provide the first offline algorithm that can learn non-linear dynamical systems without the mixing assumption, b) we significantly improve upon the sample complexity of existing results for mixing systems, c) in the much harder one-pass, streaming setting we study a SGD with Reverse Experience Replay ($\mathsf{SGD-RER}$) method, and demonstrate that for mixing systems, it achieves the same sample complexity as our offline algorithm, d) we justify the expansivity assumption by showing that for the popular ReLU link function -- a non-expansive but easy to learn link function with i. i. d.
Sample Efficient Linear Meta-Learning by Alternating Minimization
We show that, for a constant subspace dimension MLLAM obtains nearly-optimal estimation error, despite requiring only $\Omega(\log d)$ samples per task.
Streaming Linear System Identification with Reverse Experience Replay
Thus, we provide the first -- to the best of our knowledge -- optimal SGD-style algorithm for the classical problem of linear system identification with a first order oracle.
Do Input Gradients Highlight Discriminative Features?
We believe that the DiffROAR evaluation framework and BlockMNIST-based datasets can serve as sanity checks to audit instance-specific interpretability methods; code and data available at https://github. com/harshays/inputgradients.
Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization
We study online learning with bandit feedback (i. e. learner has access to only zeroth-order oracle) where cost/reward functions $\f_t$ admit a "pseudo-1d" structure, i. e. $\f_t(\w) = \loss_t(\pred_t(\w))$ where the output of $\pred_t$ is one-dimensional.
MOReL: Model-Based Offline Reinforcement Learning
In this work, we present MOReL, an algorithmic framework for model-based offline RL.
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method
Further, instead of a PO if we only have a linear minimization oracle (LMO, a la Frank-Wolfe) to access the constraint set, an extension of our method, MOLES, finds a feasible $\epsilon$-suboptimal solution using $O(\epsilon^{-2})$ LMO calls and FO calls---both match known lower bounds, resolving a question left open since White (1993).
Learning Minimax Estimators via Online Learning
We view the problem of designing minimax estimators as finding a mixed strategy Nash equilibrium of a zero-sum game.
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
Our improved rate serves as one of the first results where an algorithm outperforms SGD-DD on an interesting Markov chain and also provides one of the first theoretical analyses to support the use of experience replay in practice.
The Pitfalls of Simplicity Bias in Neural Networks
Furthermore, previous settings that use SB to theoretically justify why neural networks generalize well do not simultaneously capture the non-robustness of neural networks---a widely observed phenomenon in practice [Goodfellow et al. 2014, Jo and Bengio 2017].
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games
For Lipschitz and smooth nonconvex-nonconcave games, our algorithm requires access to an optimization oracle which computes the perturbed best response.
P-SIF: Document Embeddings Using Partition Averaging
One of the key reasons is that a longer document is likely to contain words from many different topics; hence, creating a single vector while ignoring all the topical structure is unlikely to yield an effective document representation.
MOReL : Model-Based Offline Reinforcement Learning
In this work, we present MOReL, an algorithmic framework for model-based offline RL.
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
The domain specific components are discarded after training and only the common component is retained.
Ranked #1 on
Domain Generalization
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Non-Gaussianity of Stochastic Gradient Noise
What enables Stochastic Gradient Descent (SGD) to achieve better generalization than Gradient Descent (GD) in Neural Network training?
Efficient Algorithms for Smooth Minimax Optimization
This paper studies first order methods for solving smooth minimax optimization problems $\min_x \max_y g(x, y)$ where $g(\cdot,\cdot)$ is smooth and $g(x,\cdot)$ is concave for each $x$.
Universality Patterns in the Training of Neural Networks
This paper proposes and demonstrates a surprising pattern in the training of neural networks: there is a one to one relation between the values of any pair of losses (such as cross entropy, mean squared error, 0/1 error etc.)
Rethinking learning rate schedules for stochastic optimization
One plausible explanation is that non-convex neural network training procedures are better suited to the use of fundamentally different learning rate schedules, such as the ``cut the learning rate every constant number of epochs'' method (which more closely resembles an exponentially decaying learning rate schedule); note that this widely used schedule is in stark contrast to the polynomial decay schemes prescribed in the stochastic approximation literature, which are indeed shown to be (worst case) optimal for classes of convex optimization problems.
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares
First, this work shows that even if the time horizon T (i. e. the number of iterations SGD is run for) is known in advance, SGD's final iterate behavior with any polynomially decaying learning rate scheme is highly sub-optimal compared to the minimax rate (by a condition number factor in the strongly convex case and a factor of $\sqrt{T}$ in the non-strongly convex case).
Making the Last Iterate of SGD Information Theoretically Optimal
While classical theoretical analysis of SGD for convex problems studies (suffix) \emph{averages} of iterates and obtains information theoretically optimal bounds on suboptimality, the \emph{last point} of SGD is, by far, the most preferred choice in practice.
Online Non-Convex Learning: Following the Perturbed Leader is Optimal
We show that the classical Follow the Perturbed Leader (FTPL) algorithm achieves optimal regret rate of $O(T^{-1/2})$ in this setting.
SGD without Replacement: Sharper Rates for General Smooth Convex Functions
For {\em small} $K$, we show \sgdwor can achieve same convergence rate as \sgd for {\em general smooth strongly-convex} functions.
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
More recent theory has shown that GD and SGD can avoid saddle points, but the dependence on dimension in these analyses is polynomial.
A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm
In this note, we derive concentration inequalities for random vectors with subGaussian norm (a generalization of both subGaussian random vectors and norm bounded random vectors), which are tight up to logarithmic factors.
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
Minimax optimization has found extensive applications in modern machine learning, in settings such as generative adversarial networks (GANs), adversarial training and multi-agent reinforcement learning.
BIG-bench Machine Learning
Multi-agent Reinforcement Learning
+1
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
This paper studies the problem of sparse regression where the goal is to learn a sparse vector that best optimizes a given objective function.
Unsupervised Document Representation using Partition Word-Vectors Averaging
One reason for this degradation is due to the fact that a longer document is likely to contain words from many different themes (or topics), and hence creating a single vector while ignoring all the thematic structure is unlikely to yield an effective representation of the document.
On the insufficiency of existing momentum schemes for Stochastic Optimization
Extensive empirical results in this paper show that ASGD has performance gains over HB, NAG, and SGD.
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form
Semidefinite programs (SDP) are important in learning and combinatorial optimization with numerous applications.
Learnability of Learned Neural Networks
This paper explores the simplicity of learned neural networks under various settings: learned on real vs random data, varying size/architecture and using large minibatch size vs small minibatch size.
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent
Nesterov's accelerated gradient descent (AGD), an instance of the general family of "momentum methods", provably achieves faster convergence rate than gradient descent (GD) in the convex setting.
Leverage Score Sampling for Faster Accelerated Regression and ERM
Given a matrix $\mathbf{A}\in\mathbb{R}^{n\times d}$ and a vector $b \in\mathbb{R}^{d}$, we show how to compute an $\epsilon$-approximate solution to the regression problem $ \min_{x\in\mathbb{R}^{d}}\frac{1}{2} \|\mathbf{A} x - b\|_{2}^{2} $ in time $ \tilde{O} ((n+\sqrt{d\cdot\kappa_{\text{sum}}})\cdot s\cdot\log\epsilon^{-1}) $ where $\kappa_{\text{sum}}=\mathrm{tr}\left(\mathbf{A}^{\top}\mathbf{A}\right)/\lambda_{\min}(\mathbf{A}^{T}\mathbf{A})$ and $s$ is the maximum number of non-zero entries in a row of $\mathbf{A}$.
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)
This work provides a simplified proof of the statistical minimax optimality of (iterate averaged) stochastic gradient descent (SGD), for the special case of least squares.
Accelerating Stochastic Gradient Descent For Least Squares Regression
There is widespread sentiment that it is not possible to effectively utilize fast gradient methods (e. g. Nesterov's acceleration, conjugate gradient, heavy ball) for the purposes of stochastic optimization due to their instability and error accumulation, a notion made precise in d'Aspremont 2008 and Devolder, Glineur, and Nesterov 2014.
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness
We thus effectively compute a histogram of the spectrum, which can stand in for the true singular values in many applications.
How to Escape Saddle Points Efficiently
This paper shows that a perturbed form of gradient descent converges to a second-order stationary point in a number iterations which depends only poly-logarithmically on dimension (i. e., it is almost "dimension-free").
Thresholding based Efficient Outlier Robust PCA
That is, given a data matrix $M^*$, where $(1-\alpha)$ fraction of the points are noisy samples from a low-dimensional subspace while $\alpha$ fraction of the points can be arbitrary outliers, the goal is to recover the subspace accurately.
Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification
In particular, this work provides a sharp analysis of: (1) mini-batching, a method of averaging many samples of a stochastic gradient to both reduce the variance of the stochastic gradient estimate and for parallelizing SGD and (2) tail-averaging, a method involving averaging the final few iterates of SGD to decrease the variance in SGD's final iterate.
Faster Eigenvector Computation via Shift-and-Invert Preconditioning
We give faster algorithms and improved sample complexities for estimating the top eigenvector of a matrix $\Sigma$ -- i. e. computing a unit vector $x$ such that $x^T \Sigma x \ge (1-\epsilon)\lambda_1(\Sigma)$: Offline Eigenvector Estimation: Given an explicit $A \in \mathbb{R}^{n \times d}$ with $\Sigma = A^TA$, we show how to compute an $\epsilon$ approximate top eigenvector in time $\tilde O([nnz(A) + \frac{d*sr(A)}{gap^2} ]* \log 1/\epsilon )$ and $\tilde O([\frac{nnz(A)^{3/4} (d*sr(A))^{1/4}}{\sqrt{gap}} ] * \log 1/\epsilon )$.
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
While existing algorithms are efficient for the offline setting, they could be highly inefficient for the online setting.
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis
Our algorithm is linear in the input size and the number of components $k$ up to a $\log(k)$ factor.
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm
This work provides improved guarantees for streaming principle component analysis (PCA).
Convergence rates of sub-sampled Newton methods
Provided certain conditions hold on the model class, we provide a two-stage active learning algorithm for this problem.
Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation
Combining our algorithm with previous work to initialize $x_0$, we obtain a number of improved sample complexity and runtime results.
Convergence Rates of Active Learning for Maximum Likelihood Estimation
Provided certain conditions hold on the model class, we provide a two-stage active learning algorithm for this problem.
Learning Planar Ising Models
Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering.
Fast Exact Matrix Completion with Finite Samples
In this paper, we present a fast iterative algorithm that solves the matrix completion problem by observing $O(nr^5 \log^3 n)$ entries, which is independent of the condition number and the desired accuracy.
Non-convex Robust PCA
In contrast, existing methods for robust PCA, which are based on convex optimization, have $O(m^2n)$ complexity per iteration, and take $O(1/\epsilon)$ iterations, i. e., exponentially more iterations for the same accuracy.
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization
Alternating minimization is a popular heuristic for sparse coding, where the dictionary and the coefficients are estimated in alternate steps, keeping the other fixed.
A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries
We consider the problem of learning overcomplete dictionaries in the context of sparse coding, where each sample selects a sparse subset of dictionary elements.
Phase Retrieval using Alternating Minimization
Empirically, we demonstrate that alternating minimization performs similar to recently proposed convex techniques for this problem (which are based on "lifting" to a convex matrix problem) in sample complexity and robustness to noise.
